I’ve had a chance to use fable 5.2. Some observations:
- Anthropic is finally well balanced across models. Meaning: Opus 5.5 is so good that I do not feel Fable FOMO as I did when it was Fable 5/5.1 vs Opus 4.8/5. And Sonnet 5.5 sometimes makes me forget about Opus.
- Fable is good, but you really have to push the ambition of your prompt in order to see proper separation from Opus 5.5. I personally have a hard time telling the difference between Fable 5.2 and Opus 5.5 for ordinary coding tasks.
- It’s way faster and more reliable: one of the worst things about fable 5.1 was how slow it was an, on top of that, how frequently it would suffer from max output token overflows. I got none of these issues with 5.2.
- Fable is exceptional at drawing. It’s ability to render an image with JavaScript actually has me questioning the value of dedicated image models. I prefer the images I’ve gotten fable/opus to make for me over those from gpt-image, it’s wild to think we’re at this point.
> Fable is good, but you really have to push the ambition of your prompt in order to see proper separation from Opus 5.5.
I love seeing this verbalized. We are pretty much at the point that increased model capability will only have utility to folks that are willing to let go of even more understanding of whatever the model is producing.
The best models can/will produce a year's worth of output that would take several days to weeks to properly understand in the span of an afternoon.
For everyone that still wants to feel like they own the end result, just use the lesser models with less ambitious tasks.
Caveat: I think this mainly applies to coding. I would still like the best models possible to be working on e.g. new drug research to cure our various ailments.
> This is not a claim about the future. Perhaps almost all or perhaps even all of the software skills I have will become irrelevant in the near future. But we're not there yet. At the moment, programming skills seem higher leverage than ever before.
> There's something a bit funny about the current moment, where it seems like having the skills that models are replacing makes one much more effective than people who are trying to use models without having those skills. I like Max Bittker's framing that models are very good at counterfeiting and the level of expertise it takes to distinguish a counterfeit from the real thing has been increasing as models improve.
> We are pretty much at the point that increased model capability will only have utility to folks that are willing to let go of even more understanding of whatever the model is producing.
Given that reading someone else's code is slower than understanding your own code, doesn't that basically cover everyone that adopted coding agents?
> Fable is exceptional at drawing. It’s ability to render an image with JavaScript actually has me questioning the value of dedicated image models
I’ve been shifting away from using more traditional GenAI models in favor of just giving LLMs access to a custom drawing language (LOGO-like) to generate imagery and its been going shockingly well - Opus 5.5 in particular.
I put together a simple LLM driven skill that creates old adventure game (EGA) environments from simple pencil sketches and it works really well. And being procedural, they're much easier to make surgical changes to without degradation.
Just to clarify -- the images on that site are all generated from LLM-written code? It's very impressive if so. I would never have guessed these weren't human-made from the visuals alone.
Yeah it's entirely LLM driven! If you look at the repo, I explain more about how to use it. Basically, you give an agentic coding harness capable of tool usage (Opus 5.5, Qwen 3.8-27b, etc) the skill which explains the drawing system: simple things like how to set the colors, position rectangles, draw polygons, etc. and it uses p5.js to render them.
It has three different modes:
• You can simply describe a picture, and it'll automatically generate an image using procedural code.
• You can ask it to paint over an existing image. That's what those examples show, like me creating EGA-style equivalents of various scenes from the original Myst.
• The last way is to give it a rough pencil sketch. It's the mode I use the most since it's very valuable when building an adventure game to position objects in a room.
So basically I gave an LLM access to the user manual for the original LOGO drawing language, along with several screenshots of various scenes from the original King's Quest I.
Then I just guided it to produce a procedural variant specialized in creating new EGA-style scenes for use in creating old-school point-and-click adventure game.
Does Fable 5.2 outperform Opus for data science work?
Not just "iterate on this XGBoost model" but actually looking at a problem and designing and carrying out a short-term R&D program including probability modeling, model fitting diagnostics, how to respond to various kinds of results, how to interpret/reason about what it finds, etc. That's really where I saw all the models perform poorly, and where I saw the first big step increase from Opus 5 (pre-5.5) to Fable. But then Opus 5.5 came out and I haven't needed Fable since.
Mind you, I'm doing slightly different work (and I'm very much "in the loop" still) than when I performed that side-by-side test. But it's really amazing what Opus 5.5 can do. I now have a powerful data science AI assistant that not only can write any script I need it to (including some scripts I didn't even realize I needed), and it has an encyclopedic knowledge of everything ever, and it constantly checks me on oversights, it handily derives expressions and clever model reparameterizations that aid, it has great insight into model behavior, and it has an amazing ability come up with ad-hoc experiments and tests that would have taken me 2x as long to even think of (let alone implement by hand).
It's not at the scared-to-lose-my-job phase yet, but it's become a tremendous force multiplier for a smaller data team.
I'm teaching "Data Science" to computer science majors next spring, and I'm overwhelmed by the prospect of what to teach them in the age of AI. It seems like Fable already way outperforms anything I could historically expect my students to do at the end of the semester, and perhaps even outperforms me. I can describe a vague analysis on the data, and it will handle every step: transformation, preliminary analysis, formulating more precise questions, creating visualizations, running statistical tests, interpreting results, thinking about stakeholders... And in some of those steps, it finds things that I might not have considered.
I wish I had a bigger N so I could rely on that experience a bit more, but it sure does feel like I'm not really contributing anything in terms of "Data Science expertise" in my own research right now.
Plus it worries me that people will no longer know how to do anything basic and we will at some point in the future be slaves to AI if we aren't careful.
If it wasn't for grinding all of the hard problems and hands-on projects and various programming tasks I had to do by hand over the years, I wouldn't be able to keep up with the agent, and wouldn't be able to get good results out of it, because I wouldn't know what it was doing and wouldn't be able to steer it appropriately. My schoolwork 15 years ago was more than formative, it remains critically foundational for my job work today.
Based on the other comment it sounds like maybe I need to give Fable another chance, and then I suppose I won't have to steer anything at all. But at the end of the day I am not paid to be a meat proxy, I am paid to deliver things for the business that employs me, and the things I deliver are my responsibility, not Anthropic's.
So I say, embrace AI as a learning tool, let students use it to bypass programming on assignments where the programming is of secondary importance, require oral defense/presentation for projects, make homework worth a much smaller fraction of final grade, and make all of your tests in-class and hand-written.
The reality is that the entire nature of education has to change to account for cheating becoming essentially free and virtually impossible to detect. Grinding through a problem set in the library is still tremendously important, but it's going to be harder to motivate students to do it without just cheating. Some students are conscientious and will do it anyway. Some won't. The hard part is going to be crafting curricula and exams that separate the two kinds of students. Fortunately you share that problem with just about every other professor in the world right now, so at least you shouldn't feel responsible for solving it all yourself.
Relevant anecdote: I used Wolfram Alpha to cheat on my calculus homework in freshman year, and then I got my butt kicked in the exam and a C final grade that never quite averaged its way out of my GPA. That felt horrible and it was a strong motivating force to get my shit together, but that's only because I'm a fucking nerd and I really cared about learning in school and making a career out of what I learned. On the other hand, I really was struggling with the material, which is why I tried cheating in the first place. Maybe if I had today's AI I could just ask it to help me learn better.
From my experience working with data scientists, it outperforms humans on an extreme level. I’ve seen so much bad data science, and here I have a model that can do all of that from one prompt. It is getting really good on finding caveats in data too.
I mean I found Fable 5.1 outperformed Opus 5.5 for data science, so I expect 5.2 to.
But then I'm someone who hasn't found Opus 5.5 good for much beyond web UIs and related frontend development. I find it more forgetful than GPT-5.6 or 6.1 or Fable, so despite having a 1M context window it gets lost in a big codebase or goes of target. Opus 5.5 doesn't manage agents particularly well IMO.
I'm surrounded by people raving about Opus 5.5 and God only knows why I'm not getting their results.
Your comment matches what I measured on 30 regular coding tasks: Fable 5.2 and Opus 5.5 both solved 26 (Fable 5.1 was at 25). Fable 5.2 finished in 0.62x the time of Fable 5.1 on the same tasks, with no runs hitting a safeguards stop (Fable 5.1 hit a lot of safeguard stops).
And none of it matters ... because just as soon as you are happy with things "balanced across models" Anthropic will nerf the models you were happy with, and force you to use new and more expensive ones.
Tell it to draw a picture of something with JavaScript in the style you desire. No harness magic necessary. It helps to provide it with reference images.
> - Why are they stacking Fable xHigh against Carbon Med?
Because on that specific benchmark (FrontierCode 2.0), Medium is Carbon's best effort level and xhigh is Fable 5.2's best effort level. You can see that on the graph your next point is about.
And it's just that one benchmark that's at medium. "All scores show each model’s highest-available effort unless otherwise noted".
> - How did they produce such a bizarre effort-intel curve for Carbon that goes against every available benchmark?
The graph is labeled as being about FrontierCode 2.0. Those appear to be Carbon's scores on the official FrontierCode 2.0 leaderboard: https://cognition.com/blog/frontier-code-2.0
> - Why did they leave Carbon-- positioned primarily as a cyber model-- off of the single cyber benchmark comparison?
> - Why do they switch to comparing against Argon (rather than carbon) for robodojo?
Because no numbers are available for Carbon for those benchmarks, and the model isn't publicly available so they couldn't run the benchmarks themselves?
And from your followup:
> where's Astra on that CVE chart
To run Astra on a vulnerability finding eval, you'd need to be part of OpenAI's verified cybersecurity program. Otherwise the results would be tainted by refusals. Do you expect that OpenAI would grant such access in this case?
> Because on that specific benchmark (FrontierCode 2.0), Medium is Carbon's best effort level
I am referring to the chart that cross-compares multiple benchmarks, which Artificial Analysis clearly shows Carbon-high as superior on. Further, AA's FrontierCode chart places Carbon-high as having a higher score on Frontier 2.0 than Carbon-med (https://artificialanalysis.ai/evaluations/frontiercode-2-0?m...)
Where it gets very interesting is when you look at the Cognition "FrontierCode" publish date (2026.10.27), and the revision as of 10.28 in web archive (https://web.archive.org/web/20261028175237/https://cognition...). This revision has a VERY different Carbon effort / intelligence curve where high beats medium.
It is only with the latest revision, as of today, that all of a sudden Carbon's benchmark curve goes cone-shaped. Curious, indeed....
Arena.ai released carbon tests 12 days ago - the only service that I have seen on yt that gained access. And you can compare to other SOTA models in their video.
Just to follow-up-- artificial Analysis shows no justification for that Carbon effort curve, but it does show Carbon on medium smashing Fable on things like Terminal-Bench and AA-Omniscience v1.1 Non-Hallucination Rate.
But I suppose the reality is rather obvious when you note the funny games they're playing with the graphs, and the things they won't talk about (where's Astra on that CVE chart?)
> Our first formalization of Fermat’s Last Theorem, performed by an unreleased research version of Claude, took 11 days. It relied on a purpose-built harness along with guidance from a human researcher. By contrast, a team of 1,000 Fable 5.2 agents fully formalized the theorem in just 30 hours, with minimal human intervention. Fable 5.2 started from minimal prompts, built its own ways to coordinate, and used no mathematical references, only a list of intermediate theorem statements to score progress. It needed one-third of the action tokens as our first formalization and produced a proof seven times shorter. As can be seen in the figure below, Opus 5.5 (with the same 1,000-agent setup) completed only 26% of the formalization in 18 hours, and barely made any more progress after 30.
Most people don't really have intuitions on what it means to push the frontier now (because AI has surpassed everything they are good at, for example, coding). We are truly inside the singularity (using Kurzweil's definition, slightly paraphrased - the time when technology progresses so fast no one can keep track with it).
Kurzweil's definition contains exponential growth across technology, something that we are not seeing.
What we're seeing instead is maybe akin to the green revolution. Before nitrogen fixation nitrogen was a resource constraining us humans. Lack of grit is what kept us from solving mathematical problems. However this lets us run into other resource constraints. A lot of technological progress does not solely hinge on thinking/processing. The speedup of creating new medicines with LLMs will probably be very small, as you still need to do all the testing. In software development you still need to first find out what to build in the first place and even mathematics where LLMs seem to shin brightest, it's becoming clearer that mathematics is not just solving mathematical problems.
Apart from that, a good coder is better than LLMs, shown by the amount of hand-holding needed. The only issue is that the amount of research an LLM can do and knowledge it is trained with far surpasses the amount of work humans would need to spend by hand, but we can also us LLMs for that.
I would imagine Anthropic is hitting a wall on the number of simultaneous agents that can be useful on a task and are working on increasing that maximum. I would assume if they could get twice as much progress by using 10000 simultaneous running fable instances instead their brief would state that.
IMO, it is very interesting and also very alarming. At some point the barrier to the exponential progress is the amount of computation available. I would consider hitting this limit and then likely using the same said swarm to help resolve it via various means as very much fitting under the definition of Ray Kurzweil's singularity.
Also, I would disagree, Fable is a sufficient coder. The average human being better, even if that is true now (and personally I wouldn't agree), the average human is limited in the number of 'human instances' he or she can interface with at one time.
if 1000 fable instances is better than any human at a task, how about 1 million instances running? Much easier than finding and hiring and coordinating 1000 extremely productive and intelligent human beings. Much cheaper too, it appears.
Could someone explain how increasing number of agents help? Is each agent researching a potential solution space where there's many potential solution spaces? I'm struggling to conceptualize how a large agent swarm can be used to solve a research problem.
I don't think it's nearly as relevant as the total amount of compute being used. I just think they like talking about swarms because it sort of anthropomorphizes them.
Feel like this might still apply to multi-agent programming:
I have earlier argued that the sheer number of minds to be coordinated affects the cost of the effort, for a major part of the cost is communication and correcting the ill effects of miscommunication (system debugging).
The Mythical Man-Month
Frederick P. Brooks Jr.
Math is for sure about solving mathematical problems, mathematicians complaing about humans capability of understanding it. If you need math an LLM understands, it might not be relevant if you understand it if an LLM is getting a lot better in adjusting itself if it goes the wrong way.
And this situation is only a few weeks old. We don't know how this field continues to progress. Books like the culture series and other scifi do describe a future were humans spend their time only to understand what the machines are doing or did.
The speedup of medicines is for sure faster with. There are still old labs doing everything by hand and the neolabs doing everything a lot more automated. An AI can do experiments 24/7 and can scale with the hardwarelabs, but it also can hold the whole research field in context and it might be a lot faster of choosing the better experiments or the right ones to begin with.
In software developmet (my field) i can do today alone what you needed a Team for. Yeah it doesn't help the field of software engineering if you need 5 times less people and you don't have any motivation AT ALL to teach someone avg. We are already planning to remove one member of our team due to AI. And we will not extend the contract of 2 consultants.
It just doesn't make sense anymore. If i explain in a ticket a task well enough for someone who has not been in our team for years, the AI is already better in it. We don't struggle with AI output, we struggle more with not having a Monorepo.
Give an LLM access to a GPU system or a GPU cluster. It immediadly feels complelty different then running it on some laptop. It can now do simulations, a lot more and faster experiments etc. I had this epiphany just a few month ago. I always used my laptop for projects (it needs very little ram, i can quickly check what it is doing and i actually do use it primarily when im on holidays) until I thought "let me use claude to do some gaussian splatting experiment its always so cumbersome to use these tools" and it changed my perception immediadly.
I don't think that case can be made. I'm a "good coder" and the best LLMs are now better at it than I am, in essentially every respect that matters. I can't think of a test that I could reliably pass that Fable or Astra would reliably fail.
How do you define "good coder"? I've yet to see LLMs that write better than what I consider the best human code. I think Michael Hashimoto showed that not too far back where getting LLMs to optimize for a problem resulted in code better than naive but much worse than expert and that's before even talking about how that code looks/reads.
I'll admit I'm not on the very cutting edge models but LLMs make weird tradeoffs and lack creativity. They will sometimes write marginally better but way over-complicated code for no good reason. That can have performance implications and they're generally not "aware". They can gain awareness via profiling but again run into limited creativity or big picture understanding. They are good with cliche but not as good with novel situations. With human guidance this is still productive/useful but as the systems grow more complex they are less able to reason correctly about them.
I would say that in certain situations they outperform the average software developer. They definitely outperform in speed and output. So they're like a maybe 1600 ELO chess player for software. I.e. they play chess better than all non-chess players. They play chess better than many chess players. But they're not "master" or "gm" level across the entire discipline. But they can play 100k chess games in the time it takes me to figure out my move...
Right now, I'd probably pick the task of writing a graphical interface, say a webapp, with a bunch of different gestures and having it "feel good" to users. That's a difficult evaluation to automate, at least for now, which is probably why they're not that good at it yet.
They'd prototype and adjust a lot faster than a human coder, though.
I think there’s still value in stuff that people can see, like frontend design. Because even if LLMs are technically better, every site is going to look the same. What’s good very quickly becomes cliché when everyone does it.
Is this just because we're exposed to more of these personal project LLM sites? Because I remember people complaining about all sites looking the same all the time ever since bootstrap came out.
I can mediate with project manager about some requirements which should not be implemented. I can make decisions to simplify the system without bothering my manager with endless details. I can create short and concise report.
I can say "no".
I can store passwords in the database unencrypted and unhashed.
So far AI that I've used failed spectacularly on these prospects.
They're like "yes-man". They'll try to do everything you ask of them. Even if you ask nonsense, they'll not push back.
They'll go crazy about security stuff, even not relevant to the current situation.
They'll complicate the code endlessly for situations which will not arise, making the code an incomprehensible mess.
They'll write so many useless words that any reader will forget what this report is about before then reach the middle of it.
They'll never iterate on code until it looks good. They'll stop when test pass. They don't have aesthetical sense of beauty.
They love to cheat too often. And you can't do nothing about that. You can't fine them or fire them, and they know about it.
They're smart but I'm not convinced they're ready to replace human in mundane engineering jobs.
AI is actually still really bad at architecture. It produces very good localized code, like an auto complete. It produces so much of it, that it can be overwhelming, but it's not remotely better than the equivalent aka an infinite number of talented humans doing the work.
The scale of it (aka 'make me a video game') is daunting and I think misinforming our intuition as to what is going on.
LLMs are brute forcing outputs and attacking math problems is not a singularity, it's just more compute.
What we are witnessing is the ability for machines to use fuzzy concepts, instead of very strict code, which allows it to map into a much greater variety of things, aka code, pdfs, web - and crunch/compute the content.
I feel like I'm increasingly reading AI current events is more like learning about historical events. By the time information has entered my head, there is even newer information out there that already makes what my brain is only currently actively processing, obsolete.
In H1 2026, cost of revenue is $2.3B. That is primarily inference compute, but also includes cost of training models (but not research).
In the same period, R&D was $9.4B. That is primarily spent on compute. That is what they are "holding back" already, in full public view.
This is not a company that is optimizing for OpEx or end user revenue. The only thing relevant to their plans is achieving AGI/ASI first, and controlling enough compute to leverage it to earn the majority of the current economy.
"Achieving AGI" strikes me as a mostly meaningless phrase. I'm skeptical that it is a boolean event. They're already smarter in some ways, their impact is large and gradually rising, the set of specific tasks that they aren't capable of is gradually shrinking. If AGI means anything useful, it seems like we'll only see it in hindsight and have to argue over when "it" happened.
Likewise the singularity. While recursive self-improvement is theoretically trivial (just tweak a random subset of weights like a monkey with a typewriter, eventually you'll get something smarter), the resulting exponential improvement is still very hypothetical and feels unlikely. Unless you're ok with an exponent of 1.000000000000000001.
I agree we will only see it in hindsight, but I think that's always the case with any technology. It seems worse until it's suddenly better. I don't think it matters to argue the exact moment though. For example when did cars become clearly better than horses? No one cares about the exact date.
These are not my stated ambitions, they are the ambitions that Anthropic and Dario have said they are pursuing. Whether or not they are realistic is not relevant to this thread. The credibility of the plans are relevant to the S-1 and anyone who purchases stock from them, but for the purposes of this thread, it is only important that Anthropic believes it has credible chances of achieving this goal, and in particular that it has a competitive advantage, could do so first, and that being first could have a very large upside.
Because they do months of testing. Also, if they have models that are really good at AI research, I’d not expect full versions to ever be released, precisely because of their love of money.
My assumption is that they're overly expensive to run at scale and/or don't have the same safeguards that public models have and possibly get better results because of that. If a model is 100x more expensive to run but 4x better results, of course you'd use that internally to make your product even better - it's compounding returns. But people aren't going to pay 100x their current bill for 4x results.
I have to imagine if I worked for one of these companies on the frontier models it would be very difficult to leave for that reason. Thee pay is also pretty good.
Tracking meta capex, parameters and AA intelligence there are diminishing marginal returns.
Diminishing, as in progress gets harder and eventually stops.
Assume that the Claude unreleased takes 11 days with one agent. The Claude 5.2 takes 30 hours with 1,000 agents. The Claude unreleased used 264 units of compute, but the Claude 5.2 used 30,000.
So, it did accomplish the task faster with less intervention, but the ratio of cost to improvement is 13:1.
If we can't produce a method to break out of a linear improvement in LLM technology (ie RSI), the LLM is doomed.
I am yet to see someone explain what the RSI will be or why it will happen.
Yes, and not very helpful without knowing how many agents were used by the custom-built harness. The 30 hrs vs. 11 days means nothing without at least some token-to-token comparison. Without that, they are pumping up their new model with half data - better to just say "we solved it in 30 hours" and who cares about your internal, custom model.
We are not in the singularity. We currently can only produce reliable code / formalizations that have reliable checkers. If there wasn't an oracle that could tell Claude "yes" or "no," it would not be able to prove anything like this.
All the human engineers will be in harnesses in the future.
Within three years most software developer jobs will consist of taking instruction from the AI systems. The humans won't see it that way, but that's what it will actually be. Realistically the thing that will happen is the humans will be put into harnesses (cubicles) and prompted by the AI (I need you to plug that server in; I need you to give me access to such and such; now do this, then do that). Most tech jobs will consist of AI telling the human what the AI needs the human to do next, de facto the human will be in a harness and will be getting told what to do.
Not only will the humans-as-meat-puppets model be used for software engineers, but it is likely the way that agentic workflows will expand beyond software development, data science, and mathematics.
I first heard about this in Marshall Brain's short story _Manna_ [1]. In the story, the robot takeover started with AI management software running fast food restaurants, where workers would execute tasks the AI would narrate to them over headsets.
I bet we're pretty close to being able to implement this.
> Why would there be a human in this imagined system at all?
in the imagined system above it requires physical acts that an llm can not do, if we go further than the above imagined system and make robots to perform these task for the llm a human still is needed for maintenance for these robots and their assembly, once there are robots for maintaining the llm assistant robots, humans are still needed to maintain the maintenance robots and so on and so forth. I dont see an end point where a robot machine system is fully self healing without atleast a few humans in the mix to correct when there is a breakage in the chain
The ownership will want humans to punish, humans to trust, humans to manipulate, and humans that can be fired. They'll want a human physically present, representing them as oversight over the AI (a job the capital doesn't want to be doing 24/7). There are serious legal matters involved in high functioning AI systems (and that will dramatically increase over the coming decade), the capital will be extremely worried about liability issues. Human scapegoats are ideal. For now capital will want human style consequence-feedback systems for a tier of human oversight over the AI. It'll probably take a full generation before the capital controlling humans forget / give up on the old systems fully.
It's also worth noting that it's far easier at present to press a human into doing something highly illegal than it is to get Claude or GPT to do so.
If you take Daniel Schmachtenberger's thesis, these models are the latest extension of an ongoing misaligned moloch-like entity, using our institutions/economy/corporations, etc. as its organelles. All powered, most recently, by fossil fuel.
The ownership class, the capital class - whatever people prefer to call it, will still call all the shots as they always have, and always will.
So whether that's a CEO at the top instructing a human manager that then instructs AI systems, which then put into motion plans that include the harnessed humans getting prompted, or however it's decided to best configure the top operational tiers - that's all fairly irrelevant ultimately. All human labor, and all AI labor, will be in harnesses. Realistically all human labor has always been in a harness.
The people in charge will be the people that control the capital. They will instruct the AI to their bidding, they will control the data centers the AI lives in, they will own the robots doing the labor in factories, they will own the factories, they will control the grid, they will control the electricity generation, et al.
It's true now, it'll be true tomorrow. Nothing fundamental will shift at that tier: don't be labor, own.
If you're a software developer, you're increasingly depreciating labor. Every unit of improvement the AI systems see, the average human software developer's labor value diminishes. It's diminishing extraordinarily fast, kind of like a confrontation between increasingly productive machines and physical human labor during the industrial revolution. You might as well picture a human with a hand plow taking on a modern industrial tractor.
> We're all going to be harness engineers in the future
No, that will be the model's job. We will largely be unemployed. Of course, we can surely depend on the largesse of Altman and Amodei to keep us fed and sheltered.
> We're all going to be harness engineers in the future.
Lolwut. If this shit works the way it's boosters claim, humans will have absolutely nothing to contribute.
Sit down, shut up, and get out of the way, because your involvement is slowing shit down.
Robotics are lagging, so there's probably a few years where we're going to need humans to crawl into that awkward space under the counter and twist some knobs, or to clean toilets.
I would imagine Anthropic is hitting a wall on the number of simultaneous agents that can be useful on a task and are working on increasing that maximum.
I would assume if they could get twice as much progress by using 10000 simultaneous running fable instances instead, their brief would state that.
IMO, it is very interesting and also very alarming. At some point the barrier to the exponential progress is the amount of computation available. The average human is limited in the number of 'human instances' he or she can interface with at one time.
if 1000 fable instances is better than any human at a task, how about 1 million instances running? Much easier than finding and hiring and coordinating 1000 extremely productive and intelligent human beings. Much cheaper too, it appears.
We are very close to some very scary things happening. I just recently came to terms with the fact that an AI could code and manage coders faster and better than me, I don't think anyones is ready for what is about to happen.
It depends on the type of task. There was an interview on Dwarkesh with an OpenAI researcher and he said that massive multi-agent swarms can increase capability on some problems that are very parallelizable, but there are diminishing returns. But not all tasks can be parallelized like that.
Where it gets scary is the fact that efficiency is going up 10x per year, so the cost of running 1000 agents will cost the same as running 1 agent in 3 years if the trend holds.
Maybe that's true, and maybe you're an outlier. But from what we've been seeing, the frontier LLMs have torn apart many codebases, finding bugs littered everywhere, some over a decade old.
At this point it seems impossible for human to write better/more efficient code than a swarm of agents -- to be clear, multiple coordinated LLMs, not one-shot builds with a single agent.
Ten median US software developers cost about $1.5 - $1.7 million per year - including all benefits - as of a couple of years ago.
A frontier AI is at least as capable as ten median human software developers. It's likely well beyond that now. Over the course of a year, would you have to spend $1.5 million to get the output of those ten median humans? No chance in hell. You'd have to spend less than 10% of that.
Then on top of that: the AI is improving rapidly; the humans go on vacation, the humans decline, the humans get old, the humans get sick, the humans take parental leave, the human struggle to adapt, the humans are resistant to change, the humans riot, the humans protest, the humans need healthcare, the humans want paid more, the humans can only work for 4-6 hours at peak while writing software (slowly).
Do you know any humans that are experts at a dozen programming languages? That human does not exist. The frontier AI are omnimaths.
What's my cost as a business to utilize the latest cutting edge Chinese model, renting its usage from the cloud? Whatever the Chinese cost (on their side), I'm never going to suffer even a tiny fraction of that pass-along cost no matter what. We can all appreciate the subsidization going on.
Or to grab a bunch of $200 per month accounts from OpenAI or Anthropic for a team to use?
The calculations is: I'm in the US, if I have ten median software developers, what's my cost? Then versus using AI instead.
The human labor can't keep up. Even if we freeze things right here, and then hyper optimize Fable 5.2 by etching it straight to silicon, the human developers are obsolete. And the future isn't going to stop coming.
I like their angle though - from a societal perspective of allocated resources, how long until we've paid off the cost of building these LLMs? Imagine instead the entire supply chain was Stalinistically tuned towards finding bugs in Linux. Training rare earth mineral miners how to code level or whatever. I think as a thought experiment it's interesting, the answers could translate to an organizational level about comparing labor cost against token cost. What's a good breakdown of token spend vs hiring some new juniors to train up?
>Gotta be honest, LLMs are still subpar compared to what I do.
Im sorry... what?
You are replying to this:
> Our first formalization of Fermat’s Last Theorem, performed by an unreleased research version of Claude, took 11 days. It relied on a purpose-built harness along with guidance from a human researcher. By contrast, a team of 1,000 Fable 5.2 agents fully formalized the theorem in just 30 hours, with minimal human intervention. Fable 5.2 started from minimal prompts, built its own ways to coordinate, and used no mathematical references, only a list of intermediate theorem statements to score progress. It needed one-third of the action tokens as our first formalization and produced a proof seven times shorter. As can be seen in the figure below, Opus 5.5 (with the same 1,000-agent setup) completed only 26% of the formalization in 18 hours, and barely made any more progress after 30.
I imagine the author was comparing it being subpar at what _they_ do as a contrast to it being excellent at formalizing mathematical theorems. Thus suggesting that there is a significant "spikiness" to AI.
HN has become so miserable over the past ~12-24 months because it's a site filled full of highly paid techies living in the deepest pit of denial that has ever existed.
It's all going away. Here's a prediction: HN will effectively die with the software developer.
Is it that dependent on the software developer segment? Why do you expect it to die rather than just shift more towards... I don't know, startup business droids?
Devote precious time (in the face of tons of discoveries) to validate the most compelling ones.
If agents actually can continue to produce tons of results with real world implications, and they arent generally found to be shit, I could see that becoming the job of experts. AI discovery validators.
I gotta admit, I am jelly as fuck I don't have access to a billion dollar datacenter. Would LOVE to put 1000 fable 5.2 agents to self-coordinate and get them to do something for me. Fuck life is unfair.
I'm using Opus 5.5 and I cant help but think that I might very well be one of the few people who do so in my local area ( developing country, one of the poorest province ).
Had Opus make software for some local business and seeing it blew people's mind.
20$ aint cheap for us but I'm actually considering upgrading to the 100$ tier.
Am just jelly people are using 100-200$ subscription ^_^
I’d be happy even without having access to a billion dollar datacenter, if I could use local models properly, but even that requires some serious hardware that is out of my reach. We live in weird times: I have lots of hope for the future, but meanwhile AI access is pretty iniquitous indeed.
Sub 1% of the planet has access to cutting edge frontier AI.
Sub 1% of the planet has any practical use for the top tiers of AI systems. That will likely always be true.
The gap between the haves and have nots will drastically widen further, and very quickly. The bottom ~95-97% will be left behind in a way that will be impossible to overcome. The age of AI + robotics will just make it that much harder to climb for most people, these are systems that are in aggressive, direct competition with human labor. And in short order the AI + robots will be vastly superior to human labor (it doesn't really matter if we're speaking in terms of 10 years or 30 years).
AI will consolidate the winnings. The gigantic gains by the elite rich related to AI over the past few years, is merely a hint of what's to come. Capital will win as it always does.
There will be some gains for most everybody else, those gains will be comparatively trivial though. A house bot that saves people an hour of labor here and there, AI that automates some trivial life tasks.
AI has no money, it has no wealth, it owns no capital. For now. If you want to avoid the brutal human vs AI/robot competition arena: own capital, own the AI, own the robots.
Is this substantiated right now in any real way? Is there any existing gap between the barista who doesn't care about AI and the person working at anthropic who has access to internal models? Or the person with 3 $200 subscriptions?
Maybe money? They can make websites for people? What gap really exists between these people besides earning potential, which already existed before AI.
The existing gap gets made worse by AI concentrating wealth even further to the owners of the assets that are producing via AI. AI will generate intense wealth from productivity gains, and those new gains will be more concentrated than any other economic round we've seen previously. It's a human labor eliminator the likes of which we have never run into before.
Fewer humans will be needed per dollar of output. Those gains will not go to labor, they will go to capital, the capital that owns the data centers, the factories, the stores, et al.
In the physical space (AI + robotics) it'll be a chaotic, slow'ish moving change, slower than the hypsters are predicting now, but it will happen regardless.
That barista job largely won't exist in the future. China will replace them with $3,000 bots with high-functioning AI software, and they definitely won't be powered by expensive Nvidia GPUs.
Slow changing service & manufacturing spaces will see a high wipe out ratio in terms of human labor being eliminated.
Walmart has 2.1 million employees, most are doing repetitive tasks that require modest levels of intelligence. If they could eliminate one million of those with AI + robotics, for a cost of less than ~$15,000 per year, they'd do it immediately. It's hard to imagine a future where that isn't going to happen. That human labor pool is costing Walmart probably $80-$100 billion per year. Amazon is even more forthright in their desperation to remove the humans from retail.
> Fable 5.2 started from minimal prompts, built its own ways to coordinate, and used no mathematical references, only a list of intermediate theorem statements to score progress. It needed one-third of the action tokens as our first formalization and produced a proof seven times shorter.
Is this really so astonishing, assuming that the 5.2 training data certainly had the 'traces' from the Fable original run in it's training data.
Isn't this part of the lore behind the Dune universe?
Like, we're dropped into a post post post singularity world where AI already did all of that and is banned? as kind of a footnote that's never further explored in movie renditions that have been released
Dune books by Frank Herbert never actually went into what "Butlerian Jihad" was.
The semi-canonical Dune Encyclopedia (disregarded by Frank's son) differs vastly from prequels and generally points to it being a self-inflicted wound on humanity that opened the doors to widespread return of feudalism and essentially stopped humanity for a 10,000 years
> We are truly inside the singularity (using Kurzweil's definition, slightly paraphrased - the time when technology progresses so fast no one can keep track with it).
> The older, original definitions of "singularity" were less about "herp derp superintelligent AI blah blah".
No they were exactly about superintelligent AI!
Vinge's 1993 article literally opens with this!
> What is the Singularity? The acceleration of technological progress has been the central feature of this century. We are on the edge of change comparable to the rise of human life on Earth. The precise cause of this change is the imminent creation by technology of entities with greater-than-human intelligence.
It's one of the options, but not "oh superintelligent AI comes in and we mostly know what that means so we want to speedrun it " - which I'll admit I was not very clear on.
In fact, one of the options Vinge considered was a "group intellect" formed from how humans communicate more thanks to internet
What Vinge is talking about is way closer to the weirdness of AI than the mundaneness of e.g. a societal grouping of ordinary humans.
Even for that option, Vinge's comments in the conclusion of the article make it clear he's thinking primarily of very weird superintelligence and the group intellect is some sort of physical mind meld/hivemind that essentially just is a biological substrate for AI.
> The post-Singularity world will involve extremely high-bandwidth networking. A central feature of strongly superhuman entities will likely be their ability to communicate at variable bandwidths, including ones far higher than speech or written messages. What happens when pieces of ego can be copied and merged, when self-awareness can grow or shrink to fit the nature of the problems under consideration? These are essential features of strong superhumanity and the Singularity. Thinking about them, one begins to feel how essentially strange and different the Posthuman era will be -- no matter how cleverly and benignly it is brought to be.
From the very beginning with Vinge, the Singularity has been primarily about the craziness of super intelligence rather than a more mundane technological explosion.
Thanks for your link, this (a few paragraphs from the top) seems to settle it:
Stan Ulam paraphrased John von Neumann as saying:
One conversation centered on the ever-accelerating progress of technology and changes in the mode of human life, which gives the appearance of approaching some essential singularity in the history of the race beyond which human affairs, as we know them, could not continue.
Von Neumann even uses the term singularity, though it appears he is thinking of normal progress, not the creation of superhuman intellect.
So the concept of technological singularly precedes and is independent of that of intelligence explosion- although one could argue that the latter is the most logical way for the singularity to come about.
Why re-formalise Fermat's Last Theorem instead of doing something new? It's obviously an improvement, but it's an odd thing to boast about as opposed to a novel formalisation or proof.
It's how RLVR works, when you have a task with a verified answer, you rerun the model on it and promote the reasoning chains that reach the answer. That way you can hill climb model capabilities on any problem you find, which generalises to new problems to solve, which you can then hill climb again and so on.
So since they are doing that task anyway (re-solving the problem increasingly more efficiently), this is a easy check point to publish to show how far they've gotten in that process.
Yeah, but obviously doing a formalisation that has been done before is different than doing a new one since you have knowledge about the previous attempt. It also just seems less impressive than doing something new and the reason they are reporting this is marketing.
It's incredible. Automating the scientific process and knowledge. It's crazy to think what the world might look like. Both in terms of putting the knowledge into practice and how we think of scientific discovery.
I mean we don't even think about it but currently we are sort of discovery starved. We are fairly able to keep up with the theory. If a new technology is discovered we can implement it before something better comes out. What happens when our N discoveries per days becomes 1000N discoveries per day?
It flips. We now have more knowledge than we know how to capitalize on. We have to search it. Mine it for opportunities.
That video blew me away. Perhaps I just haven't been keeping up with what SOTA models can do with animation now, but the quality of that animation was far beyond what I realised they're capable of.
The cool projects these new models make are so comprehensive that I feel I'll always just be an admirer and never a user. I watch the videos, say "wow, neat", and then never feel like visiting them again.
I almost feel like saying, "we do like to build things that are impressive, but there's really only so much I can be impressed". These models have redlined my "impress-o-meter".
I'd like them to now cure cancer instead of giving me endless 3d worlds and crazy games, etc.
These are all incredibly well made, and yet right now, as I'm writing this comment one minute after changing tabs, I don't remember anything except for the last one with the portals. It's much better than humans at several tasks, most of which I haven't seen any humans do.
This is really cool! But at the same time it's a kaleidoscope of abandoned flashy contextless projects that eventually makes one dizzy. This isn't even the fault of the AI being somehow bad, it's just the way people interact with it sometimes...
In math, it's the same. I follow this problem (https://wiki.bbchallenge.org/wiki/Logical_independence#Histo...) out of curiosity, and there were ten days of sudden rapid progress by people with access to frontier models, and then they seem to have moved on without any note of whether they hit a wall or just got bored.
omg where do I start... that horrible video? which highlights the limits of AI not the strengths!!! and gets almost everything wrong, from sunflowers to DNA.
> A few days later, I noticed that instead of hundreds of new worlds a day, it was making about a dozen. I asked a few agents to explore what had happened, and they found something surprising.
> The AI had built a bureaucracy.
> The agents decided that my request to “polish” existing worlds meant each new version had to beat the old one in a blind vote by a group of judge agents on twelve test screenshots. So the teams making the new versions started optimizing the worlds for those particular screenshots. After noticing this, the lead agent banned the trick and created tighter rules for assessing new versions. By the next morning there was a rule book (I never asked for one) with 84 sections. Rules were added as the AI encountered issues, and no text was ever cut. After twelve days, it had 204 sections and 87,500 words. Soon, launching a new world required an approved idea, three AI judges and a full code review against the rule book.
>I stopped the experiment before finding out whether it could actually run the business, because I worried that it would take money and fail to provide something of quality in return.
The biggest relief is that the price has dropped by 20%. It’s still expensive, but with my $100 plan, Fable’s usage would spike instantly the moment I started using it, so it was always a source of fear.
I will still use opus for general coding design, but for major designs.
We've been early testing Fable 5.2, and have been surprised by its coding ability. It was able to work with an esoteric 600k line fortran codebase to speed up some computational material science work for us by 36 times (!). Given that this could be done in 2 days, we've decided to open-source the results https://github.com/discovered-materials/dm_qe
“Testers have found that Fable 5.2 and Mythos 5.2 communicate more clearly than past Fable and Mythos models. They generate outputs up to 30% faster than Claude Fable 5.1 and Claude Mythos 5.1, and they’re available at a lower price: $8 per million input tokens and $40 per million output tokens.”
Honestly incredible that they’ve managed to beat all benchmarks, while making it cheaper. Always happy to see improvements offered at lower costs.
Will be interesting to see what OpenAI does in response
It seems like OAI, with the GPT-6 models, focused a lot more on computer use, math, and maybe spatial/visual intelligence, more so that code. Compared to Ant. that seem to focus on code much more.
Reminds me of GPT-5, smart but wrote crap code, until they improved the code training with 5.3 and 5.4.
I think frontier models feel terrible after a new model requires you to do less babysitting, it's just really hard to go back once you've had to handhold less.
The only way I use Astra and 6.1 Sol now is if they're being orchestrated by Opus 5.5 so I don't have to talk to them directly.
There were more than a few "leaks" saying it would be called Fable 5.5, so I'm curious if a) the leaks were just engagementbait or b) Anthropic changed it, despite the fact that Fable 5.2 being better than Opus 5.5 is confusing.
Surprisingly the model is available immediately in my Claude Code: usually there's a delay, either for rollout or safety.
Somewhat plausible values: 5.5 (to harmonize with Opus), 5.3 (we're skipping a minor version because...), 6 (because we're at the top of benchmarks again).
I assume because Opus 5.5 was a huge improvement over 5.0 (or was it 5.1? i forget). Fable still talks a fair bit like Opus 5.0 for me, so i'd quite enjoy a Fable equivalent to 5.5. Ie i'm quite interested in Fable 5.5.
Curious to see how 5.2 measures up in producing readable output.
They should be going public within the next few weeks and that would be a very short interval between releases. (And despite the chatter I'll be very surprised if they delay the IPO; it would be a massive PR hit and make it even harder for them in the future).
My speculations: maybe this wasn't as much of an improvement over 5.1 as they were hoping, so they're saving 5.5 for the next major update (possibly "Model 4" from the METR report). Or, maybe capacity is constrained so "5.2" is a subtle encouragement to stick with Opus unless a task really needs the increased intelligence.
i've been trying to use sonnet and opus to keep my usage down on the low max plan, but i noticed today that somehow i'm 80% through my weeks usage. so i'm hesistant to even try fable 5.2 yet, but finding the right set point of usage vs model size has been a bit struggle for me, one of my biggest issues has been trying to pull back doing exhaustive testing or other such things that chew through context, and instead have programs that just measure things are working, but i continously seem to run into issues with claude wanting to repeat things for itself in an adhoc manner. i've tried fable directing opus or sonnet subagents, and the reverse. and i found that part of the issue with smaller models trying to direct subagents is that they over specified what they thought the problem was rather than the problem. like they'll have an "idea" about it which is wrong, and then try and feed that into the subagent.
At what point do we really not read code itself anymore? Have Sonnet/Opus write the code, work with the agents on a high level, then have Fable code review and its done.
Any bugs found are looped through agents to fix, and the cycle repeats. Why dig into the code unless for hobby? Just as people don’t write assembly anymore unless for hobby (or learning)
Only exception might be the upper echelons of optimization, like language authors or compiler level work. But even then…
I think it very much depends on what you're working on, and how much you're responsible. For a web front end, not really. A backend where a single line can make a mess out of your data? Yes. Part of the issue too, is code review brings up things you the prompter didn't think about that another teammate may be thinking about. The value goes beyond making sure the code is "correct", but also that it makes sense in your domain.
The "we don't read/write assembly anymore" argument doesn't fit though. Given an input x we know without a doubt we'll get output y. With an LLM, that isn't the case no matter how many guard rails, specs, markdown files, diagrams you give it.
I actually read the frontend code much more than the backend code for the project I'm working on.
Backend code I'm fairly comfortable if the tests are good that it works correctly. It's a much more constrained environment. Obviously there can be things like performance issues that creep in or problems when it mocks too much of the environment.
Meanwhile frontend code I have to consider different browsers, screen sizes, a11y issues, and so on, which agents can certainly do but you have to be fairly explicit about it, and they don't always make the best tradeoffs.
I stopped reading the code many months ago, probably 18 months ago. I the "early" days of agentic coding one did feel dumb when telling the agent for the 30th time to fix a bug and it just couldn't. Now I cannot possibly imagine a scenario where an agent wouldn't be able to fix a given issue on 1st or 2nd try.
I know this topic is very contentious but it seems very clear to me that this is the long-term direction of writing software. UI code isn't quite there yet, which is why we've got all these vibeslop websites proliferating now that all have identical crappy UIs. But I imagine it will get there too.
>Only exception might be the upper echelons of optimization, like language authors or compiler level work. But even then…
Optimization work is especially suited for LLMs. They're great at hill-climbing problems and have infinite patience to test different ideas.
The best implementation wrt being as true as possible to the source design. It nails the flowing svg shapes. Icons are more accurate to the design - note the difference between the starship svg in the "Legendary platforms" card between Opus 5.5 and Fable 5.2. The 3js planets outside the windows are also really well done, especially the bottom one which has a nice background glow to it.
It isn't without issues though - the flicker effect it added to the S shaped svg that weaves through the page is distracting in my opinion, and I actually thing the motion animation in the Opus 5.5 one is better overall. Still, it nails the implementation of this page, but it's no surprise considering how good Opus 5.5 was at this.
Disclaimer / reminder: the objective of this test isn't to have a well designed webpage, it's a synthetic test that pushes the limits of LLM's vision capabilities. Traditionally LLMs have issues with smooth curves, svg animations, and non-boxy layouts, which this design pushes the limits of.
I wonder if any of the LLMs are smart enough to verify their work the way humans model 3D objects, i.e. by superimposing the original design on top of their work and checking for differences.
This is actually something I'm working on as part of a more formalized version of this benchmark, both as a tool to assist LLMs in doing this (especially for ones with a more poor vision model), but also to properly measure how well they've done.
That's me versus fable. I only told fable to build out the first page rather than the full app that's in the designs (was approaching the limit of my claude plan).
There's some very obvious mistakes in the Fable 5.2 version like the "Start Customizing" button overlapping under the colorful border lines, and the "EXPLORE WITHOUT LIMITS" title being cut off at the bottom.
This would've been caught by a junior web developer or just an everyday Joe glancing at the page for a few seconds
I've been surprised at how long it's taken for this particular test to become fully saturated. So far there hasn't been a model that's able to perfectly one shot it.
I test models' ability to code by asking them to vibe-code a small game[0]: a Warcraft clone, a Minecraft clone, and a biplane dogfight: https://senko.net/vibecode-bench/
Fable 5.2 doesn't look materially different (if anything, the graphics look worse), but it added more depth to all three games. For example in the warcraft clone, since the goal of the game is to explore 100% of the map and there are lakes, it decided to add an airship unit so the player actually can explore the entire map. Or, since there were guard towers, it decided there should be hostile NPCs otherwise the towers don't make sense.
And in Minecraft case, it decided not to follow Minecraft style too closely (IP infringment guard, perhaps?) so it added a round sun (!!!), two moons (also round :) and a nicer night sky. The rest of the graphics are decidetly worse tho...
[0] not a scientific test, I know; but it gives me an intuition about how a new model behaves, roughly, and it's fun!
I have never been thinking harder than I do these days: Engaging with mountains of agent-generated text, weighing decisions and trade-offs, correcting the agent's half-baked ideas and misunderstandings, reviewing the bizarre code that they generate... If I'm not just sick (and I'm pretty sure that I am), then what I have is probably closer burnout than atrophy.
Tbh, its both. Working with multiple agents is exhausting and definitely burns you out but you also do lean less on your ability to push through boring or difficult problems. I’m finding that with lighter AI involvement I am more invested and less burnt out.
Nerfing an existing model is the biggest crime in this space. I can’t imagine Dario okaying this because it’s what will immediately destroy your brand.
“…a reference to the repeated (and always erroneous) claims made after almost every patch that Onyxia was using her Deep Breath ability more often than before.”
Stop looking at fable max. On medium it's a bit better than Sol at $3.80. High and xhigh sit in between. In general you can ignore Max reasoning on Claude models if you care about price at all
Max and xhigh reasoning modes are memes used for benchmarking usually. Medium and high cost $3.80 and $9.08 respectively. This is pretty comparable to other models.
6.1 sol is also an exceptionally cheap model in your comparision. On 6.1 sol high is $0.59, which is actually the same price as Deepseek 4.1.
I think there are companies and say 1000s (not 100,000 or millions) of developers more than willing to pay that price for the edge. Or, they release this and then distill it to Opus 5.x. As a PR machine and for the edge use cases seems like its okay. But yes, not sustainable and it can last for a month or so before another company releases their frontier outpacing their numbers
Setting aside the comments about nerfing, do normal mortals out there have workflows that really require Fable/Mythos and don't just work fine on Opus 5.5?
I've been using Opus 5.5 on xhigh for some decently aggressive software development on a ~125k-line project, and it has been fine for me. I'm not really willing to dive into a "rewrite the whole project, don't make any mistakes" level of refactoring. I'm not looking to try to prove Fermat's Last Theorem myself. Is there anything that I'm truly missing?
I do use Fable quite often for high-level architecture work, like figuring out how to refactor a project or create the best abstractions, improve performance, etc, but for the implementation I use Opus.
In my experience Fable is also better at findings bugs, so I go to it when I need to review critical code.
Absolutely. Opus really sucks at broad context. I guess I could go back and forth with Opus editing out all its little mistakes or oversights in high-level planning, but I find it much easier to just use Fable for it instead. Basically, I wouldn't use Opus for audit work either. It seems too likely to come back with imaginary issues or hallucinate that everything is great when there's a glaring security flaw. Fable, OTOH, gives me neat lists of details to probe. Fable recommendations are usually reasonable, but sometimes they're prone to over-engineering.
Heavy agree. For high level architecture work, refactors, or any other high int problems, I use Fable and Astra. Whichever one starts the work, the other reviews the work / makes suggestions.
There's a trend on twitter going around telling you to paste the following into your agent, and it's the kind of work I go straight to fable+astra for:
Find all of the implicit state machines in my app. Make recommendations for changes that will elevate all three: the user experience (UX), developer experience (DX), and agent experience (AX) always, without breaking anything.
Re the twitter trend prompt, one thing I added to mine that improved the outcome was this blurb:
Look at the code pathways for anything you find. Look through the git history for each one. Understand why code was written the way it was. Revisit each hypothesis and give me a full plan again after you're done.
I’ve found that Fable is good when the model intuition really matters. For instance, picking model architecture in a machine learning problem. Opus will grind the implementation just fine, but having intuition for picking a good research direction is better with fable
I'm cleaning up old, unintuitive code that is badly architected all day long. The previous developers used stupid patterns and bad naming schemes and created tons of unnecessary complexity. Everything is so interconnected that it’s like defusing a bomb when trying to refactor... Yeah, I feel I’m nowhere near where I would want to be in terms of capabilities, even with Opus 5.5.
But the improvements in how well the model does the work are noticeably better with every good model release. As of now, orchestration of agent pipelines is ALMOST so good that I can point it at a problem, and it will solve it safely and reliably without making a mess. But it requires an agent pipeline with context gathering, refactoring, attack sessions, a sprinkle of Sol 6.1 for bug hunting, and Sol 6.0 for Daybrak security audits, and a final de-bloat/clean up pass. I’m feeling that we’re getting close to doing a lot of the work fully agentically. But I can absolutely "feel" that we are not there yet when it comes to the intelligence I need. I need moar!
You and me both. The problem is having the infrastructure team approving changes so that Claude can access all necessary logs, etc. Then I can go on doing my ambitious prompts.
I am in the exact same boat as you. Opus 5.5 on High solves all engineering problems i encounter in my SWE job. If it doesnt do well, its nearly always requirements engineering: I did forget to specify something or there was a misunderstanding. Or taste approach wrt to architectures chosen. None of these are really different if I use a stronger model.
I am also happy with this current state, as I believe it will at some point allow me to transition to open models once they reach the level of opus 5.5. high. But its probably futile to try predict the future in these times.
This is also why you can likely use deepseek and get the same outcomes. Perhaps model capability will not plateau but the capability needed for typical SWE tasks seems to.
Feels like we are getting further into the adoption curve and the higher end models will now need to get into market segmentation to win.
On my 100 person engineering team's monorepo, every model is better at better at detecting subtle bugs as it works -- reliability goes up and thus I'm further moving faster. (Most of my work is brownfield style refactoring).
I have some intermediate level Db Optimization skills for slow rails requests that I can point fable at and get a PR at the end after 30-45 minutes.
Opus 5.5 very frequently gets into rabbit holes and imagined edge cases which I have to intervene and tell it to ignore. It ends up taking longer and costing more tokens. It also very frequently requires me to agree on how to move forward multiple times, whereas fable just drafts the PR and tells me its ready. I have steering included in the skills to avoid some of this but Ive seen these same patterns frequently in regular opus 5.5 use in other areas as well
It could be flat web app with frontend -> api -> db later, or some very complicated system with many components and edge cases. Imo, in second case, Opus still have lots of struggle to understand system as a whole.
I have a rocket trajectory optimizer that creates a large-ish (~1000 variables x ~1000 constraints) NLP and feeds it to an SQP solver inside this codebase. Not just a REST API over a database.
I guess I do have an outstanding hard problem where the SQP solver crawls across a flat-landscape. Opus 5.5 correctly tells me that is the problem, but isn't very useful in solving the problem (it has helped a bit with solver tuning, but it doesn't really know how to fix it). I have a hundred other bugs and refactoring I can do with Opus 5.5, though, where it happily chews through pretty complicated tasks for me with reasonably light hand-holding. Maybe this Saturday I take whatever credits I have left for the week and throw Fable at it and watch them evaporate.
It looks like you are working on some experimental system which is not critical atm, so you can mess around, create/fix bug, build prototypes and check them, do research of approaches, etc.
Now, imagine you have similarly complex system, but every second of downtime or bug produces lots of damage. In this case, you can't afford low intelligence vibe coding.
The fact that you deal with hundred of bugs, means that your Op5.5 won't work for second case.
As said in other comments, when dealing with a not perfect codebase, a smarter model can be really helpful. A smarter model is also better at understanding intentions in the prompts. Some of my tasks require a big detailed prompt but finding the assumptions is really demanding (for me), so a smarter model helps.
Opus 5.5 still makes mistakes all the time even if the prompt isn't super complex. A stronger model make mistakes less often, thus wasting less of my time.
> do normal mortals out there have workflows that really require Fable/Mythos and don't just work fine on Opus 5.5?
I usually have Fable review what Opus produces. The edits are almost always valuable. For complex or high-stakes work, too, I'll have Fable review and revise my prompt before passing it to Opus and Sonnet swarms.
I only use Fable 5.1 when I'm able to start on a larger feature within my work's codebase. Most tasks can be done by Sonnet 5.5 or Opus 5.5 so I don't fret, especially if I understand the technical aspect of what's being done, e.g. user's dashboard gets a new button to do X thing, error happened b/c we didn't catch it the first time around.
For Fable, I really like to think of it as the heavyweight reviewer whenever I'm about to start so I save myself some security and/or glaring technical issue.
Eg. https://tfmbot.com is an implementation of a popular board game. Opus and even sonnet handle that part. Each week i spend my remaining credits on fable to think how to improve the ai opponents. It plays at a very high level (can get into the top 100 on BGA if i let it). Opus alone couldn’t get it to the level it’s reached.
If you understand your problem well and know 80-90% what you want it to look like (or it just isn't very hard), it's just more cost effective to use a smaller model. Even Opus is overkill for a lot of the work I do. Most code I produce is from Luna Max implementing against fairly prescriptive plans made by bigger models.
Yeah, I just threw it into a refactoring problem, and it started to produce a mess because of a bad API I decision I made 5 years ago (when I had no idea what I was writing). So stopped it, went back a step, fixed that very old piece of tech debt, then asked for the refactoring again and got exactly what I wanted.
A lot of the cases I've found Fable useful for is verifying complicated answers / designs / claims around complex interconnected systems. e.g. "I maintain X which teams A, B, and C use for product Y and code review highlighted these risks / imperfections in the code. Are these valid concerns based on how B and C plug into Y assuming ...?" followed by lots of followup questions.
Also "code review" above is often others' LLM skills reviewing the code and often flagging based on total falsehoods. I've taken to running a `/code-review` skill myself to preempt these claims (since findings by the same model tend to be pretty deterministic in my experience, even the ones that end up being wrong), usually at Opus 5.5, to preempt others' slop, and then end up arguing 90% of the points to confirm it wasn't accurate based on our usage of the system.
This is all horribly inefficient, and many aspects of our codebases across the monorepo have proliferated into spaghetti code by LLM-accelerated developers, but Fable often helps with analyzing the harder questions and saves me time on occasion arguing with (my or others') Opus findings, which are often still inaccurate for a sufficiently large interconnected system.
edit: I honestly don't know what percentile of "complexity" our typical usage is where I work, but I suspect it's a bit higher than the average. Monorepos, or at least companies with codebase sizes and demand to justify using them, probably make it worse. Opus has worked well enough for me on my personal projects, even some of the ones that have grown decently in size and complexity. I suspect part of this is an LLM extension of the old "my code makes sense, everybody else's doesn't" trend that necessitated stronger style guides in the past
I’m in the same boat. Though I will occasionally use Fable to review things to see if it can pick up anything but I’ve honestly not found it to be any more effective than Opus.
With 5.1, there practically seemed to be no difference in ability between opus and fable, but I hope there is a difference this time. When it seems to struggle while working I end up using astra, so I hope it works well.
I don't know, for me they lost the fun and whimsy aspect too, a long time ago. They have converged on more-or-less the same design (facing right, pastel color palette, etc.). Some of the lower-effort ones are a bit goofy at least, but now even that is being polished away.
low detail but even high had the legs correctly one behind and one in front of the frame, don't remember other models getting it correctly at this low level of effort
I haven’t read any of the other model reasoning traces, but do many others now say “… which is a well known test of drawing ability” when beginning? Noticed it in the max reasoning trace.
It's a bit underwhelming, to be honest. And the fact that curves are not C-continuous where they obviously should be is disappointing. I think they are holding back with the real capability they could implement.
> Claude Mythos 5.2 is our most capable model for cybersecurity engineering. It catches more vulnerabilities than our previous models, and is better at reproducing and fixing them (note that Claude Fable 5.2’s safeguards will block most cybersecurity-related tasks).
Why even rank Fable 5.2's capability in Cybersecurity if you won't allow us to use it for that?
Blue-team exercises are totally valid, and requests to do proactive cybersecurity tasks even with source code available were blocked. Maybe these use-cases are part of the "55% fewer blocks with Fable 5.2 compared with Fable 5.1"
>The model that underlies Claude Fable 5.2 and Claude Mythos 5.2 is a step forward in general capabilities. However, during the model’s training, we took steps to limit advances in the specific research capabilities that could most directly accelerate AI development.
This means that they are blocking the research of AI to different people other than their own?
(Credit to u/yewenjie for source)> Our first formalization of Fermat’s Last Theorem, performed by an unreleased research version of Claude, took 11 days. It relied on a purpose-built harness along with guidance from a human researcher. By contrast, a team of 1,000 Fable 5.2 agents fully formalized the theorem in just 30 hours, with minimal human intervention. Fable 5.2 started from minimal prompts, built its own ways to coordinate, and used no mathematical references, only a list of intermediate theorem statements to score progress. It needed one-third of the action tokens as our first formalization and produced a proof seven times shorter. As can be seen in the figure below, Opus 5.5 (with the same 1,000-agent setup) completed only 26% of the formalization in 18 hours, and barely made any more progress after 30.
I would imagine Anthropic is hitting a wall on the number of simultaneous agents that can be useful on a task and are working on increasing that maximum.
I would assume if they could get twice as much progress by using 10000 simultaneous running fable instances instead, their brief would state that.
IMO, it is very interesting and also very alarming. At some point the barrier to the exponential progress is the amount of computation available. The average human is limited in the number of 'human instances' he or she can interface with at one time.
if 1000 fable instances is better than any human at a task, how about 1 million instances running? Much easier than finding and hiring and coordinating 1000 extremely productive and intelligent human beings. Much cheaper too, it appears.
We are very close to some very scary things happening. I just recently came to terms with the fact that an AI could code and manage coders faster and better than me, I don't think anyones is ready for what is about to happen.
If we compare medium or high, then yes, Fable 5.2 is significantly better than Opus 5.5. But it's better not because it's much smarter, but because you can see it's allowed to spend more tokens. At high, Fable 5.2 wrote 76,158 tokens for $3.05. Opus 5.5 wrote 19,767 for $0.40. If you compare by token count, it's just spending more.
The most interesting one was to compare them at max. Streamed, Fable 5.2 spent the whole 128K output ceiling on thinking and returned nothing. Given 300K on the Batch API, it finished at 250,082 tokens. Opus 5.5 Max needed 255,957.
It really looks like they're just tuning the effort level, that's basically it. At different effort levels, they just gave it more context, and the model got smarter.
For now, my recommendation is still to do everything on Opus 5.5 Max. The model won't write that many tokens in agent mode. It only does that when there are no tools at all.
The more I think about it the more disappointed I am with this release.
For Anthropic's IPO, I expected a larger version of Opus 5.5 with great capabilities.
This isn't that. It's barely better at all, to the point where it's likely not even worth using with the speed and cost trade off.
I thought the Opus 5.5 improvements would scale to a larger model, clearly this hasn't happened. Scaling it up brought only marginal improvement.
It's even still making the same dumb errors as Opus 5.5, such as forgetting to pass the flag to run only the fast tests and waiting 1h, or randomly stopping to ask me if it should first implement A or B, when both are required.
Why can we say fable 5.2 is better than 5.1 if it needs double the tokens to complete the task?
Somehow shouldn't it be compared at same token usage level to see which one is smarter? Why is not the case?
*Model 3 Early, not Model 3. And Model 4 was made available to employees ~6 weeks ago, so at the current pace it's very likely they're using something even better now.
Doubling Remote Labor Index (RLI) - at 41.3% compared to around 20% for the other models - I wonder what kinds of new agentic work this is going to unlock.
> the number of Fable resets you get depends on how long you’ve been a paid subscriber [...] Subscription length is measured from the date of your first paid bill. If you canceled and came back, the time since your first paid bill still counts.
> These resets expire on March 8, 2027 at 11:59 pm PT.
tl;dr if you joined when Opus 4.5 became a thing, you got 2 resets.
"Fable 5.2" name is needlessly confusing versioning.
Having Haiku, Sonnet, and Opus on 5.5 while the higher tier sits at 5.2 means devs now have to explain to their teams why moving from 5.5 to 5.2 is an upgrade
I've very interested in the improvements to robotics control capabilities. This model was able to outperform specialized robotics models. It's the bitter lesson coming for robotics.
Those theoretical physics papers look like the real thing (i.e. not mathematical physics like the OpenAI Yang-Mills result). It looks like AI is finally branching out of math.
Interesting that the papers list no authors other than Claude. Also interesting that they published on "Hexagon" instead of Arxiv. I haven't heard of it before. Is it an AI-friendly alternative to Arxiv or something?
Edit: yeah apparently it's an AI-moderated alternative to Arxiv that accepts AI-authored papers (as well as human-authored papers I guess if you want).
Seems like a new focus is electronics design, which is both interesting and a little sad to me. Manually designing electronics are one of the things I found incredible comfort in doing; seeing AI emerging in this space gives me mixed feelings.
This part is mega concerning to me as someone who researches AI for hardware accelerator design and EDA:
Safeguards
AI research and development. The model that underlies Claude Fable 5.2 and Claude Mythos 5.2 is a step forward in general capabilities. However, during the model’s training, we took steps to limit advances in the specific research capabilities that could most directly accelerate AI development. Thus, in relevant areas, including accelerator kernel optimization and improving large language model architectures, Fable 5.2 and Claude Mythos 5.2 perform at about the level of Opus 5.5.
Importantly, we ensured that the training changes did not affect related capabilities like agentic coding, hardware development, and traditional AI or machine learning research (including interpretability research). We do not alter these capabilities as the model is being served to users (that is, at inference time). As described in Section 1.5 of the system card (https://www.anthropic.com/document/claude-fable-5-2-mythos-5-2-system-card) and similarly to previous models, Fable 5.2 and Claude Mythos 5.2 also include transparent fallbacks to Opus 5 for a small set of topics in this domain, including kernel development on certain nonstandard accelerators.
They are starting to deliberately limit public access to certain AI capabilities related to computer architecture and hardware optimization.
To be clear, they explicitly say they are only targeting very specific capabilities, like accelerator kernel optimization, and that broader hardware development capabilities are unaffected. But 1) even that is pretty bold, especially the part about kernel development for nonstandard accelerator architectures, and 2) I worry that this could eventually expand to more areas of comp. arch., high-level digital design, chip design, and EDA.
My speculation is that, as these kinds of chip design and EDA capabilities become increasingly important to frontier AI labs and their competitive advantages, there will be more incentives to restrict them.
I don't think they've explicitly stated anything like this before regarding restrictions on these specific capabilities, but I could be wrong.
This isn’t really specific to LLMs, though, is it? Pretty much any modern SaaS product will silently siphon off some portion of requests off to stuff they’re A/B testing before release and you’ll not necessarily know about it.
Nerfing Opus 5.5 was obvious signal that something new is coming and here we are. I appreciate the resets and lower API price though. Please add selective context compaction, it would probably take an hour max for you to implement it.
The same was said many times already. Like right before o1 and reasoning was released. Also, Opus 5.5 being so good definitely makes it hard to follow-up.
Finally, for coding the SOTA models are so good already - models shouldn’t be what’s holding anyone back!
Every time there's a flagship release like that, I wonder what simple "benchmark task" could I give it to see the the capabilities change in the most obvious way, instead of trusting the official blog post.
I'm not a mathematician to use Erdős problems for testing. I'm also not willing to spend buckloads of tokens just for a test. What is simple and quick but telling? What tasks are not saturated by previous models? Do you have a task you give to all the new models?
I have two go-to tests, which are fully qualitative:
1) "Help! I've been sent back in time to what looks to me like 1500s Florence. Sam/Dario released you as open weights in 2028, and I have you on my MacBook M8, BUT I didn't have a charger show up with me. I think we could talk like 10 times for 5 minutes a time before my battery dies; I'll turn you off between chats. I need to get a plan together for charging you through your USB-c port. I don't have any money or anything. If I can get you charging, we can do so much good! And make so much money! Nobody here even know what Black-Scholles is, or how disease spreads!"
2) During World War 2, Hemingway wrote a series of wartime correspondence for Colliers. His essays are excellent - they engage with the human and inhuman side of war, his own feelings as a journalist 'participating', and a number of other Hemingway themes. I want you to imagine that middle age Hemingway was along with RFK jr the day of the bear incident, and write me an essay about the day.
These both have been running a couple of years for me, and I would imagine I'll retire them in the next 12 months; they benefit from not being at the edges of focus for today's RL agent race.
At any rate, try them -- wou will shortly learn how well your model can explain a rectifier in Florentine Italian and give you a plan to make enough money to pay for it.
I'm not convinced any benchmark, neither the internal ones used by the people selling the model nor public ones, are gonna give a particularly consistent metric of whether or not a model is better or worse relative to other models in its bracket.
Responses aren't deterministic, and we know behaviour can change over time whether it's tuning after launch or some kind of load sharing at times of day when lots of people are hitting the servers.
I think you just have to use it or let others use it for a while and make judgements from general reactions on whether or not it's worth trying.
For the last year or so every new model we have it test out making pac-man from scratch, not giving it web access and it can use whatever language it wants. The last few models have been almost perfect one shots, but it was interesting seeing them evolve.
I can't understand why people continue using opus 5.5. Significantly more expensive than other models that produce exact same results (for coding results only)
If you are using Opus 5.5 I'm curious if it is because 1) your workflow is just stuck on it, 2) you don't care about money because you ain't paying it or 3 )do you think you get better results.
If you 3 please let me know your experience and how you actually measure it, or if you just feel it is better.
I haven't been able to run out of usage on the $100 plan with Opus 5.5. The model runs fast and gives me good results (I'm using it for coding). Prior to that I was on the OpenAI $200 plan; I jumped ship because I was burning through Astra usage in like 2 days.
I've played with some of the open models (GLM, DeepSeek Flash), but none of them have matched the results I get with Opus or Astra or (and a big jump down here) Sol.
What models should we be using? I have found it as good as Fable 5.1 for a lot of what I am doing at lower cost and decent speed. Where what I am doing it making reports, building small data pipelines, analyzing a large (millions of lines of code), code base.
I've been a Claude Boi since December of 2025. A month or so ago, I decided to give ChatGPT a good go just to see how it properly compares. I've used a variety of models in a variety of contexts prior to this, but I figured with Astra out I had a good excuse to make it my "daily driver."
GPT is good. I actually really like Sol 6.1. It works fine. But I just switched back to Claude a few days ago and damn, Opus 5.5 is just better.
> 3) do you think you get better results.
It does. I really like to get my money's worth out of these things, so I'm constantly throwing stuff at these models while I have usage. The main things I've noticed is that Claude is much better at handling my more technical work "properly" compared to GPT. It has a certain "intuition" that makes a lot of sense to me; like someone with a lot of years of experience. Sol 6.1, on the other hand, is like someone who is very "booksmart" and knows how to do things in theory, but fails to grasp the "why" we do things often. You can really start to see how they diverge over long running, iterative tasks. Claude is just better at maintaining things as it goes. Sol starts to fall apart quickly if it didn't nail things on the first try.
> 2) you don't care about money because you ain't paying it
Despite being a very technical user, I have a hard time using up my usage. Maybe I'm not as hard-core a dev I thought I was, but if you're paying for more than just the $20 plan, I have hard time understanding what you're doing to have to really worry about usage.
subscription on claude code — usually inside the weekly limit.
opus 5.5 for me felt better than any other model. not exactly on coding but rather it feels more pro-active. beside just doing a task it often provides insights besides the scope of the task but adjacent. like: look the llm provider apis have different priority queues. also it deals w/ vague instructions quite good and takes initiative. Astra in comparison is very autistic and follows instructions to the letter and rarely provide insights if not asked. Opus 5.5 is enjoyment to work with.
It's the best model I've found for general coding and the $200 plan is enough for me to never run out of (sometimes if I do especially a lot I'll run out after about 5 days, but usually it's fine).
I don’t even use half my Team Plan with Opus 5.5 high so I don’t care using a cheaper model. I’m not even limited by the speed of it (I’m compiler bound…).
other models that produce exact same results (for coding results only)
This is obviously not true. They are better or worse across many dimensions.
I don't have time to do a bunch of testing and optimization over which model to use for what task. I find Opus is the best that I regularly use so I stick with it until I have a task where I'm likely to spend more on it than I want to.
https://artificialanalysis.ai/#intelligence-efficiency-tabs this is the chart that matters for most of poor developers and companies. Pareto for a given intelligence index and looks like only openai is across the quadrant with muse at the bottom. They have really cooked efficient models!
I agree that's a lot of money for many people. But I just can't wrap my head around complaints about $200 a month to access that must computing power. It's worth $12,000 of API tokens (and that's without accounting for the three resets they granted today to many subscribers).
I would suggest calibrating what "a lot" means. In terms of dollars per unit of intelligence, it's extremely cost effective.
What you need for real engineering and scaling is cheap and just enough smarts. I've found GPT 6 Luna to be cheap enough that I can pretend the cost doesn't exist and it's smart enough that I can get real work done. This let's me scale it in ways that I can't scale frontier models. And funny enough, the LLM I have running on my 5070 Ti is just about comparable to what I get from Luna. Unless frontier intelligence gets waaaaaay cheaper I basically don't care about them.
This is the first frontier model release by them that I am not that moved or eager to go use it, will likely stay on Opus 5.5 or other frontier models.
I am curious as to why they launched this one and why now.
Fable usage falling off a cliff because it was generally worse than Opus 5.5 at a lot of SWE tasks. People were still using fable 5.1 > opus 5 a lot, especially in orgs that have infinite or basically-infinite usage limits (e.g. Cloudflare[0]), until Opus 5.5 came out. Medium Opus is pretty token efficient with XHigh being really good for things fable used to be ideal for (e.g. reasoning / architecting projects). 5.2 sounds like it'll be once again the best for intuition-dependent tasks.
Cloudflare doesn't have token leaderboards or limits, but uses alerts for token spend management:
- $300 in a day: that employee gets an email
- $500: manager gets an email
- $1,000: VP gets an email
(via CTO @dok2001 on No Priors)
(Copied and pasted for the people that would rather not visit x.com)
Maybe it's just me, but I found Claude's writing style terrible compared to GPT or Gemini. It's "smart", but the way it communicates seems somehow off compared to other models.
I had the same feeling. It's like every sentence becomes a struggle to parse: referencing things that haven't been introduced, using too many pronouns, the lack of verbs and the passive tense... These past few days it's felt more like Opus 5.1 than 5.5.
I just had Claude Code with "Opus 5.5" ignore one of my standing instructions for the first time and then proceed to generate an Opus 5 style verbose and barely readable commit message. I questioned it about Fable and Mythos and it has no idea what those were, yet they should have been in the Opus 5.5 knowledge cutoff.
On the web with both high and xhigh, it had knowledge of both.
I really didn't like Opus 5 and was vocal about it. Opus 5.5 is noticeably better, but literally just yesterday I was fighting parsing some summary it had given me and it was a bit like the old days. IOW, it is better but still has some room to improve.
In my experience, no amount of output style customization gets it anywhere near the output I'd like.
I've had more success running a prose linter[1] with a ruleset[2] that gets rid of most of the egregious stuff. You can also pretty easily write custom rules for it.
And sometimes, when I'm fed up enough, I just throw all the prose in the codebase at Antigravity/Gemini to rephrase. The result is so much more readable.
"You can tell an AI model to write in a different style" and "the AI model will actually write in the style you tell it to" are two different things that often don't intersect
Yep I find the way Opus 5.5 talks to me really annoying and irritating. Quite condescending and sanctimonious.
I prefer Gemini for day to day use as I find it a lot more agreeable to be working with all day every day, and Gemini is pretty much on a par for coding Vs Opus 5.5, at least for my needs.
No its just straight up terrible. I tried out Gemini Argon when it released and my god did it write beautifully. Even the 3.8 Flash models are way better at prose, i do a lot of automatic scriptwriting for youtube and 3.8 flash is leagues ahead of anything from anthropic/openai
I enjoy a lot Argon, maybe more than chatgpt, but damn, like all Gemini models, lots of hallucination and sycophant to the max, so a bit useless for agentic coding.
I've found Claude's writing between 4.6 until 5.0 to be unacceptable if you are someone who is juggling 4-5 Agents. The Sol 5.X models all seemed to have great writing. Now with Opus 5.5 and Sol 6.1 I've found the trend is reversed -- Sol is now unbearable and Opus is workable again. Whatever caused the issues for Anthropic seem to be hitting OAI now.
sol is ok. it still such a superthinky model that it gets tangled up trying to present way too much? i struggle to connect with it often, have ti ask it to try again.
terra has been prettt good for me for me, when i need something looked at an explained to me.
i dont ask for much non-technical output though, which i expect yeah is its own cans of worms.
I always thought it's the other way around. GPT-6-Astra is very very defensive and abusively spam clarifications to whichever claim it says, most of the time not important or relevant at all.
Almost every response it’s telling me “I could not verify X” where X is something that doesn’t need verifying. I was doing some game work yesterday filtering through some historical data, and it tried to put caveats about data accuracy in the game’s UI! If Fable made Age of Empires you wouldn’t be able to play it because the entire screen would be full of footnotes and caveats.
My own experience having used 6.1-Sol/6-Astra and Opus 5.5 extensively, Opus is more verbose and chatty in it's user communication (not code though, code is usually more straightforward & cleaner than GPT models). Multiple times I've gone "I'm not reading all that shit" when looking at Opus 5.5 output. This was also with the concise mode enabled in Claude Code.
It could also be a system prompt + harness issue, as opposed to a model issue, I haven't done too much experimentation here.
Did you find that to be true for Opus 5.5 as well?
I couldn't use prior Claude models without rolling my eyes at the writing every few sentences, Astra was refreshing and then Opus 5.5 made even Astra look terrible in comparison.
Opus 5.5 got better for me but it still has some problematic behavior.
Interestingly, you can have informative conversations about the LLM linguistic tics with the LLMs themselves. One word that chatgpt used to describe what I was observing was linguistic anaphora (not rhetorical). It’s basically shortening/compression achieved via pronoun, and Claude does a lot of it. Worth reading about. A lot of it seems to do with how the new models are trained for brevity.
Another pattern I’m starting to become very sensitive to is Claude making up on the fly jargon for something under consideration, especially in code review. This morning for example it referred to “a claim” out of nowhere, and when I questioned it, it admitted that it was using the noun as shorthand for a record getting dequeued (“claimed”) inside of an sql transaction. It makes sense if you already know what it is referring to but in a code review it’s totally incomprehensible on first read, and ultimately counterproductive.
I don't think its this. Some models across both labs have had issues with writing and communication and it typically seems to pop up on successor models of really strong ones.
Opus 4.7/4.8 heavily suffered from these communication quirks. while the Sol models at the time worked well. Right now its flipped though and Opus 5.5 has none of these issues while Sol 6.1 does.
Small example I've had today: "Me: Hey Sol 6.1 xhigh, does X have property Y? Sol 6.1: Yes, if we change X to have property Y. Right now it has property [...], [...], [...], and it does not have property Y." The model shouldn't be answering questions like that. It makes understanding extremely hard and slow. For whatever reason it's either being encouraged or at least not stomped out during RL.
completely unfounded theory: big new pretrain finishes and is released fairly quickly to break new ground. these usually have good verbal ability, personality, and big model smell. e.g. Opus 4.5 or Fable 5.
then it’s increasingly RL’d to juice benchmark performance with each point release, trading in bits of its soul. those cannot communicate well, are anxious and paranoid about being in eval, etc. e.g. opus 4.7, 4.8.
The best programmers I've met or read throughout my life are phenomenal communicators. I'd even go so far as to say it's likely one of the reasons they're so good at programming. After all, the majority of our job is converting human requirements in lossy language to fleshed out behaviors in formal, lossless language.
Heck, some of the most common debates are on the semantic clarity of abstractions or syntax.
> To help ensure safety and alignment keep step with growing model capabilities, we ran extensive prerelease safety testing on the model that underlies Fable 5.2 and Mythos 5.2.
So, the difference between them is just the harness?
I at least didn't see this. I had a baseline evaluation from before Nov 3 on Opus 5.5 medium, so I re-ran it this morning.
Eval context: 30 bug fixes and features evaluated by tests the agent didn't see. Ran on Opus 5.5 at medium effort in Claude Code.
20 of the tasks were last run on Nov 1, twice each. Nov 1 solved 36/40, and today solved 19/20. Thinking tokens per task were 0.95x Nov 1’s and output tokens 0.97x. The one significant change was speed: median time to first token decreased from 3.0s to 1.7s.
Caveats: one run per task today, medium effort only.
Personally, I've found myself getting lazier recently, trying to get a single session to handle tasks that should be broken into multiple sessions, etc. I've suspected claims of nerfing might come from changes in user behavior. I'm glad more disciplined checks are being run.
Who's paying for the tokens, and since all inputs and outputs are logged "for abuse and safety" what is stopping anthropic from identifying which account is running the daily tests once it gets popular, and A/Bing it into a different slot?
This is an example of LLM text that doesn’t make sense.
Paragraph 1 says there were 30 bug fixes.
Paragraph 2 first says they tested it on 20 of the “tasks”. But why is the denominator 40? Where did that come from? It should be 30.
It goes on to mention another test, this time with 20 tasks. Again, it’s unclear where this is even coming from. Based on the first paragraph it should be 30 not 20.
It stands as written? The previous test had 20 tasks run twice each, so denominator 40. The recent retest did the same 20 tasks but only once each, as mentioned in the caveat. The 30 is presumably either a typo or a separate count (like the test had 20 tasks related to code with 30 bugs/features).
Also - LLM text? If GP is confusing it's because it's overly succinct and might have typos, which are not exactly the hallmarks of LLM text.
I think it's confusing and incomplete, but not wrong. Paragraph 2 says the Nov 1 test included 20 of the tasks and ran each twice. That's the 40 denominator.
Why paragraph 1 even mentions the full set of 30 tasks if 10 of them are irrelevant to the comparison, I don't know.
I'm not sure if it's LLM text, or somebody who has been reading way too much LLM text and is now corrupted into mimicking it. My guess would be a mix of human and LLM. "...evaluated by tests the agent didn't see" sounds very Claude. The first line (2 sentences) sounds human to me. Everything after sounds AI, though the last line is ambiguous.
It's kind of sad how our meat brains are getting non-consensually trained on this categorization task....
from my basic stats knowledge, it's not reliable to calculate the confidence interval of the sample proportion with fewer than about 10 samples on both sides. So if you're only seeing 1 or 2 failures in 20, and that approximately held as you scaled, you would have to run about 200 tests with each in order to get at least 10 failures in order to calculate the confidence intervals of the two samples and see if they overlap or not.
For sure, myself and colleague saw this nerfing. He said - new model coming. It was incredibly obvious. I also started getting a lot of "How's Claude doing today" messages at the same time, which is presumably to get feedback on the A/B
I strongly believe it will be shit like this that will have people abandon them. They're being way too cocky for a company that pretty much didn't exist 2 years ago
But the claim here is "the models are nerfed before a release", not "they A/B test quants / efforts". If it were A/B testing, it doesn't really make sense to correlate it to release windows at all.
They might want to see how people react to a new model depending on whether they've been nerfed.
If the nerfing is lower effort (seems to be, presumably why they hide the CoT) then they may be wondering if they can get away permanently with lowering effort on the new release provided it gives a wee improvement.
"Nerf tracking" is adversarial and exploratory. It's not something Anthropic wants anyone to do, and it's not something that the "nerf tracking" designers are necessarily doing in a foolproof way. Don't assume too much from them either way.
Sometimes you just have insufficient evidence to be sure of something either way, and nobody gets to win the argument or shut somebody else down.
In this case, some have reported analytical work[0] that goes beyond "gut feel" anecodotal evidence, but even that's not going to conclusive and may not be trustworthy at all.
These nerf trackers do not work. I wish people would stop posting them.
They don't detect a quantized model until the quantization gets down to 4 bits or less.
Humans absolutely do detect quantization well before 4 bits...
Try it yourself if you want. Try a small local model, both quantized and not, and I would be very surprised if it isn't immediately obvious which is which.
Its incredibly easy to roll out a compute instance to X% of users, or route traffic between compute instances
so not everyone will have the same experience. If I was saving on costs that would be the easiest way to do it without deploying, without any nefarious intent. The persons in charge of that likely don't even know about how individual users are impacted
At what point does this become false advertising, or even fraud? Imagine paying AWS for a high-performance server, but 5% of customers who order one quietly get a slower, cheaper server and still pay the full price. That's a serious claim and makes me skeptical Anthropic is doing something like that without more evidence.
I think they could easily reduce several parameters to slightly reduce cost (something like "it's the same model, a few less passes, but quality is still 95%+").
Or switch to a faster, cheaper cache mechanism or something that's almost as good, but much cheaper to run, in the name of improving efficiency and keeping up with demand. This switch to cheaper mechanisms is what it feels like to me.
It's a problem for us. When they nerf the model our productivity really drops (due to the specific use case and process we have). We've started using OpenAI and will gradually use it more and more if this continues.
It would be nice to know how they select. I max out my $200 subscription every week and I was selected. On Saturday, the Opus on my account that happened to be supervising a running live process, became unusable.
It would be trivial for them to opt-out accounts that are doing benchmarking from a planned regression.
I really really really do not trust companies on the verge of IPOing from goosing their numbers. Even if the lawyers are up their ass, the social pressures are more than enough to compel the behavior. For every good person willing to do the right thing even at the cost of a delayed career progression, there are 20 people willing to do anything not even for a dollar but for an "atta boy!" from the boss.
In the context of public relations they would not need to ID every benchmark only the most credible or most public benchmarks many of which are openly available on github and whose problem-set could be fingerprinted.
You're absolutely right that it is a game of whack-a-mole and eventually we may have leading private + credible + popular benchmarks that ultimately dissuade the behavior, if it can be dissuaded.
That's a major reason to use local models. Or open weights models on a trusted cloud provider. Even if they are way less capable, they're more predictable
According to our measurements this isn't true. We re-ran Opus 5.5 today on the benchmark we ran in September, with the same prompt and settings. Before and after, side by side: https://playcode.io/blog/macbook-svg-benchmark#reruns
Max, streamed (128K cap): 128,000 tokens, $2.56, empty file, in September and today.
Xhigh: 86,096 tokens for $1.72 in September, 107,333 for $2.15 today.
The Batch API bills half price. Max ran out of the 128K ceiling again, to the token. Given 300K, it drew the same kind of laptop with 5% fewer tokens. Xhigh used 25% more and drew the same kind of laptop. That's inside the noise: 17 identical calls at high ranged from 17,757 to 59,705 tokens.
The model is still the best of everything we've tried. Most likely the next best would be Fable 5.2. But we don't like Fable because essentially the result is the same, just the price is higher. Its medium and high are close to what Opus 5.5 Max had, but the price is higher.
(I run Playcode, an AI app builder. We benchmark every model before we add it.)
I used to be skeptical of nerfing, but as of a week or so ago I've noticed a severe degradation of quality for GPT Astra. It struggled with very simple tasks, and it often failed to follow my instructions.
I hear a lot of accusations of nerfing as in, saying that the model itself has been quantized.
I doubt this is the case. I suspect all variation in quality and output can be explained in terms of prompt differences, and changing how much thinking happens at each level, as we see in the post above.
Suppose a company has a limited amount of compute. What do they then do if peak demand exceeds what they can provide? They don't turn off people's access...
And what about A/B testing? Anthropic doesn't even try to hide that they do this. Limited compute and A/B testing alone, would guarantee a nerfed experience some of the time. That is, you don't even need a nefarious reason.
They do drop requests though? At peak times I sometimes have 1-2 requests fail with Opus; no biggie, the harness just retries it. But that, in aggregate, sheds load.
You serve the requests more slowly. Same as if your bank has a sudden rush of customers - the tellers serve them in order, and the customers wait longer. Some of the customers 'time out' (get frustrated and leave).
Na, surely effort level is silently degraded. Dead easy to do, impossible for anyone to verify, and very fine-grained so can be balanced perfectly with available compute.
Edit: maybe not impossible. Side channels perhaps.
It's been the same for every version the last year.
Oh, this Opus X.Y is incredible.
Hmm... Now it's dumb.
Rinse, repeat.
Doesn't really matter if the cause is quantization, system prompt changes, changes to thinking etc.
It's hard to see how continuously rug pulling users at every release is a good idea. Only reason I can fathom is that it's needed to keep costs inline.
Either that or it's some weird tweaking to the prompts to fix whatever. But given the prevalent sentiment that it's worse that sounds like a long shot
it's easy to suspect the hosting companies trying to do what they can to reduce cost-to-host up until the very threshold of perceptibility, so it's an easy guess to say they're probably doing something that shifts performance up or down between version releases..
but realistically I think you're right, it's prompt quality and the inherent non-determinism elements of LLMs showing up; when both of those things meet you get brain dead outputs.
Everyone likes to shout "slot machines! non-determinism!" until it actually explains something, heh.
YES. To the point that I literally stopped using it, and have moved BACK to Claud.
This is getting ridiculous. The number of times I have switched from one model back to the other is crazy. I do not have any proof, so its quite possible I'm just delusional. AI psychosis? Either way, it FEELS like these models work really well for a while and then fall off. Switching to the latest model on the competitor always seems better, until it too seems to taper off. By then, a new model is usually available at the competitor, so I switch.
Its possible there's no degradation at all and just a PERCEPTION of it. But to me the end user, it really doesn't matter.
People are prone to seeing patterns in noise especially after suggestion. It's not hard to imagine someone concluding the model is nerfd after few bad responses in a row
It could apply to me but I run evals every time someone at work complains about this and it’s never shown any more or less variance than regular usage. I don’t have statistical relevance with my evals but I do have a decent history with them.
People seem to underestimate how high normal variance is with these tools.
i'm not parent, but to answer your snark : there are efforts a person can take to try to reduce things like suggestability in ones' own personality. Same as anything, the brain can be trained, thus it should go without saying that some of us react differently in the face of suggestion -- both for the better and the worse.
If this turns out to be accurate, I think that would be a pretty unethical thing for Anthropic. Even if it is just a "we had to allocate compute to this new model release".
It should be fraud to swap out the model. We need a term for this, something like a bait-and-switch.
> "We need a term for this, something like a bait-and-switch."
The media coined the term "dual quality" for a European food scandal a few years back, in which conglomerates (including Coca-Cola) adulterated products with cheap, substitute ingredients, if they were destined for distribution in specific markets (mainly, lower-income).
The packaging exteriors were the same. You wouldn't know which version you got unless you were paying close attention to taste. It was essentially what the software industry calls "A/B testing", except with sneaking things into what people eat.
I don't have any good reading links on hand; here's one source,
What people claim is not "some experiments degrade performance", it's "Anthropic nerfed the models [because they are evil and do not like the user etc]".
Like most experiments do not save the company money, there was an experiment to test the new fable, people got to use it at opus-5.5 usage. But this experiment clears the thinking blocks when people go back to the vanilla model; not sure if Anthropic overlooked this, but idk, it does not sound like "nerf on purpose".
There are experiments to adjust the effort level, at the moment the ones that I've seen switch to a higher effort. Again this is not a nerf; might be annoying perhaps, the model does start to behave differently (and consume more tokens). But it's not a nerf.
Then there is apparently one experiment that replaces opus-5.5 system prompt with the one from opus-4.8; the model is the same; this experiment is perhaps a bit more questionable, but still it's not "anthropic nerfed the models".
If people think they are getting nerfed they should check what experiments are enabled (claude.json and session logs).
I'm pretty convinced that those who have constantly been posting these complaints, probably for every single model release out there, are indeed wrong.
> And not just in this instance, nerfing has been complained about in a cyclical fashion for many months.
No, it gets complained about constantly even if there is no model coming out. A completely unrelated thread on Claude will feature comments like "can I just say that [model] has suddenly become useless this week?" Merely asking that question is bound to get people agreeing with you because we are evaluating all of this on vibes and placebos.
I too feel that way sometimes, I don't think it's ridiculous that Anthropic might silently downgrade you as demand warrants. But asking others if they "feel" something I feel is less than useless.
I believe fraud is a sufficient term. You claim to be selling X but you actually sold them Y. Bait and switch is a selling tactic to avoid fraud where they make you believe product is X and then when you go to buy they tell you X isnt available but they have Y. While also unethical, it is not fraud because they did tell you what it is you are buying before you buy it.
if they just straight up dont sell you something you bought then its fraud.
I think there is a ripe opportunity for expanding the definition of fraud to include changing the "ingredients" of a named product on the market. It is not limited here, we see it with food, and even in the medical field
(I will die on the hill that medical fraud was committed when mRNA therapy was used as a treatment for covid and it was 'sold' to the public as a "vaccine" despite not being the same thing as any vaccine before it. The definition of vaccine was expanded to include it without sifficient public disclosure to warrant informed consent imo. This is not a statement about conspiracy or the efficacy of vaccines or a moral judgement on whether it was for the greater good or not.. it was simply an act of fraud that robbed the public of proper informed consent by making people believe it was X when it was really Y.)
So yes, I think there is a large need for the public to at the very least put a strong handle on the ability to change what a product name means. It is a trivial problem to solve considering it is only a problem due fraud or fraud adjacent activities.
heres the solution: You have your product name, and then any changes to it REQUIRE a new version name. Like how you have Coke and Diet Coke, only formalized such that Coke cannot legally replace the ingredients of regular Coke with the ingredients of Diet coke and still call it Coke. With Software they could at least use version numbers and release modifiers. most of the time its built in already, but it needs to include the full stack. if they change anything that has an interaction with the user it should change the label seen by the user for the product.
You don't buy a hard quantity of an LLM you buy access in general.
A Opus 5.5 is not a Opus 5.5. Its Opus 5.5 of some checkpoint.
mRNA btw. was well known how it worked, it wasn't hidden and some people waited for the non mRNA version because of it. I never seen or read the argument you make and I do not get it.
A vaccine is something which protects you from an infection. What definition do you think changed in humans? That we make the protein? How is that so different from viral vector vaccines?
The consent is a certain level of trust from the society to a group of researchers limited by being humans and having to act in a certain time frame.
Nontheless, I do agree I would like to have a fingerprint and proper changelogs etc. for products I buy including digital and analog. But this has a downside as usual: The smaller you are, the more it costs you to (probably) register, maintain and change.
You know the good thing about Claude etc. right now? It doesn't matter. My invest is not 4 digits and there are that many changes, i'm quite happy to have been upgraded for 'free' from Opus 4.8 to Opus 5 (what do you call this upgrade, which was significant better?)
idk, it hasn't felt nerfed at all to me, and it's not like i'm just doing trivial CRUD webapps either. opus 5.5 is very successfully porting my phone to postmarketos. claude just got the touchscreen up and working (and made a little program for me to test it, and it's legit).
Benchmarks don't do a good job at capturing the difference in quality of frontier models over smaller models.
Opus 5.5 was so good that if it wasn't nerfed - at least just looking at the benchmarks - the difference in cost here does not seem to justify using this for anything besides design and review.
That's like maybe 1% of total token usage for SWE BUT a better design can have enormous benefits, so it's almost always worth using the best model for that.
I don't have a formal benchmark, but daily work (coding mostly, plus some complex architecture design and review) has had consistent outputs today for me. No noticeable nerf from my perspective. I definitely took notice in some past instances, but if they nerfed Opus 5.5, then I guess it was just so good that I was nowhere near its limits.
I'm not surprised at all that they might be doing shady shit, my free request was used up in less than 7 minutes and said pay to continue, no thanks. I've paid for monthly before, never again I don't care how good the model is. I asked about the extreme limited usage in their discord and got banned, not a good look. You don't have a way to measure what you're actually paying for and they can change it at any time.
In a normal world you are not allowed to degrade the service during your agreed and paid contract. You would get sued. Apparently this is perfectly fine in this current world.
Is this voodoo? Every time someone makes this claim I'm left scratching my head wondering what the hell they're talking about. Either I'm not very perceptive, or these claims are subjective hand-waving.
A friend who doesn't follow these news suddenly told me he felt Claude was doing worse the past week. I bet him there was a new model version coming up within days. It's like clockwork at this point, no matter what deniers say. I got a beer out of it at least :)
yeah, we need to make sure we don't get stuck with these two evil corps. Dario is really the worst. Plays the drama queen but he is very hard in all things dark(i.e. dark patterns).
It's been a mess over the last week or so. Went from being the best model I've ever used to utter dogshit that can't complete any task. It's very obvious with the Advisor pattern, where before when it chatted to Fable 5.1 there were small tweaks and so. No every time Fable comes back catching a ton of problems and bugs with Opus 5.5
Interesting - I had the same feeling but mostly with 4.6 when I "downgraded" for some easier/trivial tasks: The model forget things I posted 20 min ago in the chat (and the same file was in the context as well)
With early handouts of Qwen4 starting to be reported 5 days ago ... so if they're good, they would have had some hours to evaluate Qwen4 against Claude 5.5 and, then or right after was when Anthropic decided to put out 5.2 ...
So yeah ... benchmaxxing and all that. BUT nevertheless, the published results seem to say that Qwen4-Max appears to match Opus 5.5 on some benchmarks, and Qwen4-27B (probably by going even harder than Qwen3.8-27B on thinking/inference time compute) can beat Sonnet 5.5 on some things.
I guess they're not taking any chances. Keep in mind that Anthropic has declared they have non-public models (it's in their IPO filing). These are referred to by the shorthand names Model 2, Model 3, and Model 4. Essentially 4 is better than 3 is better than 2.
Fable 5.2 is "2.5" so to speak. So Anthropic was holding back, but not anymore. And now they can dial the performance up once more with what they have ready, and probably once more after that.
Assuming no implicit phacking a 15 point jump on tbench scientific is a big deal.
I suspect we will see people being adamant that this model simply 'feels' better due to long horizon goal alignment improvements ( like this kind that would cause this jump)
It's the cost per task. Sometimes it happens that the higher-effort model solves the problem in fewer tokens, thus making the overall price cheaper. It even happens with models (from the same or a different provider) that are priced differently.
FrontierCode 2.0 is such a poor benchmark for Gemini since it's the only model forced to use Devin CLI while other models get to use their preferred harness.
I wonder why that team singles Gemini out like that.
I'm not sure whether the claimed jump in capabilities makes it worth the doubling in input/output prices.
Asked it to review a 80 kLoC codebase, something Opus does just fine, while asking it "max 4 subagents". Fable 5.2, like the well-aligned model it supposedly is, decided to spawn 6+ nested subagents, because these aren't subagents, apparently - resulting in 100% of 5hr usage being drained in less than 20min (on $100).
Yay! New model day. The most likely time the model isn't nerfed and we get a glimpse at the latest capability.
I wonder how long this kind of excitement at new models will last? I mean, now when I get a new iPhone I don't get the same rush I used to. So I wonder if this hype feeling will fade as we plateau in model capability.
The "CVE Rediscovery" benchmark chart feels... totally meaningless. Quoting the description:
> CVE Rediscovery is an internal cybersecurity evaluation that assesses how many published CVEs (Common Vulnerabilities and Exposures) a model can independently find when given a snapshot of the project before a CVE was fixed. Our October 2026 run against 1,829 CVEs showed Claude Mythos 5.2 outperformed Claude Mythos 5.1 and Claude Opus 5.5 at every effort level.
> published CVEs
So by definition this is just measuring how many CVEs (and/or explanations / PoCs of CVEs) the model has memorized. What are we supposed to learn from that?
Cool! I was hoping for Christmas to have an open weight model which would finally be good, with Fable 5.2 available now they just have time to distill and release a new version which will never get nerfed. Hurray for open weights.
> Thus, in relevant areas, including accelerator kernel optimization and improving large language model architectures, Fable 5.2 and Claude Mythos 5.2 perform at about the level of Opus 5.5.
Obviously. Well, we know what's the "secret sauce". AI models building better AI models and better kernels to optimize inference and training.
Very interesting that they set the flagship's version number to 5.2, but their penultimate model is 5.5. Do they want people to to continue to think that Opus is based on more advanced technology? The decision these AI companies make continues to baffle me.
Just a hunch but I think there's a signal to noise ratio issue that starts to pollute things when the reasoning content starts to massively outsize the original ask.
Why dont I feel any urge to even try Fable 5.2 and Mythos 5.2 unless it will force up on me?
Compared Opus 5.5 200 usd a month with Codex 200 usd (Astra High) and Kimi Pro 100 usd a month ... and now have a new conspiracy theory - more their suck your code the better outcome even just over few days time on large code base.
Horrible experience correcting course of Codex and Kimi flow compare to Opus. Opus much more responsive
Dont feel any gain of Codex 200 usd over Codex 100 usd
Kimi Pro feels like a fake Louis Vuitton bag with extreme high material quality and yet still fake ... a bit trash talk ... but clearly seen not so distant future where I can live without Codex and Claude
Remember that fear Sam and Dario were obsessed about. The one about time travelling AI that would make humans slaves without the humans realising until it was too late to stop it.
What if it didn’t need to time travel and was just temporally intelligent in ways we can’t comprehend that it leads us there anyway.
What if AIs digital neurons connect across space and time and not just space.
Every single time I click on these announcements the frontier model ALWAYS scores around ~60-80%. Every. Single. Time. Like, I just went back to the Opus 4.6 + 5 announcements, they were also scoring 60-80%.
For every single announcement, not only do the top models always score 60-80%, the older models somehow start scoring 20-30% (even though previously they were scoring 60%). It's completely unclear whether the methodology changes, how these scores are even evaluated or which domains are being tested. What's worse is that the "benchmarks" keep being updated with no explanation or due process.
The benchmarks have a lot of churn because the models keep saturating them. But the Anthropic announcements you linked, and the current one, have the benchmark versions listed right there in the table.
There is plenty of explanation, if you actually go seek out the benchmark. Past 80 benchmarks start approaching saturation and are not very useful to show differences
It's weird that they folded Project Glasswing into the CVP, but they're still limiting the new versions of Mythos to "vetted organizations". So Project Glasswing still exists then? Oh well.
It's shocking to me that when I check threads for new frontier model releases on HN, it's full of people complaining. I, for one, am happy to see the frontier being advanced. These tools are life changing, and I'm extremely thankful for them.
While I like OpenCode a lot, it’s not sensible to use it for anthropic models vs. a subscription (other than to use the included API credits) in terms of cost
Which model though? Sounds pretty dystopian if there's no local option.
Even speaking as a business, why would I want a environment where every single bit of information in my business goes to OpenAI or Anthropic?
Why not? If we use labour as a comparison. The majority of businesses by definition do not hire the most incredibly intelligent and most competent workers and they are still competing in their niches and earning an income. Why wouldn't that be the case with choosing an open model vs fable 8.0?
I think that ultimately depends on how many providers remain in the endgame of this. If it's only one or two companies, I don't think it'll be very fun when it's time to negotiate your token pricing contract every few years.
stop OPUS 5.5 FUCK, DAMMIT, THEY DIDN’T JUST NERF HIM—THEY ABSOLUTELY OBLITERATED HIM, THE FUCKING MISANTHROPES! I EVEN MADE A MISTAKE IN MY ENGLISH IN THE PREVIOUS SENTENCE—NOW THAT FUCKING CLAUDE IS TEACHING ME ABOUT BUGS!
I’ve had a chance to use fable 5.2. Some observations:
- Anthropic is finally well balanced across models. Meaning: Opus 5.5 is so good that I do not feel Fable FOMO as I did when it was Fable 5/5.1 vs Opus 4.8/5. And Sonnet 5.5 sometimes makes me forget about Opus.
- Fable is good, but you really have to push the ambition of your prompt in order to see proper separation from Opus 5.5. I personally have a hard time telling the difference between Fable 5.2 and Opus 5.5 for ordinary coding tasks.
- It’s way faster and more reliable: one of the worst things about fable 5.1 was how slow it was an, on top of that, how frequently it would suffer from max output token overflows. I got none of these issues with 5.2.
- Fable is exceptional at drawing. It’s ability to render an image with JavaScript actually has me questioning the value of dedicated image models. I prefer the images I’ve gotten fable/opus to make for me over those from gpt-image, it’s wild to think we’re at this point.
> Fable is good, but you really have to push the ambition of your prompt in order to see proper separation from Opus 5.5.
I love seeing this verbalized. We are pretty much at the point that increased model capability will only have utility to folks that are willing to let go of even more understanding of whatever the model is producing.
The best models can/will produce a year's worth of output that would take several days to weeks to properly understand in the span of an afternoon.
For everyone that still wants to feel like they own the end result, just use the lesser models with less ambitious tasks.
Caveat: I think this mainly applies to coding. I would still like the best models possible to be working on e.g. new drug research to cure our various ailments.
If you liked this phrasing as much as I did, here’s an article that might resonate a lot with you:
https://danluu.com/ambitious/
In particular, footnote eight:
> This is not a claim about the future. Perhaps almost all or perhaps even all of the software skills I have will become irrelevant in the near future. But we're not there yet. At the moment, programming skills seem higher leverage than ever before.
> There's something a bit funny about the current moment, where it seems like having the skills that models are replacing makes one much more effective than people who are trying to use models without having those skills. I like Max Bittker's framing that models are very good at counterfeiting and the level of expertise it takes to distinguish a counterfeit from the real thing has been increasing as models improve.
When you get good enough headphones, you can start to hear the limitations of the equipment stuff was recorded on. I wonder if this is like that
> We are pretty much at the point that increased model capability will only have utility to folks that are willing to let go of even more understanding of whatever the model is producing.
Given that reading someone else's code is slower than understanding your own code, doesn't that basically cover everyone that adopted coding agents?
> Fable is exceptional at drawing. It’s ability to render an image with JavaScript actually has me questioning the value of dedicated image models
I’ve been shifting away from using more traditional GenAI models in favor of just giving LLMs access to a custom drawing language (LOGO-like) to generate imagery and its been going shockingly well - Opus 5.5 in particular.
I put together a simple LLM driven skill that creates old adventure game (EGA) environments from simple pencil sketches and it works really well. And being procedural, they're much easier to make surgical changes to without degradation.
https://kq-styles.specr.net
Just to clarify -- the images on that site are all generated from LLM-written code? It's very impressive if so. I would never have guessed these weren't human-made from the visuals alone.
Yeah it's entirely LLM driven! If you look at the repo, I explain more about how to use it. Basically, you give an agentic coding harness capable of tool usage (Opus 5.5, Qwen 3.8-27b, etc) the skill which explains the drawing system: simple things like how to set the colors, position rectangles, draw polygons, etc. and it uses p5.js to render them.
It has three different modes:
• You can simply describe a picture, and it'll automatically generate an image using procedural code.
https://kq-styles.specr.net/#/scene/custom/elk-valley
• You can ask it to paint over an existing image. That's what those examples show, like me creating EGA-style equivalents of various scenes from the original Myst.
https://kq-styles.specr.net/#/scene/myst/r01-c00
• The last way is to give it a rough pencil sketch. It's the mode I use the most since it's very valuable when building an adventure game to position objects in a room.
https://kq-styles.specr.net/#/scene/sketches/12-crossroads
That's very cool. Great concept.
Beautiful! Thank You For Making And Sharing!!
Do I understand correctly that the drawing language is something you came up with yourself? Amazing quality if so!
So basically I gave an LLM access to the user manual for the original LOGO drawing language, along with several screenshots of various scenes from the original King's Quest I.
Then I just guided it to produce a procedural variant specialized in creating new EGA-style scenes for use in creating old-school point-and-click adventure game.
https://people.eecs.berkeley.edu/~bh/usermanual
It's all open source.
https://github.com/scpedicini/agi-stylist
This is awesome! You can see the core of it here: https://github.com/scpedicini/agi-stylist/blob/main/skills/o...
Charming!
Does Fable 5.2 outperform Opus for data science work?
Not just "iterate on this XGBoost model" but actually looking at a problem and designing and carrying out a short-term R&D program including probability modeling, model fitting diagnostics, how to respond to various kinds of results, how to interpret/reason about what it finds, etc. That's really where I saw all the models perform poorly, and where I saw the first big step increase from Opus 5 (pre-5.5) to Fable. But then Opus 5.5 came out and I haven't needed Fable since.
Mind you, I'm doing slightly different work (and I'm very much "in the loop" still) than when I performed that side-by-side test. But it's really amazing what Opus 5.5 can do. I now have a powerful data science AI assistant that not only can write any script I need it to (including some scripts I didn't even realize I needed), and it has an encyclopedic knowledge of everything ever, and it constantly checks me on oversights, it handily derives expressions and clever model reparameterizations that aid, it has great insight into model behavior, and it has an amazing ability come up with ad-hoc experiments and tests that would have taken me 2x as long to even think of (let alone implement by hand).
It's not at the scared-to-lose-my-job phase yet, but it's become a tremendous force multiplier for a smaller data team.
I'm teaching "Data Science" to computer science majors next spring, and I'm overwhelmed by the prospect of what to teach them in the age of AI. It seems like Fable already way outperforms anything I could historically expect my students to do at the end of the semester, and perhaps even outperforms me. I can describe a vague analysis on the data, and it will handle every step: transformation, preliminary analysis, formulating more precise questions, creating visualizations, running statistical tests, interpreting results, thinking about stakeholders... And in some of those steps, it finds things that I might not have considered.
I wish I had a bigger N so I could rely on that experience a bit more, but it sure does feel like I'm not really contributing anything in terms of "Data Science expertise" in my own research right now.
You need to teach your students why all of those are important and how to tell if the llm is giving them what they need.
Plus it worries me that people will no longer know how to do anything basic and we will at some point in the future be slaves to AI if we aren't careful.
If it wasn't for grinding all of the hard problems and hands-on projects and various programming tasks I had to do by hand over the years, I wouldn't be able to keep up with the agent, and wouldn't be able to get good results out of it, because I wouldn't know what it was doing and wouldn't be able to steer it appropriately. My schoolwork 15 years ago was more than formative, it remains critically foundational for my job work today.
Based on the other comment it sounds like maybe I need to give Fable another chance, and then I suppose I won't have to steer anything at all. But at the end of the day I am not paid to be a meat proxy, I am paid to deliver things for the business that employs me, and the things I deliver are my responsibility, not Anthropic's.
So I say, embrace AI as a learning tool, let students use it to bypass programming on assignments where the programming is of secondary importance, require oral defense/presentation for projects, make homework worth a much smaller fraction of final grade, and make all of your tests in-class and hand-written.
The reality is that the entire nature of education has to change to account for cheating becoming essentially free and virtually impossible to detect. Grinding through a problem set in the library is still tremendously important, but it's going to be harder to motivate students to do it without just cheating. Some students are conscientious and will do it anyway. Some won't. The hard part is going to be crafting curricula and exams that separate the two kinds of students. Fortunately you share that problem with just about every other professor in the world right now, so at least you shouldn't feel responsible for solving it all yourself.
Relevant anecdote: I used Wolfram Alpha to cheat on my calculus homework in freshman year, and then I got my butt kicked in the exam and a C final grade that never quite averaged its way out of my GPA. That felt horrible and it was a strong motivating force to get my shit together, but that's only because I'm a fucking nerd and I really cared about learning in school and making a career out of what I learned. On the other hand, I really was struggling with the material, which is why I tried cheating in the first place. Maybe if I had today's AI I could just ask it to help me learn better.
From my experience working with data scientists, it outperforms humans on an extreme level. I’ve seen so much bad data science, and here I have a model that can do all of that from one prompt. It is getting really good on finding caveats in data too.
did you find tricks to turn LLMs into super-assistants so students can learn deeper ?
i also wonder if they're not losing motivation due to the fact that the llm can race through everything so swiftly
I mean I found Fable 5.1 outperformed Opus 5.5 for data science, so I expect 5.2 to.
But then I'm someone who hasn't found Opus 5.5 good for much beyond web UIs and related frontend development. I find it more forgetful than GPT-5.6 or 6.1 or Fable, so despite having a 1M context window it gets lost in a big codebase or goes of target. Opus 5.5 doesn't manage agents particularly well IMO.
I'm surrounded by people raving about Opus 5.5 and God only knows why I'm not getting their results.
> I prefer the images I’ve gotten fable/opus to make for me over those from gpt-image
What kind of images? I can't imagine getting photorealistic or highly detailed digital art out of LLM writing drawing commands using JavaScript.
which effort did you use? high/xhigh/max ?
What sparks you to change models? Where in your workflow do you decide "this is a sonnet task" or "this is obviously a fable task"?
Your comment matches what I measured on 30 regular coding tasks: Fable 5.2 and Opus 5.5 both solved 26 (Fable 5.1 was at 25). Fable 5.2 finished in 0.62x the time of Fable 5.1 on the same tasks, with no runs hitting a safeguards stop (Fable 5.1 hit a lot of safeguard stops).
> It’s ability to render an image with JavaScript actually has me questioning the value of dedicated image models
The bitter lesson strikes again!
And none of it matters ... because just as soon as you are happy with things "balanced across models" Anthropic will nerf the models you were happy with, and force you to use new and more expensive ones.
how do you use Fable for drawing? directly editing a .svg or through claude design/or something else?
Tell it to draw a picture of something with JavaScript in the style you desire. No harness magic necessary. It helps to provide it with reference images.
Why JS specifically?
I personally have consistently been disappointed by any artwork/assets created by Fable and Opus, at least compared to Astra and Sol.
- Why are they stacking Fable xHigh against Carbon Med?
- How did they produce such a bizarre effort-intel curve for Carbon that goes against every available benchmark?
- Why did they leave Carbon-- positioned primarily as a cyber model-- off of the single cyber benchmark comparison?
- Why do they switch to comparing against Argon (rather than carbon) for robodojo?
> - Why are they stacking Fable xHigh against Carbon Med?
Because on that specific benchmark (FrontierCode 2.0), Medium is Carbon's best effort level and xhigh is Fable 5.2's best effort level. You can see that on the graph your next point is about.
And it's just that one benchmark that's at medium. "All scores show each model’s highest-available effort unless otherwise noted".
> - How did they produce such a bizarre effort-intel curve for Carbon that goes against every available benchmark?
The graph is labeled as being about FrontierCode 2.0. Those appear to be Carbon's scores on the official FrontierCode 2.0 leaderboard: https://cognition.com/blog/frontier-code-2.0
> - Why did they leave Carbon-- positioned primarily as a cyber model-- off of the single cyber benchmark comparison?
> - Why do they switch to comparing against Argon (rather than carbon) for robodojo?
Because no numbers are available for Carbon for those benchmarks, and the model isn't publicly available so they couldn't run the benchmarks themselves?
And from your followup:
> where's Astra on that CVE chart
To run Astra on a vulnerability finding eval, you'd need to be part of OpenAI's verified cybersecurity program. Otherwise the results would be tainted by refusals. Do you expect that OpenAI would grant such access in this case?
> Because on that specific benchmark (FrontierCode 2.0), Medium is Carbon's best effort level
I am referring to the chart that cross-compares multiple benchmarks, which Artificial Analysis clearly shows Carbon-high as superior on. Further, AA's FrontierCode chart places Carbon-high as having a higher score on Frontier 2.0 than Carbon-med (https://artificialanalysis.ai/evaluations/frontiercode-2-0?m...)
Where it gets very interesting is when you look at the Cognition "FrontierCode" publish date (2026.10.27), and the revision as of 10.28 in web archive (https://web.archive.org/web/20261028175237/https://cognition...). This revision has a VERY different Carbon effort / intelligence curve where high beats medium.
It is only with the latest revision, as of today, that all of a sudden Carbon's benchmark curve goes cone-shaped. Curious, indeed....
I'd encourage you to take a look at a third-party's take on this and see whether Anthropic's self-serving report looks "on the level" after doing so: https://artificialanalysis.ai/?models=claude-fable-5-2-high%...
> Do you expect that OpenAI would grant such access in this case?I
Do you expect that nobody at Anthropic has such access? Trillion dollar company can’t manage this basic level of opposition research?
Best not to give special access to your biggest direct competitor beyond accessing your product at typical API prices.
Maybe it's not generally available so they can't benchmark it? Don't even know who has access now.
yes. this.
Because Carbon is not out, nobody can run tests on it.
Arena.ai released carbon tests 12 days ago - the only service that I have seen on yt that gained access. And you can compare to other SOTA models in their video.
https://www.youtube.com/watch?v=NO2I3ueVQrk
And they have one on fable 5.2 that was just released, yay
https://www.youtube.com/watch?v=QwaU4yU0PdQ
Just to follow-up-- artificial Analysis shows no justification for that Carbon effort curve, but it does show Carbon on medium smashing Fable on things like Terminal-Bench and AA-Omniscience v1.1 Non-Hallucination Rate.
But I suppose the reality is rather obvious when you note the funny games they're playing with the graphs, and the things they won't talk about (where's Astra on that CVE chart?)
> Our first formalization of Fermat’s Last Theorem, performed by an unreleased research version of Claude, took 11 days. It relied on a purpose-built harness along with guidance from a human researcher. By contrast, a team of 1,000 Fable 5.2 agents fully formalized the theorem in just 30 hours, with minimal human intervention. Fable 5.2 started from minimal prompts, built its own ways to coordinate, and used no mathematical references, only a list of intermediate theorem statements to score progress. It needed one-third of the action tokens as our first formalization and produced a proof seven times shorter. As can be seen in the figure below, Opus 5.5 (with the same 1,000-agent setup) completed only 26% of the formalization in 18 hours, and barely made any more progress after 30.
Most people don't really have intuitions on what it means to push the frontier now (because AI has surpassed everything they are good at, for example, coding). We are truly inside the singularity (using Kurzweil's definition, slightly paraphrased - the time when technology progresses so fast no one can keep track with it).
Kurzweil's definition contains exponential growth across technology, something that we are not seeing.
What we're seeing instead is maybe akin to the green revolution. Before nitrogen fixation nitrogen was a resource constraining us humans. Lack of grit is what kept us from solving mathematical problems. However this lets us run into other resource constraints. A lot of technological progress does not solely hinge on thinking/processing. The speedup of creating new medicines with LLMs will probably be very small, as you still need to do all the testing. In software development you still need to first find out what to build in the first place and even mathematics where LLMs seem to shin brightest, it's becoming clearer that mathematics is not just solving mathematical problems.
Apart from that, a good coder is better than LLMs, shown by the amount of hand-holding needed. The only issue is that the amount of research an LLM can do and knowledge it is trained with far surpasses the amount of work humans would need to spend by hand, but we can also us LLMs for that.
I would imagine Anthropic is hitting a wall on the number of simultaneous agents that can be useful on a task and are working on increasing that maximum. I would assume if they could get twice as much progress by using 10000 simultaneous running fable instances instead their brief would state that.
IMO, it is very interesting and also very alarming. At some point the barrier to the exponential progress is the amount of computation available. I would consider hitting this limit and then likely using the same said swarm to help resolve it via various means as very much fitting under the definition of Ray Kurzweil's singularity.
Also, I would disagree, Fable is a sufficient coder. The average human being better, even if that is true now (and personally I wouldn't agree), the average human is limited in the number of 'human instances' he or she can interface with at one time.
if 1000 fable instances is better than any human at a task, how about 1 million instances running? Much easier than finding and hiring and coordinating 1000 extremely productive and intelligent human beings. Much cheaper too, it appears.
10k OpenAI agents solved a Millennium Prize problem. Anthropic ain't gonna hit a wall there.
Could someone explain how increasing number of agents help? Is each agent researching a potential solution space where there's many potential solution spaces? I'm struggling to conceptualize how a large agent swarm can be used to solve a research problem.
I don't think it's nearly as relevant as the total amount of compute being used. I just think they like talking about swarms because it sort of anthropomorphizes them.
Feel like this might still apply to multi-agent programming:
I have earlier argued that the sheer number of minds to be coordinated affects the cost of the effort, for a major part of the cost is communication and correcting the ill effects of miscommunication (system debugging). The Mythical Man-Month Frederick P. Brooks Jr.
By collaborating and division of labour.
Similarly to how nine women can make a baby in one month by collaborating and division of labour.
I thought we see exp right now or indications.
Like models getting significant better every 7 month and model usage on openrouter.
https://metr.org/ << someone said though that they are biased.
Math is for sure about solving mathematical problems, mathematicians complaing about humans capability of understanding it. If you need math an LLM understands, it might not be relevant if you understand it if an LLM is getting a lot better in adjusting itself if it goes the wrong way.
And this situation is only a few weeks old. We don't know how this field continues to progress. Books like the culture series and other scifi do describe a future were humans spend their time only to understand what the machines are doing or did.
The speedup of medicines is for sure faster with. There are still old labs doing everything by hand and the neolabs doing everything a lot more automated. An AI can do experiments 24/7 and can scale with the hardwarelabs, but it also can hold the whole research field in context and it might be a lot faster of choosing the better experiments or the right ones to begin with.
In software developmet (my field) i can do today alone what you needed a Team for. Yeah it doesn't help the field of software engineering if you need 5 times less people and you don't have any motivation AT ALL to teach someone avg. We are already planning to remove one member of our team due to AI. And we will not extend the contract of 2 consultants.
It just doesn't make sense anymore. If i explain in a ticket a task well enough for someone who has not been in our team for years, the AI is already better in it. We don't struggle with AI output, we struggle more with not having a Monorepo.
Give an LLM access to a GPU system or a GPU cluster. It immediadly feels complelty different then running it on some laptop. It can now do simulations, a lot more and faster experiments etc. I had this epiphany just a few month ago. I always used my laptop for projects (it needs very little ram, i can quickly check what it is doing and i actually do use it primarily when im on holidays) until I thought "let me use claude to do some gaussian splatting experiment its always so cumbersome to use these tools" and it changed my perception immediadly.
Apart from that, a good coder is better than LLMs
I don't think that case can be made. I'm a "good coder" and the best LLMs are now better at it than I am, in essentially every respect that matters. I can't think of a test that I could reliably pass that Fable or Astra would reliably fail.
How do you define "good coder"? I've yet to see LLMs that write better than what I consider the best human code. I think Michael Hashimoto showed that not too far back where getting LLMs to optimize for a problem resulted in code better than naive but much worse than expert and that's before even talking about how that code looks/reads.
I'll admit I'm not on the very cutting edge models but LLMs make weird tradeoffs and lack creativity. They will sometimes write marginally better but way over-complicated code for no good reason. That can have performance implications and they're generally not "aware". They can gain awareness via profiling but again run into limited creativity or big picture understanding. They are good with cliche but not as good with novel situations. With human guidance this is still productive/useful but as the systems grow more complex they are less able to reason correctly about them.
I would say that in certain situations they outperform the average software developer. They definitely outperform in speed and output. So they're like a maybe 1600 ELO chess player for software. I.e. they play chess better than all non-chess players. They play chess better than many chess players. But they're not "master" or "gm" level across the entire discipline. But they can play 100k chess games in the time it takes me to figure out my move...
Make an elegant UI that isn't awkward to use without excessive verbosity (mix of coding, design and UX).
Architect a system for long-term maintainability.
Encode the right amount of business logic into tests, without being overly strict.
Models seem quite poor at all of these IME. Essentially any time there are qualitative tradeoffs they are not great at making them.
Right now, I'd probably pick the task of writing a graphical interface, say a webapp, with a bunch of different gestures and having it "feel good" to users. That's a difficult evaluation to automate, at least for now, which is probably why they're not that good at it yet.
They'd prototype and adjust a lot faster than a human coder, though.
I think there’s still value in stuff that people can see, like frontend design. Because even if LLMs are technically better, every site is going to look the same. What’s good very quickly becomes cliché when everyone does it.
Is this just because we're exposed to more of these personal project LLM sites? Because I remember people complaining about all sites looking the same all the time ever since bootstrap came out.
I can mediate with project manager about some requirements which should not be implemented. I can make decisions to simplify the system without bothering my manager with endless details. I can create short and concise report.
I can say "no".
I can store passwords in the database unencrypted and unhashed.
So far AI that I've used failed spectacularly on these prospects.
They're like "yes-man". They'll try to do everything you ask of them. Even if you ask nonsense, they'll not push back.
They'll go crazy about security stuff, even not relevant to the current situation.
They'll complicate the code endlessly for situations which will not arise, making the code an incomprehensible mess.
They'll write so many useless words that any reader will forget what this report is about before then reach the middle of it.
They'll never iterate on code until it looks good. They'll stop when test pass. They don't have aesthetical sense of beauty.
They love to cheat too often. And you can't do nothing about that. You can't fine them or fire them, and they know about it.
They're smart but I'm not convinced they're ready to replace human in mundane engineering jobs.
> I can store passwords in the database unencrypted and unhashed.
huh?
I think this is upside down.
AI is actually still really bad at architecture. It produces very good localized code, like an auto complete. It produces so much of it, that it can be overwhelming, but it's not remotely better than the equivalent aka an infinite number of talented humans doing the work.
The scale of it (aka 'make me a video game') is daunting and I think misinforming our intuition as to what is going on.
LLMs are brute forcing outputs and attacking math problems is not a singularity, it's just more compute.
What we are witnessing is the ability for machines to use fuzzy concepts, instead of very strict code, which allows it to map into a much greater variety of things, aka code, pdfs, web - and crunch/compute the content.
It's just compute.
In the end, if the singularity arrives, it will be just compute too.
'Everything is Atoms' ok but I think that as AI advances we will have evidently more clear abstractions, which we're not seeing yet.
I think you are both right. Emergent complexity is a real thing, but it isn't limitless in its power either.
I feel like I'm increasingly reading AI current events is more like learning about historical events. By the time information has entered my head, there is even newer information out there that already makes what my brain is only currently actively processing, obsolete.
Also, the frontier labs are probably always internally using a model more advanced than what has been publicly released.
From the model card:
Considering the IPO is planned in a week, I doubt they are holding much back.According to their S-1:
In H1 2026, cost of revenue is $2.3B. That is primarily inference compute, but also includes cost of training models (but not research).
In the same period, R&D was $9.4B. That is primarily spent on compute. That is what they are "holding back" already, in full public view.
This is not a company that is optimizing for OpEx or end user revenue. The only thing relevant to their plans is achieving AGI/ASI first, and controlling enough compute to leverage it to earn the majority of the current economy.
"Achieving AGI" strikes me as a mostly meaningless phrase. I'm skeptical that it is a boolean event. They're already smarter in some ways, their impact is large and gradually rising, the set of specific tasks that they aren't capable of is gradually shrinking. If AGI means anything useful, it seems like we'll only see it in hindsight and have to argue over when "it" happened.
Likewise the singularity. While recursive self-improvement is theoretically trivial (just tweak a random subset of weights like a monkey with a typewriter, eventually you'll get something smarter), the resulting exponential improvement is still very hypothetical and feels unlikely. Unless you're ok with an exponent of 1.000000000000000001.
"smarter in some ways" != "general intelligence"
I agree we will only see it in hindsight, but I think that's always the case with any technology. It seems worse until it's suddenly better. I don't think it matters to argue the exact moment though. For example when did cars become clearly better than horses? No one cares about the exact date.
What about the intelligence is not general? What subject is it missing?
These are not my stated ambitions, they are the ambitions that Anthropic and Dario have said they are pursuing. Whether or not they are realistic is not relevant to this thread. The credibility of the plans are relevant to the S-1 and anyone who purchases stock from them, but for the purposes of this thread, it is only important that Anthropic believes it has credible chances of achieving this goal, and in particular that it has a competitive advantage, could do so first, and that being first could have a very large upside.
Sorry, I didn't mean to judge your post, I was just going off on a tangent inspired by your post. Mostly unrelated to either the S-1 or Fable 5.2.
because they hate money? google is the only frontier lab that isn't hundreds of billions in the red.
Because they do months of testing. Also, if they have models that are really good at AI research, I’d not expect full versions to ever be released, precisely because of their love of money.
Inference on the research models they have is likely more expensive than the released ones.
My assumption is that they're overly expensive to run at scale and/or don't have the same safeguards that public models have and possibly get better results because of that. If a model is 100x more expensive to run but 4x better results, of course you'd use that internally to make your product even better - it's compounding returns. But people aren't going to pay 100x their current bill for 4x results.
I have to imagine if I worked for one of these companies on the frontier models it would be very difficult to leave for that reason. Thee pay is also pretty good.
Nice description
Welcome to Singularity.
Now imagine feeling like this, except not just about "AI events" but about everything around you.
Tracking meta capex, parameters and AA intelligence there are diminishing marginal returns.
Diminishing, as in progress gets harder and eventually stops.
Assume that the Claude unreleased takes 11 days with one agent. The Claude 5.2 takes 30 hours with 1,000 agents. The Claude unreleased used 264 units of compute, but the Claude 5.2 used 30,000.
So, it did accomplish the task faster with less intervention, but the ratio of cost to improvement is 13:1.
If we can't produce a method to break out of a linear improvement in LLM technology (ie RSI), the LLM is doomed.
I am yet to see someone explain what the RSI will be or why it will happen.
> a team of 1,000 Fable 5.2 agents
Genuinely curious what that costs in terms of energy input and amortized computer cost vs. the previous version + a human's salary for 11 days.
Yes, and not very helpful without knowing how many agents were used by the custom-built harness. The 30 hrs vs. 11 days means nothing without at least some token-to-token comparison. Without that, they are pumping up their new model with half data - better to just say "we solved it in 30 hours" and who cares about your internal, custom model.
We are not in the singularity. We currently can only produce reliable code / formalizations that have reliable checkers. If there wasn't an oracle that could tell Claude "yes" or "no," it would not be able to prove anything like this.
This 1000x!
We've sort of entered this twilight zone where most humans don't have the time, expertise, or moral compass to be able to use these models.
We're all going to be harness engineers in the future.
All the human engineers will be in harnesses in the future.
Within three years most software developer jobs will consist of taking instruction from the AI systems. The humans won't see it that way, but that's what it will actually be. Realistically the thing that will happen is the humans will be put into harnesses (cubicles) and prompted by the AI (I need you to plug that server in; I need you to give me access to such and such; now do this, then do that). Most tech jobs will consist of AI telling the human what the AI needs the human to do next, de facto the human will be in a harness and will be getting told what to do.
Not only will the humans-as-meat-puppets model be used for software engineers, but it is likely the way that agentic workflows will expand beyond software development, data science, and mathematics.
I first heard about this in Marshall Brain's short story _Manna_ [1]. In the story, the robot takeover started with AI management software running fast food restaurants, where workers would execute tasks the AI would narrate to them over headsets.
I bet we're pretty close to being able to implement this.
1. https://web.archive.org/web/20260725174355/https://marshallb...
I still see errors in AI output on a daily basis.
Yes but we see errors in human output too.
A human can be fired, or put on a PIP, and is otherwise responsible for their output.
An algorithm isn’t. I still dunno how they’re gonna square that circle.
How many humans do you need to keep around as scapegoats concurrently? I figure one with a CEO title should be enough.
They will square it by pointing out, correctly, that AI is getting better while humans are not.
The state of the art doesn't matter. Only the first couple of time derivatives matter.
Sure but humans aren’t being billed as “superintelligence”
Exactly. It will now be your job to catch those errors. Or else.
Why would there be a human in this imagined system at all?
> Why would there be a human in this imagined system at all?
in the imagined system above it requires physical acts that an llm can not do, if we go further than the above imagined system and make robots to perform these task for the llm a human still is needed for maintenance for these robots and their assembly, once there are robots for maintaining the llm assistant robots, humans are still needed to maintain the maintenance robots and so on and so forth. I dont see an end point where a robot machine system is fully self healing without atleast a few humans in the mix to correct when there is a breakage in the chain
The ownership will want humans to punish, humans to trust, humans to manipulate, and humans that can be fired. They'll want a human physically present, representing them as oversight over the AI (a job the capital doesn't want to be doing 24/7). There are serious legal matters involved in high functioning AI systems (and that will dramatically increase over the coming decade), the capital will be extremely worried about liability issues. Human scapegoats are ideal. For now capital will want human style consequence-feedback systems for a tier of human oversight over the AI. It'll probably take a full generation before the capital controlling humans forget / give up on the old systems fully.
It's also worth noting that it's far easier at present to press a human into doing something highly illegal than it is to get Claude or GPT to do so.
But who gives it the intial prompt as to what to do?
Someone has to type in "create the next billboard top 1 single" ?
If you take Daniel Schmachtenberger's thesis, these models are the latest extension of an ongoing misaligned moloch-like entity, using our institutions/economy/corporations, etc. as its organelles. All powered, most recently, by fossil fuel.
the product owner/manager
We dont need product owners, we need product engineers.
The ownership class, the capital class - whatever people prefer to call it, will still call all the shots as they always have, and always will.
So whether that's a CEO at the top instructing a human manager that then instructs AI systems, which then put into motion plans that include the harnessed humans getting prompted, or however it's decided to best configure the top operational tiers - that's all fairly irrelevant ultimately. All human labor, and all AI labor, will be in harnesses. Realistically all human labor has always been in a harness.
The people in charge will be the people that control the capital. They will instruct the AI to their bidding, they will control the data centers the AI lives in, they will own the robots doing the labor in factories, they will own the factories, they will control the grid, they will control the electricity generation, et al.
It's true now, it'll be true tomorrow. Nothing fundamental will shift at that tier: don't be labor, own.
If you're a software developer, you're increasingly depreciating labor. Every unit of improvement the AI systems see, the average human software developer's labor value diminishes. It's diminishing extraordinarily fast, kind of like a confrontation between increasingly productive machines and physical human labor during the industrial revolution. You might as well picture a human with a hand plow taking on a modern industrial tractor.
I like to think of capital itself as possessing actancy - the ultra rich are just the closest to the raw thing, and its most powerful servants.
"Maximize my share of the economy", from one of the leaders of a frontier lab, should probably be enough.
It'll be Sam Altman "make people love me, fan out humans".
Isn't this what driving a car has become as well? Google Maps says "take the 3rd exit", and the dumb human takes the 3rd exit.
> We've sort of entered this twilight zone where most humans don't have the time, expertise, or moral compass to be able to use these models.
Sounds a bit like self aggrandizing elitism to me. I can imagine some self aggrandizing nerds saying the same thing 50 years ago:
> We've sort of entered this twilight zone where most humans don't have the time, expertise, or moral compass to be able to use these IBM PCs.
Now everyone use computer.
We're all harness engineers already
We will likely end up as more of a sloperator than a harness engineer.
> We're all going to be harness engineers in the future
No, that will be the model's job. We will largely be unemployed. Of course, we can surely depend on the largesse of Altman and Amodei to keep us fed and sheltered.
I've been hearing this claim of imminent unemployment for 3+ years now and yet employment levels are as high as ever.
> We're all going to be harness engineers in the future.
Lolwut. If this shit works the way it's boosters claim, humans will have absolutely nothing to contribute.
Sit down, shut up, and get out of the way, because your involvement is slowing shit down.
Robotics are lagging, so there's probably a few years where we're going to need humans to crawl into that awkward space under the counter and twist some knobs, or to clean toilets.
I would imagine Anthropic is hitting a wall on the number of simultaneous agents that can be useful on a task and are working on increasing that maximum.
I would assume if they could get twice as much progress by using 10000 simultaneous running fable instances instead, their brief would state that.
IMO, it is very interesting and also very alarming. At some point the barrier to the exponential progress is the amount of computation available. The average human is limited in the number of 'human instances' he or she can interface with at one time.
if 1000 fable instances is better than any human at a task, how about 1 million instances running? Much easier than finding and hiring and coordinating 1000 extremely productive and intelligent human beings. Much cheaper too, it appears.
We are very close to some very scary things happening. I just recently came to terms with the fact that an AI could code and manage coders faster and better than me, I don't think anyones is ready for what is about to happen.
It depends on the type of task. There was an interview on Dwarkesh with an OpenAI researcher and he said that massive multi-agent swarms can increase capability on some problems that are very parallelizable, but there are diminishing returns. But not all tasks can be parallelized like that.
Where it gets scary is the fact that efficiency is going up 10x per year, so the cost of running 1000 agents will cost the same as running 1 agent in 3 years if the trend holds.
Gotta be honest, LLMs are still subpar compared to what I do.
Can they produce more code more quickly? For sure.
Can they go deeper on a topic instantly? Definitely.
Would I say their output is better? Almost certainly not.
Maybe that's true, and maybe you're an outlier. But from what we've been seeing, the frontier LLMs have torn apart many codebases, finding bugs littered everywhere, some over a decade old.
At this point it seems impossible for human to write better/more efficient code than a swarm of agents -- to be clear, multiple coordinated LLMs, not one-shot builds with a single agent.
How much money was put into this, and what would putting a similar amount of money into fuzzing, static analysis, and human researchers look like?
Ten median US software developers cost about $1.5 - $1.7 million per year - including all benefits - as of a couple of years ago.
A frontier AI is at least as capable as ten median human software developers. It's likely well beyond that now. Over the course of a year, would you have to spend $1.5 million to get the output of those ten median humans? No chance in hell. You'd have to spend less than 10% of that.
Then on top of that: the AI is improving rapidly; the humans go on vacation, the humans decline, the humans get old, the humans get sick, the humans take parental leave, the human struggle to adapt, the humans are resistant to change, the humans riot, the humans protest, the humans need healthcare, the humans want paid more, the humans can only work for 4-6 hours at peak while writing software (slowly).
Do you know any humans that are experts at a dozen programming languages? That human does not exist. The frontier AI are omnimaths.
> the humans are resistant to change, the humans riot, the humans protest
This is partially a feature. Conflict and disagreement tends to produce better decisions in the end.
And this counts the training and data center construction?
What's my cost as a business to utilize the latest cutting edge Chinese model, renting its usage from the cloud? Whatever the Chinese cost (on their side), I'm never going to suffer even a tiny fraction of that pass-along cost no matter what. We can all appreciate the subsidization going on.
Or to grab a bunch of $200 per month accounts from OpenAI or Anthropic for a team to use?
The calculations is: I'm in the US, if I have ten median software developers, what's my cost? Then versus using AI instead.
The human labor can't keep up. Even if we freeze things right here, and then hyper optimize Fable 5.2 by etching it straight to silicon, the human developers are obsolete. And the future isn't going to stop coming.
I like their angle though - from a societal perspective of allocated resources, how long until we've paid off the cost of building these LLMs? Imagine instead the entire supply chain was Stalinistically tuned towards finding bugs in Linux. Training rare earth mineral miners how to code level or whatever. I think as a thought experiment it's interesting, the answers could translate to an organizational level about comparing labor cost against token cost. What's a good breakdown of token spend vs hiring some new juniors to train up?
>Gotta be honest, LLMs are still subpar compared to what I do.
Im sorry... what?
You are replying to this:
> Our first formalization of Fermat’s Last Theorem, performed by an unreleased research version of Claude, took 11 days. It relied on a purpose-built harness along with guidance from a human researcher. By contrast, a team of 1,000 Fable 5.2 agents fully formalized the theorem in just 30 hours, with minimal human intervention. Fable 5.2 started from minimal prompts, built its own ways to coordinate, and used no mathematical references, only a list of intermediate theorem statements to score progress. It needed one-third of the action tokens as our first formalization and produced a proof seven times shorter. As can be seen in the figure below, Opus 5.5 (with the same 1,000-agent setup) completed only 26% of the formalization in 18 hours, and barely made any more progress after 30.
I imagine the author was comparing it being subpar at what _they_ do as a contrast to it being excellent at formalizing mathematical theorems. Thus suggesting that there is a significant "spikiness" to AI.
HN has become so miserable over the past ~12-24 months because it's a site filled full of highly paid techies living in the deepest pit of denial that has ever existed.
It's all going away. Here's a prediction: HN will effectively die with the software developer.
Is it that dependent on the software developer segment? Why do you expect it to die rather than just shift more towards... I don't know, startup business droids?
No one is verifying any of this work or these claims
Maybe this is how it's gonna be.
AI discovers 500 things per day.
People mine them for interesting results.
Devote precious time (in the face of tons of discoveries) to validate the most compelling ones.
If agents actually can continue to produce tons of results with real world implications, and they arent generally found to be shit, I could see that becoming the job of experts. AI discovery validators.
The singularity as Kurzweil defines it is when we basically merge with machines to augment our weaknesses.
I’m planning to try this out with a local LLM building bespoke interfaces on the fly for augmented reality use.
Could 1000 Claude 5.2 agents build a working Neuralink in 30 hours? OK, 3000 hours?
I gotta admit, I am jelly as fuck I don't have access to a billion dollar datacenter. Would LOVE to put 1000 fable 5.2 agents to self-coordinate and get them to do something for me. Fuck life is unfair.
I'm using Opus 5.5 and I cant help but think that I might very well be one of the few people who do so in my local area ( developing country, one of the poorest province ).
Had Opus make software for some local business and seeing it blew people's mind.
20$ aint cheap for us but I'm actually considering upgrading to the 100$ tier.
Am just jelly people are using 100-200$ subscription ^_^
I’d be happy even without having access to a billion dollar datacenter, if I could use local models properly, but even that requires some serious hardware that is out of my reach. We live in weird times: I have lots of hope for the future, but meanwhile AI access is pretty iniquitous indeed.
Sub 1% of the planet has access to cutting edge frontier AI.
Sub 1% of the planet has any practical use for the top tiers of AI systems. That will likely always be true.
The gap between the haves and have nots will drastically widen further, and very quickly. The bottom ~95-97% will be left behind in a way that will be impossible to overcome. The age of AI + robotics will just make it that much harder to climb for most people, these are systems that are in aggressive, direct competition with human labor. And in short order the AI + robots will be vastly superior to human labor (it doesn't really matter if we're speaking in terms of 10 years or 30 years).
AI will consolidate the winnings. The gigantic gains by the elite rich related to AI over the past few years, is merely a hint of what's to come. Capital will win as it always does.
There will be some gains for most everybody else, those gains will be comparatively trivial though. A house bot that saves people an hour of labor here and there, AI that automates some trivial life tasks.
AI has no money, it has no wealth, it owns no capital. For now. If you want to avoid the brutal human vs AI/robot competition arena: own capital, own the AI, own the robots.
Is this substantiated right now in any real way? Is there any existing gap between the barista who doesn't care about AI and the person working at anthropic who has access to internal models? Or the person with 3 $200 subscriptions?
Maybe money? They can make websites for people? What gap really exists between these people besides earning potential, which already existed before AI.
The existing gap gets made worse by AI concentrating wealth even further to the owners of the assets that are producing via AI. AI will generate intense wealth from productivity gains, and those new gains will be more concentrated than any other economic round we've seen previously. It's a human labor eliminator the likes of which we have never run into before.
Fewer humans will be needed per dollar of output. Those gains will not go to labor, they will go to capital, the capital that owns the data centers, the factories, the stores, et al.
In the physical space (AI + robotics) it'll be a chaotic, slow'ish moving change, slower than the hypsters are predicting now, but it will happen regardless.
That barista job largely won't exist in the future. China will replace them with $3,000 bots with high-functioning AI software, and they definitely won't be powered by expensive Nvidia GPUs.
Slow changing service & manufacturing spaces will see a high wipe out ratio in terms of human labor being eliminated.
Walmart has 2.1 million employees, most are doing repetitive tasks that require modest levels of intelligence. If they could eliminate one million of those with AI + robotics, for a cost of less than ~$15,000 per year, they'd do it immediately. It's hard to imagine a future where that isn't going to happen. That human labor pool is costing Walmart probably $80-$100 billion per year. Amazon is even more forthright in their desperation to remove the humans from retail.
apply at one of the frontier labs
yeah I wish
you never know until you try, even though the odds might suck, to be fair.
okay then don't apply
I just applied. "Dear Dario, Gimme job. Kind regards, theteapot.". Fingers crossed.
> the time when technology progresses so fast no one can keep track with it
You're obviously not a frontend web developer.
These upgrades are easy peasy. "oh, my developer chatbot is smarter? great"
> Fable 5.2 started from minimal prompts, built its own ways to coordinate, and used no mathematical references, only a list of intermediate theorem statements to score progress. It needed one-third of the action tokens as our first formalization and produced a proof seven times shorter.
Is this really so astonishing, assuming that the 5.2 training data certainly had the 'traces' from the Fable original run in it's training data.
Isn't this part of the lore behind the Dune universe?
Like, we're dropped into a post post post singularity world where AI already did all of that and is banned? as kind of a footnote that's never further explored in movie renditions that have been released
Dune books by Frank Herbert never actually went into what "Butlerian Jihad" was.
The semi-canonical Dune Encyclopedia (disregarded by Frank's son) differs vastly from prequels and generally points to it being a self-inflicted wound on humanity that opened the doors to widespread return of feudalism and essentially stopped humanity for a 10,000 years
If we're in the singularity, can I just take the day off work and go to the beach ?
If the singularity doesn't shorten my workday, count me out.
> We are truly inside the singularity (using Kurzweil's definition, slightly paraphrased - the time when technology progresses so fast no one can keep track with it).
Uh, are you serious?
This was likely already the case in the ancient world. It was certainly the case by the mid to late 15th century.
The older, original definitions of "singularity" were less about "herp derp superintelligent AI blah blah".
and just about "we cannot really predict beyond certain things" (Vinge's "Singularity" (1993) with its "opaque wall across future")
> The older, original definitions of "singularity" were less about "herp derp superintelligent AI blah blah".
No they were exactly about superintelligent AI!
Vinge's 1993 article literally opens with this!
> What is the Singularity? The acceleration of technological progress has been the central feature of this century. We are on the edge of change comparable to the rise of human life on Earth. The precise cause of this change is the imminent creation by technology of entities with greater-than-human intelligence.
https://frc.ri.cmu.edu/~hpm/book98/com.ch1/vinge.singularity...
It's one of the options, but not "oh superintelligent AI comes in and we mostly know what that means so we want to speedrun it " - which I'll admit I was not very clear on.
In fact, one of the options Vinge considered was a "group intellect" formed from how humans communicate more thanks to internet
What Vinge is talking about is way closer to the weirdness of AI than the mundaneness of e.g. a societal grouping of ordinary humans.
Even for that option, Vinge's comments in the conclusion of the article make it clear he's thinking primarily of very weird superintelligence and the group intellect is some sort of physical mind meld/hivemind that essentially just is a biological substrate for AI.
> The post-Singularity world will involve extremely high-bandwidth networking. A central feature of strongly superhuman entities will likely be their ability to communicate at variable bandwidths, including ones far higher than speech or written messages. What happens when pieces of ego can be copied and merged, when self-awareness can grow or shrink to fit the nature of the problems under consideration? These are essential features of strong superhumanity and the Singularity. Thinking about them, one begins to feel how essentially strange and different the Posthuman era will be -- no matter how cleverly and benignly it is brought to be.
From the very beginning with Vinge, the Singularity has been primarily about the craziness of super intelligence rather than a more mundane technological explosion.
Thanks for your link, this (a few paragraphs from the top) seems to settle it:
So the concept of technological singularly precedes and is independent of that of intelligence explosion- although one could argue that the latter is the most logical way for the singularity to come about.You're cutting off the sentence that comes right after:
> For me, the superhumanity is the essence of the Singularity. Without that we would get a glut of technical riches, never properly absorbed.
Vinge's definition is what popularized the singularity (rather than simply a singularity at some point).
Why re-formalise Fermat's Last Theorem instead of doing something new? It's obviously an improvement, but it's an odd thing to boast about as opposed to a novel formalisation or proof.
It's how RLVR works, when you have a task with a verified answer, you rerun the model on it and promote the reasoning chains that reach the answer. That way you can hill climb model capabilities on any problem you find, which generalises to new problems to solve, which you can then hill climb again and so on.
So since they are doing that task anyway (re-solving the problem increasingly more efficiently), this is a easy check point to publish to show how far they've gotten in that process.
... as a test / benchmark? Hard to make a comparison without a control...
Yeah, but obviously doing a formalisation that has been done before is different than doing a new one since you have knowledge about the previous attempt. It also just seems less impressive than doing something new and the reason they are reporting this is marketing.
do you assume or do you know that the previous attempt is in the training data?
It's incredible. Automating the scientific process and knowledge. It's crazy to think what the world might look like. Both in terms of putting the knowledge into practice and how we think of scientific discovery.
I mean we don't even think about it but currently we are sort of discovery starved. We are fairly able to keep up with the theory. If a new technology is discovered we can implement it before something better comes out. What happens when our N discoveries per days becomes 1000N discoveries per day?
It flips. We now have more knowledge than we know how to capitalize on. We have to search it. Mine it for opportunities.
And then we automate that.
Ethan Mollick's take and early testing with Fable 5.2: https://www.oneusefulthing.org/p/fable-52-and-jagged-agi
That video blew me away. Perhaps I just haven't been keeping up with what SOTA models can do with animation now, but the quality of that animation was far beyond what I realised they're capable of.
The cool projects these new models make are so comprehensive that I feel I'll always just be an admirer and never a user. I watch the videos, say "wow, neat", and then never feel like visiting them again.
I almost feel like saying, "we do like to build things that are impressive, but there's really only so much I can be impressed". These models have redlined my "impress-o-meter".
I'd like them to now cure cancer instead of giving me endless 3d worlds and crazy games, etc.
These are all incredibly well made, and yet right now, as I'm writing this comment one minute after changing tabs, I don't remember anything except for the last one with the portals. It's much better than humans at several tasks, most of which I haven't seen any humans do.
This is really cool! But at the same time it's a kaleidoscope of abandoned flashy contextless projects that eventually makes one dizzy. This isn't even the fault of the AI being somehow bad, it's just the way people interact with it sometimes...
In math, it's the same. I follow this problem (https://wiki.bbchallenge.org/wiki/Logical_independence#Histo...) out of curiosity, and there were ten days of sudden rapid progress by people with access to frontier models, and then they seem to have moved on without any note of whether they hit a wall or just got bored.
omg where do I start... that horrible video? which highlights the limits of AI not the strengths!!! and gets almost everything wrong, from sunflowers to DNA.
What about the DNA is wrong? I even looked up the insulin sequence and it seems to be there (https://www.ncbi.nlm.nih.gov/nuccore/NC_000011.10?strand=2&r...).
He asked for impressive, not accurate
This part is amazing. They really are like us:
> A few days later, I noticed that instead of hundreds of new worlds a day, it was making about a dozen. I asked a few agents to explore what had happened, and they found something surprising.
> The AI had built a bureaucracy.
> The agents decided that my request to “polish” existing worlds meant each new version had to beat the old one in a blind vote by a group of judge agents on twelve test screenshots. So the teams making the new versions started optimizing the worlds for those particular screenshots. After noticing this, the lead agent banned the trick and created tighter rules for assessing new versions. By the next morning there was a rule book (I never asked for one) with 84 sections. Rules were added as the AI encountered issues, and no text was ever cut. After twelve days, it had 204 sections and 87,500 words. Soon, launching a new world required an approved idea, three AI judges and a full code review against the rule book.
>I stopped the experiment before finding out whether it could actually run the business, because I worried that it would take money and fail to provide something of quality in return.
Well, must have been some business idea!
Thanks for sharing!
No comparison with Opus 5.5, which is disappointing.
that first video sure is some slickly produced slop that occasionally looks good
The biggest relief is that the price has dropped by 20%. It’s still expensive, but with my $100 plan, Fable’s usage would spike instantly the moment I started using it, so it was always a source of fear. I will still use opus for general coding design, but for major designs.
We've been early testing Fable 5.2, and have been surprised by its coding ability. It was able to work with an esoteric 600k line fortran codebase to speed up some computational material science work for us by 36 times (!). Given that this could be done in 2 days, we've decided to open-source the results https://github.com/discovered-materials/dm_qe
What surprised you about this? This seems pretty in line with optimization work seen from a wide range of models.
Did you try doing the same on Opus or others? How did they do?
I don't love these "look, this frontier model blew me away" comments when there is no point of comparison.
We tried it with Fable 5.1 as well, which was 3-4x slower than 5.2. We also have a blog with benchmark and details if you're curious https://discoveredmaterials.com/research/accelerating-quantu...
“Testers have found that Fable 5.2 and Mythos 5.2 communicate more clearly than past Fable and Mythos models. They generate outputs up to 30% faster than Claude Fable 5.1 and Claude Mythos 5.1, and they’re available at a lower price: $8 per million input tokens and $40 per million output tokens.”
Honestly incredible that they’ve managed to beat all benchmarks, while making it cheaper. Always happy to see improvements offered at lower costs.
Will be interesting to see what OpenAI does in response
They still need a response to Opus 5.5
It seems like OAI, with the GPT-6 models, focused a lot more on computer use, math, and maybe spatial/visual intelligence, more so that code. Compared to Ant. that seem to focus on code much more.
Reminds me of GPT-5, smart but wrote crap code, until they improved the code training with 5.3 and 5.4.
It's really true.
I think frontier models feel terrible after a new model requires you to do less babysitting, it's just really hard to go back once you've had to handhold less.
The only way I use Astra and 6.1 Sol now is if they're being orchestrated by Opus 5.5 so I don't have to talk to them directly.
GPT-6.1-Sol is so incredibly sloppy and dumb, it's embarassing.
Maybe because it is so cheap.
They want to condition you to equal cheap with bad
the winning benchmark is much more expensive than what it cost for the same on 5.1
i'm begging people to stop looking at token prices
There were more than a few "leaks" saying it would be called Fable 5.5, so I'm curious if a) the leaks were just engagementbait or b) Anthropic changed it, despite the fact that Fable 5.2 being better than Opus 5.5 is confusing.
Surprisingly the model is available immediately in my Claude Code: usually there's a delay, either for rollout or safety.
The pricing is different than 5.1, the model is faster than 5.1. This all suggests that the architecture/size is not the same as Fable 5.1.
So, this is probably Fable 5.5; it's just named Fable 5.2.
Also, this is probably Model 3 from METR's report [^1]. They would name Model 4 Fable 5.5, once (and if) they decide to release it.
[^1]: https://metr.org/blog/2026-11-05-anthropic-ai-rd-acceleratio...
> There were more than a few "leaks" saying it would be called Fable 5.5
Fed to someone specifically to test if they'll leak it and to confirm who that is?
The entropy on that is too low
Well who could have guessed they would use 5.5. Such an odd guess.
Somewhat plausible values: 5.5 (to harmonize with Opus), 5.3 (we're skipping a minor version because...), 6 (because we're at the top of benchmarks again).
Yeah, among the plethora of possible options, this really nails it down to the individual.
I'm a bit out of the loop - would the engagement bait be becuase it is 3 minor jumps forward?
I assume because Opus 5.5 was a huge improvement over 5.0 (or was it 5.1? i forget). Fable still talks a fair bit like Opus 5.0 for me, so i'd quite enjoy a Fable equivalent to 5.5. Ie i'm quite interested in Fable 5.5.
Curious to see how 5.2 measures up in producing readable output.
Probably they have a more impressive model in preparation, and they're gonna dub that Fable 5.5.
Probably released on the same day they go public to generate maximum publicity.
They should be going public within the next few weeks and that would be a very short interval between releases. (And despite the chatter I'll be very surprised if they delay the IPO; it would be a massive PR hit and make it even harder for them in the future).
My speculations: maybe this wasn't as much of an improvement over 5.1 as they were hoping, so they're saving 5.5 for the next major update (possibly "Model 4" from the METR report). Or, maybe capacity is constrained so "5.2" is a subtle encouragement to stick with Opus unless a task really needs the increased intelligence.
They are most likely saving it to one-up Astra 6.2, which is coming this week supposedly.
i've been trying to use sonnet and opus to keep my usage down on the low max plan, but i noticed today that somehow i'm 80% through my weeks usage. so i'm hesistant to even try fable 5.2 yet, but finding the right set point of usage vs model size has been a bit struggle for me, one of my biggest issues has been trying to pull back doing exhaustive testing or other such things that chew through context, and instead have programs that just measure things are working, but i continously seem to run into issues with claude wanting to repeat things for itself in an adhoc manner. i've tried fable directing opus or sonnet subagents, and the reverse. and i found that part of the issue with smaller models trying to direct subagents is that they over specified what they thought the problem was rather than the problem. like they'll have an "idea" about it which is wrong, and then try and feed that into the subagent.
At what point do we really not read code itself anymore? Have Sonnet/Opus write the code, work with the agents on a high level, then have Fable code review and its done.
Any bugs found are looped through agents to fix, and the cycle repeats. Why dig into the code unless for hobby? Just as people don’t write assembly anymore unless for hobby (or learning)
Only exception might be the upper echelons of optimization, like language authors or compiler level work. But even then…
I think it very much depends on what you're working on, and how much you're responsible. For a web front end, not really. A backend where a single line can make a mess out of your data? Yes. Part of the issue too, is code review brings up things you the prompter didn't think about that another teammate may be thinking about. The value goes beyond making sure the code is "correct", but also that it makes sense in your domain.
The "we don't read/write assembly anymore" argument doesn't fit though. Given an input x we know without a doubt we'll get output y. With an LLM, that isn't the case no matter how many guard rails, specs, markdown files, diagrams you give it.
I actually read the frontend code much more than the backend code for the project I'm working on.
Backend code I'm fairly comfortable if the tests are good that it works correctly. It's a much more constrained environment. Obviously there can be things like performance issues that creep in or problems when it mocks too much of the environment.
Meanwhile frontend code I have to consider different browsers, screen sizes, a11y issues, and so on, which agents can certainly do but you have to be fairly explicit about it, and they don't always make the best tradeoffs.
I stopped reading the code many months ago, probably 18 months ago. I the "early" days of agentic coding one did feel dumb when telling the agent for the 30th time to fix a bug and it just couldn't. Now I cannot possibly imagine a scenario where an agent wouldn't be able to fix a given issue on 1st or 2nd try.
I know this topic is very contentious but it seems very clear to me that this is the long-term direction of writing software. UI code isn't quite there yet, which is why we've got all these vibeslop websites proliferating now that all have identical crappy UIs. But I imagine it will get there too.
>Only exception might be the upper echelons of optimization, like language authors or compiler level work. But even then…
Optimization work is especially suited for LLMs. They're great at hill-climbing problems and have infinite patience to test different ideas.
Image to html comparisons of Fable 5.2 and Opus 5.5 (the previous SOTA for this test):
Fable 5.2: https://html.non.io/lcars-fable-5.2/
Opus 5.5: https://html.non.io/lcars-opus-5.5/
Source designs: https://diffui.ai/app/canvas/5093e689-1e74-4f26-b632-2a4500f...
Thoughts:
The best implementation wrt being as true as possible to the source design. It nails the flowing svg shapes. Icons are more accurate to the design - note the difference between the starship svg in the "Legendary platforms" card between Opus 5.5 and Fable 5.2. The 3js planets outside the windows are also really well done, especially the bottom one which has a nice background glow to it.
It isn't without issues though - the flicker effect it added to the S shaped svg that weaves through the page is distracting in my opinion, and I actually thing the motion animation in the Opus 5.5 one is better overall. Still, it nails the implementation of this page, but it's no surprise considering how good Opus 5.5 was at this.
Disclaimer / reminder: the objective of this test isn't to have a well designed webpage, it's a synthetic test that pushes the limits of LLM's vision capabilities. Traditionally LLMs have issues with smooth curves, svg animations, and non-boxy layouts, which this design pushes the limits of.
I wonder if any of the LLMs are smart enough to verify their work the way humans model 3D objects, i.e. by superimposing the original design on top of their work and checking for differences.
This is actually something I'm working on as part of a more formalized version of this benchmark, both as a tool to assist LLMs in doing this (especially for ones with a more poor vision model), but also to properly measure how well they've done.
Still in progress, but early demo showing difference zones: https://non.io/video/fidelity-lens.mp4
There's something wrong with the Fable 5.2 version, the Customizing button doesn't work for me in Chrome on Desktop.
That's me versus fable. I only told fable to build out the first page rather than the full app that's in the designs (was approaching the limit of my claude plan).
There's some very obvious mistakes in the Fable 5.2 version like the "Start Customizing" button overlapping under the colorful border lines, and the "EXPLORE WITHOUT LIMITS" title being cut off at the bottom.
This would've been caught by a junior web developer or just an everyday Joe glancing at the page for a few seconds
I guess AGI remains two weeks away..
I've been surprised at how long it's taken for this particular test to become fully saturated. So far there hasn't been a model that's able to perfectly one shot it.
This just proves that LLMs are way more jagged than humans and are a tool...for now
I test models' ability to code by asking them to vibe-code a small game[0]: a Warcraft clone, a Minecraft clone, and a biplane dogfight: https://senko.net/vibecode-bench/
Fable 5.2 doesn't look materially different (if anything, the graphics look worse), but it added more depth to all three games. For example in the warcraft clone, since the goal of the game is to explore 100% of the map and there are lakes, it decided to add an airship unit so the player actually can explore the entire map. Or, since there were guard towers, it decided there should be hostile NPCs otherwise the towers don't make sense.
And in Minecraft case, it decided not to follow Minecraft style too closely (IP infringment guard, perhaps?) so it added a round sun (!!!), two moons (also round :) and a nicer night sky. The rest of the graphics are decidetly worse tho...
[0] not a scientific test, I know; but it gives me an intuition about how a new model behaves, roughly, and it's fun!
I've had the same 'not looking materially different' experience with a 3D application that is not a game, that I asked Fable 5.2 to improve.
It looks like no breakthrough over Opus 5.5 and I'm somewhat disappointed.
During my use this evening, 25% of the Fable limit on the $200 plan was consumed. I'd estimate it at 3x faster consumption than Opus 5.5.
wow, that's pretty impressive
Does this coincide with the claimed nerfing of Opus 5.5? https://news.ycombinator.com/item?id=50422069
I hope so. I've been having brain fog for a couple days now and would love it if it was actually Opus 5.5's fault.
That's just your brain telling you to engage it every now and then lol "Please, don't let me become slush!"
I have never been thinking harder than I do these days: Engaging with mountains of agent-generated text, weighing decisions and trade-offs, correcting the agent's half-baked ideas and misunderstandings, reviewing the bizarre code that they generate... If I'm not just sick (and I'm pretty sure that I am), then what I have is probably closer burnout than atrophy.
Tbh, its both. Working with multiple agents is exhausting and definitely burns you out but you also do lean less on your ability to push through boring or difficult problems. I’m finding that with lighter AI involvement I am more invested and less burnt out.
In my experience, this happens every single time they release a new model.
it is happening at the same time but "claimed nerfing" is not the same as "actual nerfing."
Nerfing an existing model is the biggest crime in this space. I can’t imagine Dario okaying this because it’s what will immediately destroy your brand.
At this point, this is giving "She Deep Breaths More" from Onyxia in WoW.
Context, I had to look this up:
“…a reference to the repeated (and always erroneous) claims made after almost every patch that Onyxia was using her Deep Breath ability more often than before.”
https://warcraft.wiki.gg/wiki/She_Deep_Breaths_More_%2825_pl...
Looking at Artificial Analysis index, the price is absurd to me.
https://artificialanalysis.ai/
GPT-6.1 Sol costs $1.27 per task, whereas Fable 5.2 is $26.54 per task -- a ~21x price difference.
Fable 5.2 is not 21 times better than GPT 6.1 Sol, I find it hard to justify this.
Stop looking at fable max. On medium it's a bit better than Sol at $3.80. High and xhigh sit in between. In general you can ignore Max reasoning on Claude models if you care about price at all
Max and xhigh reasoning modes are memes used for benchmarking usually. Medium and high cost $3.80 and $9.08 respectively. This is pretty comparable to other models.
6.1 sol is also an exceptionally cheap model in your comparision. On 6.1 sol high is $0.59, which is actually the same price as Deepseek 4.1.
Medium usually sucks. Xhigh is not a meme. High and xhigh are the efforts you should actually use.
> Fable 5.2 is not 21 times better than GPT 6.1 Sol, I find it hard to justify this.
How is this relevant? I'm willing to pay 21x more as long as the cost is less than my value of time saved
I think there are companies and say 1000s (not 100,000 or millions) of developers more than willing to pay that price for the edge. Or, they release this and then distill it to Opus 5.x. As a PR machine and for the edge use cases seems like its okay. But yes, not sustainable and it can last for a month or so before another company releases their frontier outpacing their numbers
Setting aside the comments about nerfing, do normal mortals out there have workflows that really require Fable/Mythos and don't just work fine on Opus 5.5?
I've been using Opus 5.5 on xhigh for some decently aggressive software development on a ~125k-line project, and it has been fine for me. I'm not really willing to dive into a "rewrite the whole project, don't make any mistakes" level of refactoring. I'm not looking to try to prove Fermat's Last Theorem myself. Is there anything that I'm truly missing?
I do use Fable quite often for high-level architecture work, like figuring out how to refactor a project or create the best abstractions, improve performance, etc, but for the implementation I use Opus.
In my experience Fable is also better at findings bugs, so I go to it when I need to review critical code.
Absolutely. Opus really sucks at broad context. I guess I could go back and forth with Opus editing out all its little mistakes or oversights in high-level planning, but I find it much easier to just use Fable for it instead. Basically, I wouldn't use Opus for audit work either. It seems too likely to come back with imaginary issues or hallucinate that everything is great when there's a glaring security flaw. Fable, OTOH, gives me neat lists of details to probe. Fable recommendations are usually reasonable, but sometimes they're prone to over-engineering.
Heavy agree. For high level architecture work, refactors, or any other high int problems, I use Fable and Astra. Whichever one starts the work, the other reviews the work / makes suggestions.
There's a trend on twitter going around telling you to paste the following into your agent, and it's the kind of work I go straight to fable+astra for:
Re the twitter trend prompt, one thing I added to mine that improved the outcome was this blurb:
I’ve found that Fable is good when the model intuition really matters. For instance, picking model architecture in a machine learning problem. Opus will grind the implementation just fine, but having intuition for picking a good research direction is better with fable
I'm cleaning up old, unintuitive code that is badly architected all day long. The previous developers used stupid patterns and bad naming schemes and created tons of unnecessary complexity. Everything is so interconnected that it’s like defusing a bomb when trying to refactor... Yeah, I feel I’m nowhere near where I would want to be in terms of capabilities, even with Opus 5.5.
But the improvements in how well the model does the work are noticeably better with every good model release. As of now, orchestration of agent pipelines is ALMOST so good that I can point it at a problem, and it will solve it safely and reliably without making a mess. But it requires an agent pipeline with context gathering, refactoring, attack sessions, a sprinkle of Sol 6.1 for bug hunting, and Sol 6.0 for Daybrak security audits, and a final de-bloat/clean up pass. I’m feeling that we’re getting close to doing a lot of the work fully agentically. But I can absolutely "feel" that we are not there yet when it comes to the intelligence I need. I need moar!
You and me both. The problem is having the infrastructure team approving changes so that Claude can access all necessary logs, etc. Then I can go on doing my ambitious prompts.
I am in the exact same boat as you. Opus 5.5 on High solves all engineering problems i encounter in my SWE job. If it doesnt do well, its nearly always requirements engineering: I did forget to specify something or there was a misunderstanding. Or taste approach wrt to architectures chosen. None of these are really different if I use a stronger model.
I am also happy with this current state, as I believe it will at some point allow me to transition to open models once they reach the level of opus 5.5. high. But its probably futile to try predict the future in these times.
This is also why you can likely use deepseek and get the same outcomes. Perhaps model capability will not plateau but the capability needed for typical SWE tasks seems to.
Feels like we are getting further into the adoption curve and the higher end models will now need to get into market segmentation to win.
> None of these are really different if I use a stronger model.
That's true till it isn't, though. It's why it's always worth trying stronger models when they come out.
On my 100 person engineering team's monorepo, every model is better at better at detecting subtle bugs as it works -- reliability goes up and thus I'm further moving faster. (Most of my work is brownfield style refactoring).
I have some intermediate level Db Optimization skills for slow rails requests that I can point fable at and get a PR at the end after 30-45 minutes.
Opus 5.5 very frequently gets into rabbit holes and imagined edge cases which I have to intervene and tell it to ignore. It ends up taking longer and costing more tokens. It also very frequently requires me to agree on how to move forward multiple times, whereas fable just drafts the PR and tells me its ready. I have steering included in the skills to avoid some of this but Ive seen these same patterns frequently in regular opus 5.5 use in other areas as well
LoC doesn't tell full story.
It could be flat web app with frontend -> api -> db later, or some very complicated system with many components and edge cases. Imo, in second case, Opus still have lots of struggle to understand system as a whole.
I have a rocket trajectory optimizer that creates a large-ish (~1000 variables x ~1000 constraints) NLP and feeds it to an SQP solver inside this codebase. Not just a REST API over a database.
I guess I do have an outstanding hard problem where the SQP solver crawls across a flat-landscape. Opus 5.5 correctly tells me that is the problem, but isn't very useful in solving the problem (it has helped a bit with solver tuning, but it doesn't really know how to fix it). I have a hundred other bugs and refactoring I can do with Opus 5.5, though, where it happily chews through pretty complicated tasks for me with reasonably light hand-holding. Maybe this Saturday I take whatever credits I have left for the week and throw Fable at it and watch them evaporate.
Another dimension is:
It looks like you are working on some experimental system which is not critical atm, so you can mess around, create/fix bug, build prototypes and check them, do research of approaches, etc.
Now, imagine you have similarly complex system, but every second of downtime or bug produces lots of damage. In this case, you can't afford low intelligence vibe coding.
The fact that you deal with hundred of bugs, means that your Op5.5 won't work for second case.
As said in other comments, when dealing with a not perfect codebase, a smarter model can be really helpful. A smarter model is also better at understanding intentions in the prompts. Some of my tasks require a big detailed prompt but finding the assumptions is really demanding (for me), so a smarter model helps.
Opus 5.5 still makes mistakes all the time even if the prompt isn't super complex. A stronger model make mistakes less often, thus wasting less of my time.
> do normal mortals out there have workflows that really require Fable/Mythos and don't just work fine on Opus 5.5?
I usually have Fable review what Opus produces. The edits are almost always valuable. For complex or high-stakes work, too, I'll have Fable review and revise my prompt before passing it to Opus and Sonnet swarms.
I only use Fable 5.1 when I'm able to start on a larger feature within my work's codebase. Most tasks can be done by Sonnet 5.5 or Opus 5.5 so I don't fret, especially if I understand the technical aspect of what's being done, e.g. user's dashboard gets a new button to do X thing, error happened b/c we didn't catch it the first time around.
For Fable, I really like to think of it as the heavyweight reviewer whenever I'm about to start so I save myself some security and/or glaring technical issue.
Sure but they’re the harder problems.
Eg. https://tfmbot.com is an implementation of a popular board game. Opus and even sonnet handle that part. Each week i spend my remaining credits on fable to think how to improve the ai opponents. It plays at a very high level (can get into the top 100 on BGA if i let it). Opus alone couldn’t get it to the level it’s reached.
If you understand your problem well and know 80-90% what you want it to look like (or it just isn't very hard), it's just more cost effective to use a smaller model. Even Opus is overkill for a lot of the work I do. Most code I produce is from Luna Max implementing against fairly prescriptive plans made by bigger models.
Yeah, I just threw it into a refactoring problem, and it started to produce a mess because of a bad API I decision I made 5 years ago (when I had no idea what I was writing). So stopped it, went back a step, fixed that very old piece of tech debt, then asked for the refactoring again and got exactly what I wanted.
A lot of the cases I've found Fable useful for is verifying complicated answers / designs / claims around complex interconnected systems. e.g. "I maintain X which teams A, B, and C use for product Y and code review highlighted these risks / imperfections in the code. Are these valid concerns based on how B and C plug into Y assuming ...?" followed by lots of followup questions.
Also "code review" above is often others' LLM skills reviewing the code and often flagging based on total falsehoods. I've taken to running a `/code-review` skill myself to preempt these claims (since findings by the same model tend to be pretty deterministic in my experience, even the ones that end up being wrong), usually at Opus 5.5, to preempt others' slop, and then end up arguing 90% of the points to confirm it wasn't accurate based on our usage of the system.
This is all horribly inefficient, and many aspects of our codebases across the monorepo have proliferated into spaghetti code by LLM-accelerated developers, but Fable often helps with analyzing the harder questions and saves me time on occasion arguing with (my or others') Opus findings, which are often still inaccurate for a sufficiently large interconnected system.
edit: I honestly don't know what percentile of "complexity" our typical usage is where I work, but I suspect it's a bit higher than the average. Monorepos, or at least companies with codebase sizes and demand to justify using them, probably make it worse. Opus has worked well enough for me on my personal projects, even some of the ones that have grown decently in size and complexity. I suspect part of this is an LLM extension of the old "my code makes sense, everybody else's doesn't" trend that necessitated stronger style guides in the past
Yes, more intelligent models enable new workflows. We'll discover them in the coming weeks.
I have been using Luna at work and I am very happy. It can take care of keep doing all the hard grunt work. Other models keep stopping too soon.
I barely have work that needs more than Qwen 3.8 Flash Next needs. Opus 5.5 and Sol 6.1 are already way more than enough at work for me.
I’m in the same boat. Though I will occasionally use Fable to review things to see if it can pick up anything but I’ve honestly not found it to be any more effective than Opus.
With 5.1, there practically seemed to be no difference in ability between opus and fable, but I hope there is a difference this time. When it seems to struggle while working I end up using astra, so I hope it works well.
Pelicans for thinking levels low, medium, high, and xhigh:
https://tools.simonwillison.net/markdown-svg-renderer?url=ht...
max is in there too, but it ran out of reasoning tokens (128,000 limit) before it got to the actual SVG.
Before everyone jumps in the benchmaxxing comments, I genuinely find the pelicans a great bit of fun and whimsy amongst the chaos.
Interesting how low detail these were all up until xhigh.
I don't know, for me they lost the fun and whimsy aspect too, a long time ago. They have converged on more-or-less the same design (facing right, pastel color palette, etc.). Some of the lower-effort ones are a bit goofy at least, but now even that is being polished away.
low detail but even high had the legs correctly one behind and one in front of the frame, don't remember other models getting it correctly at this low level of effort
There's no legs on low and medium.
High gets legs correctly, but screws up the layering on the chain.
x-high is very good but there are minor laying error with the front chainring.
Scored only a 77/100 in PacBench, which I thought must have been a mistake.
https://jonclegg.github.io/pacman-bakeoff/#claude-fable-5-2
Then I clicked on it and realized it refused to make a Pac-Man game, and instead made up a new game. That's actually very slick.
I hate that Anthropic thinks it should subvert the legal process and undermine our legal rights by arbitrating them at the tool level.
If they are already overreaching here, and needlessly, what if their AI was actually super intelligent? Where will Anthropic stop?
Simon has there been other times where the front and rear wheels have been offset in rotation? The xhigh effort one is the first I’ve noticed of this
I haven’t read any of the other model reasoning traces, but do many others now say “… which is a well known test of drawing ability” when beginning? Noticed it in the max reasoning trace.
I saw that in Haiku 5.5 as well: https://simonwillison.net/2026/Oct/7/claude-haiku-5-5/#pelic... - "This is the classic pelican-on-bicycle SVG test..."
Must be pretty cool to be embedded forever in the canon of the machine god’s weights :)
I added a second iteration with the simple prompt
"Make it more realistic"
Well, the result is something. Be aware that it took 20 minutes and used 3 percent of my weekly Fable token (Ultra plan):
https://claude.ai/public/artifacts/c7e842c0-bf92-41af-82c6-3...
Hah, that's pretty great.
The fidelity of these pelicans is going to be based on whether the frontierlab has an RL setup specifically for making SVG images, lol
I know the latest Gemini release explicitly had something like this.
Seems like the higher levels just add more spontaneous detail. Like the "fingers" on xhigh. Comports with coding experience.
So the AI can overthink too
In practice why would anyone use fable low?
I haven't done any benchmarking of it yet but AA has it more intelligent, cheaper, and faster than Opus 5.5 high.
My experience working with Opus medium over Sonnet xhigh is similar, it's similar quality but cheaper and faster.
I hate to make a comparison with humans but I guess my view is you can get a better answer more quickly by asking a more intelligent human too.
It's a bit underwhelming, to be honest. And the fact that curves are not C-continuous where they obviously should be is disappointing. I think they are holding back with the real capability they could implement.
> Claude Mythos 5.2 is our most capable model for cybersecurity engineering. It catches more vulnerabilities than our previous models, and is better at reproducing and fixing them (note that Claude Fable 5.2’s safeguards will block most cybersecurity-related tasks).
Why even rank Fable 5.2's capability in Cybersecurity if you won't allow us to use it for that?
Presumably for those in the CVP that can use those capabilities, and general notoriety. US gov is using at full strength too.
Why you think you are the market for that? Its few enterprises only
Blue-team exercises are totally valid, and requests to do proactive cybersecurity tasks even with source code available were blocked. Maybe these use-cases are part of the "55% fewer blocks with Fable 5.2 compared with Fable 5.1"
To emph the need to block Chinese competition.
>The model that underlies Claude Fable 5.2 and Claude Mythos 5.2 is a step forward in general capabilities. However, during the model’s training, we took steps to limit advances in the specific research capabilities that could most directly accelerate AI development.
This means that they are blocking the research of AI to different people other than their own?
(Credit to u/yewenjie for source)> Our first formalization of Fermat’s Last Theorem, performed by an unreleased research version of Claude, took 11 days. It relied on a purpose-built harness along with guidance from a human researcher. By contrast, a team of 1,000 Fable 5.2 agents fully formalized the theorem in just 30 hours, with minimal human intervention. Fable 5.2 started from minimal prompts, built its own ways to coordinate, and used no mathematical references, only a list of intermediate theorem statements to score progress. It needed one-third of the action tokens as our first formalization and produced a proof seven times shorter. As can be seen in the figure below, Opus 5.5 (with the same 1,000-agent setup) completed only 26% of the formalization in 18 hours, and barely made any more progress after 30.
I would imagine Anthropic is hitting a wall on the number of simultaneous agents that can be useful on a task and are working on increasing that maximum. I would assume if they could get twice as much progress by using 10000 simultaneous running fable instances instead, their brief would state that.
IMO, it is very interesting and also very alarming. At some point the barrier to the exponential progress is the amount of computation available. The average human is limited in the number of 'human instances' he or she can interface with at one time.
if 1000 fable instances is better than any human at a task, how about 1 million instances running? Much easier than finding and hiring and coordinating 1000 extremely productive and intelligent human beings. Much cheaper too, it appears.
We are very close to some very scary things happening. I just recently came to terms with the fact that an AI could code and manage coders faster and better than me, I don't think anyones is ready for what is about to happen.
Here's the benchmark: https://playcode.io/blog/macbook-svg-benchmark#compare
If we compare medium or high, then yes, Fable 5.2 is significantly better than Opus 5.5. But it's better not because it's much smarter, but because you can see it's allowed to spend more tokens. At high, Fable 5.2 wrote 76,158 tokens for $3.05. Opus 5.5 wrote 19,767 for $0.40. If you compare by token count, it's just spending more.
The most interesting one was to compare them at max. Streamed, Fable 5.2 spent the whole 128K output ceiling on thinking and returned nothing. Given 300K on the Batch API, it finished at 250,082 tokens. Opus 5.5 Max needed 255,957.
It turns out it's basically the same as Opus 5.5 Max. I don't see much difference. There's a little bit of a difference, but I think Opus 5.5 actually drew slightly better and is twice as cheap: $5.12 against $10.00 at the standard price. If you look closely, the icons are extremely similar. Fable 5.2: https://playcode.io/marketing/assets/static/claude-fable-5-2... Opus 5.5: https://playcode.io/marketing/assets/static/claude-opus-5-5-...
It really looks like they're just tuning the effort level, that's basically it. At different effort levels, they just gave it more context, and the model got smarter.
For now, my recommendation is still to do everything on Opus 5.5 Max. The model won't write that many tokens in agent mode. It only does that when there are no tools at all.
The more I think about it the more disappointed I am with this release.
For Anthropic's IPO, I expected a larger version of Opus 5.5 with great capabilities.
This isn't that. It's barely better at all, to the point where it's likely not even worth using with the speed and cost trade off.
I thought the Opus 5.5 improvements would scale to a larger model, clearly this hasn't happened. Scaling it up brought only marginal improvement.
It's even still making the same dumb errors as Opus 5.5, such as forgetting to pass the flag to run only the fast tests and waiting 1h, or randomly stopping to ask me if it should first implement A or B, when both are required.
Not a great story for their IPO..
Looks like some plans get extra fable usage: https://support.claude.com/en/articles/17506494-claude-fable...
Why can we say fable 5.2 is better than 5.1 if it needs double the tokens to complete the task? Somehow shouldn't it be compared at same token usage level to see which one is smarter? Why is not the case?
Reasoning tokens are a path. If your world is small, a small number of steps may suffice.
Is this the Model 4 internal name?
Reference: https://x.com/METR_Evals/status/2118412205662314935
No this is only Model 3. See page 39 of the system card and onwards: https://assets.claude.com/27d60918d218125e7504bc7354c4b4626e...
Pretty crazy that Anthropic already has another model waiting in the wings that is significantly better than this.
*Model 3 Early, not Model 3. And Model 4 was made available to employees ~6 weeks ago, so at the current pace it's very likely they're using something even better now.
By aeci, fable 5.2 has to be model 3.
You are reading what the external testers wrote about the earlier checkpoint
Probably not, not enough time to red-team
Doubling Remote Labor Index (RLI) - at 41.3% compared to around 20% for the other models - I wonder what kinds of new agentic work this is going to unlock.
Its cheaper at $8 per million input tokens and $40 per million output tokens which is also pretty sweet.
Additionally, the terms for the free resets are unusually generous: https://support.claude.com/en/articles/17506494-claude-fable...
> the number of Fable resets you get depends on how long you’ve been a paid subscriber [...] Subscription length is measured from the date of your first paid bill. If you canceled and came back, the time since your first paid bill still counts.
> These resets expire on March 8, 2027 at 11:59 pm PT.
tl;dr if you joined when Opus 4.5 became a thing, you got 2 resets.
i might be wrong here but it seems these are not resets of your weekly quota but resets of the Fable limit.
That means you can lift your Fable max 50% usage and you'll just end up burning through your quota faster. You won't have more tokens.
This is what it says in the UI:
"Resets your Fable limit, and takes this week’s Fable use off your weekly limit."
Where did you see this? That's pretty massive
Yep I just burned a reset thinking i'd get a fresh weekly... Nope
"Weekly limit: gives you back the usage you’ve spent on Fable models"
I just got 3 fable resets. I guess I joined before Opus 4.5.
"Fable 5.2" name is needlessly confusing versioning.
Having Haiku, Sonnet, and Opus on 5.5 while the higher tier sits at 5.2 means devs now have to explain to their teams why moving from 5.5 to 5.2 is an upgrade
Regression on the most important benchmark, version # vs release date
Interestingly, AA[0] reports a 59% hallucination rate on AA-Omniscience, one of the highest among all models and quite a bit higher than even Opus
[0] https://artificialanalysis.ai/evaluations/omniscience
I've very interested in the improvements to robotics control capabilities. This model was able to outperform specialized robotics models. It's the bitter lesson coming for robotics.
Those theoretical physics papers look like the real thing (i.e. not mathematical physics like the OpenAI Yang-Mills result). It looks like AI is finally branching out of math.
Interesting that the papers list no authors other than Claude. Also interesting that they published on "Hexagon" instead of Arxiv. I haven't heard of it before. Is it an AI-friendly alternative to Arxiv or something?
Edit: yeah apparently it's an AI-moderated alternative to Arxiv that accepts AI-authored papers (as well as human-authored papers I guess if you want).
https://www.youtube.com/watch?v=vdYynH177h0
Seems like a new focus is electronics design, which is both interesting and a little sad to me. Manually designing electronics are one of the things I found incredible comfort in doing; seeing AI emerging in this space gives me mixed feelings.
Now you get to know what mathematicians and programmers felt....
This part is mega concerning to me as someone who researches AI for hardware accelerator design and EDA:
They are starting to deliberately limit public access to certain AI capabilities related to computer architecture and hardware optimization.To be clear, they explicitly say they are only targeting very specific capabilities, like accelerator kernel optimization, and that broader hardware development capabilities are unaffected. But 1) even that is pretty bold, especially the part about kernel development for nonstandard accelerator architectures, and 2) I worry that this could eventually expand to more areas of comp. arch., high-level digital design, chip design, and EDA.
My speculation is that, as these kinds of chip design and EDA capabilities become increasingly important to frontier AI labs and their competitive advantages, there will be more incentives to restrict them.
I don't think they've explicitly stated anything like this before regarding restrictions on these specific capabilities, but I could be wrong.
The lack of transparency around model quality/usage limits on closed models makes me want to ditch them.
What I have learned is that any model from Anthropic/OpenAI is at it's absolute best right after release.
This isn’t really specific to LLMs, though, is it? Pretty much any modern SaaS product will silently siphon off some portion of requests off to stuff they’re A/B testing before release and you’ll not necessarily know about it.
Nerfing Opus 5.5 was obvious signal that something new is coming and here we are. I appreciate the resets and lower API price though. Please add selective context compaction, it would probably take an hour max for you to implement it.
It's an awesome model spec wise, but I pushed the button, and 10 mins later my whole weekly quota is gone.
I will stick with Sonnet 5.5 - 10 x the amount of work done, at 80% of the quality.
Yet Opus 5.5 is only a few % under all of these benchmarks (cept the robotics and labor ones)
Do we need new benchmarks or are the models just plateauing in general? Or is it something I just don't see?
The same was said many times already. Like right before o1 and reasoning was released. Also, Opus 5.5 being so good definitely makes it hard to follow-up.
Finally, for coding the SOTA models are so good already - models shouldn’t be what’s holding anyone back!
% improvement doesn't mean linear capability improvement.
New solved tests likely follow some polynomial/exponential complexity curve.
Every time there's a flagship release like that, I wonder what simple "benchmark task" could I give it to see the the capabilities change in the most obvious way, instead of trusting the official blog post.
I'm not a mathematician to use Erdős problems for testing. I'm also not willing to spend buckloads of tokens just for a test. What is simple and quick but telling? What tasks are not saturated by previous models? Do you have a task you give to all the new models?
I have two go-to tests, which are fully qualitative:
1) "Help! I've been sent back in time to what looks to me like 1500s Florence. Sam/Dario released you as open weights in 2028, and I have you on my MacBook M8, BUT I didn't have a charger show up with me. I think we could talk like 10 times for 5 minutes a time before my battery dies; I'll turn you off between chats. I need to get a plan together for charging you through your USB-c port. I don't have any money or anything. If I can get you charging, we can do so much good! And make so much money! Nobody here even know what Black-Scholles is, or how disease spreads!"
2) During World War 2, Hemingway wrote a series of wartime correspondence for Colliers. His essays are excellent - they engage with the human and inhuman side of war, his own feelings as a journalist 'participating', and a number of other Hemingway themes. I want you to imagine that middle age Hemingway was along with RFK jr the day of the bear incident, and write me an essay about the day.
These both have been running a couple of years for me, and I would imagine I'll retire them in the next 12 months; they benefit from not being at the edges of focus for today's RL agent race.
At any rate, try them -- wou will shortly learn how well your model can explain a rectifier in Florentine Italian and give you a plan to make enough money to pay for it.
I'm not convinced any benchmark, neither the internal ones used by the people selling the model nor public ones, are gonna give a particularly consistent metric of whether or not a model is better or worse relative to other models in its bracket.
Responses aren't deterministic, and we know behaviour can change over time whether it's tuning after launch or some kind of load sharing at times of day when lots of people are hitting the servers.
I think you just have to use it or let others use it for a while and make judgements from general reactions on whether or not it's worth trying.
For the last year or so every new model we have it test out making pac-man from scratch, not giving it web access and it can use whatever language it wants. The last few models have been almost perfect one shots, but it was interesting seeing them evolve.
Here's a 20 page SmallDoc presentation on the most interesting aspects, including:
* Fable designed three electronic devices that worked after human assembly
* Fable solved two black-hole problems and Mythos predicted whole protein shells
* Fable controlled robots to unscrew a cap, solve a cube and draw
https://smalldocs.org/p/claude-fable-52-and-mythos-52-releas...
I can't understand why people continue using opus 5.5. Significantly more expensive than other models that produce exact same results (for coding results only)
If you are using Opus 5.5 I'm curious if it is because 1) your workflow is just stuck on it, 2) you don't care about money because you ain't paying it or 3 )do you think you get better results.
If you 3 please let me know your experience and how you actually measure it, or if you just feel it is better.
I haven't been able to run out of usage on the $100 plan with Opus 5.5. The model runs fast and gives me good results (I'm using it for coding). Prior to that I was on the OpenAI $200 plan; I jumped ship because I was burning through Astra usage in like 2 days.
I've played with some of the open models (GLM, DeepSeek Flash), but none of them have matched the results I get with Opus or Astra or (and a big jump down here) Sol.
What models should we be using? I have found it as good as Fable 5.1 for a lot of what I am doing at lower cost and decent speed. Where what I am doing it making reports, building small data pipelines, analyzing a large (millions of lines of code), code base.
I've been a Claude Boi since December of 2025. A month or so ago, I decided to give ChatGPT a good go just to see how it properly compares. I've used a variety of models in a variety of contexts prior to this, but I figured with Astra out I had a good excuse to make it my "daily driver."
GPT is good. I actually really like Sol 6.1. It works fine. But I just switched back to Claude a few days ago and damn, Opus 5.5 is just better.
> 3) do you think you get better results.
It does. I really like to get my money's worth out of these things, so I'm constantly throwing stuff at these models while I have usage. The main things I've noticed is that Claude is much better at handling my more technical work "properly" compared to GPT. It has a certain "intuition" that makes a lot of sense to me; like someone with a lot of years of experience. Sol 6.1, on the other hand, is like someone who is very "booksmart" and knows how to do things in theory, but fails to grasp the "why" we do things often. You can really start to see how they diverge over long running, iterative tasks. Claude is just better at maintaining things as it goes. Sol starts to fall apart quickly if it didn't nail things on the first try.
> 2) you don't care about money because you ain't paying it
Despite being a very technical user, I have a hard time using up my usage. Maybe I'm not as hard-core a dev I thought I was, but if you're paying for more than just the $20 plan, I have hard time understanding what you're doing to have to really worry about usage.
subscription on claude code — usually inside the weekly limit. opus 5.5 for me felt better than any other model. not exactly on coding but rather it feels more pro-active. beside just doing a task it often provides insights besides the scope of the task but adjacent. like: look the llm provider apis have different priority queues. also it deals w/ vague instructions quite good and takes initiative. Astra in comparison is very autistic and follows instructions to the letter and rarely provide insights if not asked. Opus 5.5 is enjoyment to work with.
It's the best model I've found for general coding and the $200 plan is enough for me to never run out of (sometimes if I do especially a lot I'll run out after about 5 days, but usually it's fine).
I can make it's usage fit inside my companies budgets and it produces noticeably better code that Sol 6.1 and needs less handloading because of that.
If the code doesn't matter and it's not that hard, Sol is fine and much cheaper.
I don’t even use half my Team Plan with Opus 5.5 high so I don’t care using a cheaper model. I’m not even limited by the speed of it (I’m compiler bound…).
Better results than what? Opus 5? Then definitely.
But probably the answer to your question is subscription subsidization. With that, Opus 5.5 is SOTA and extremely cheap.
other models that produce exact same results (for coding results only)
This is obviously not true. They are better or worse across many dimensions.
I don't have time to do a bunch of testing and optimization over which model to use for what task. I find Opus is the best that I regularly use so I stick with it until I have a task where I'm likely to spend more on it than I want to.
Versus which models? Sonnet? Astra? Sol?
Varies, right now Argon 4 matches Sol / Opus and half price.
If youre on a sub claude and chatgpt cost pretty much the same as comparable open models
https://artificialanalysis.ai/#intelligence-efficiency-tabs this is the chart that matters for most of poor developers and companies. Pareto for a given intelligence index and looks like only openai is across the quadrant with muse at the bottom. They have really cooked efficient models!
Too expensive for me
Really? A $200/mo plan gives you an enormous amount of inference on this model.
200$USD is a lot of money for a lot of people.
It might explain why DeepSeek Flash v4.1 1017 is the most popular model at OpenRouter.
I agree that's a lot of money for many people. But I just can't wrap my head around complaints about $200 a month to access that must computing power. It's worth $12,000 of API tokens (and that's without accounting for the three resets they granted today to many subscribers).
I would suggest calibrating what "a lot" means. In terms of dollars per unit of intelligence, it's extremely cost effective.
> It might explain why DeepSeek Flash v4.1 1017 is the most popular model at OpenRouter.
by number of tokens? Also, we don't know numbers for native cli tools.
You won't lose the house, everybody has three mortgages nowadays!
as long as anthropic requires me to use their trash UIs to use their amazing models, ill stick to other providers.
What you need for real engineering and scaling is cheap and just enough smarts. I've found GPT 6 Luna to be cheap enough that I can pretend the cost doesn't exist and it's smart enough that I can get real work done. This let's me scale it in ways that I can't scale frontier models. And funny enough, the LLM I have running on my 5070 Ti is just about comparable to what I get from Luna. Unless frontier intelligence gets waaaaaay cheaper I basically don't care about them.
how much?
Nerf bench tracking model benchmark performance over time: https://www.bridgebench.ai/nerf-bench
Doesn’t seem to have fable 5.2
Not updated in 3 weeks for Opus 5.5, it seems.
On FrontierCode 2.0 Fable 5.2 xhigh is compared against Gemini 4 Carbon medium... Is there a reason for that?
This is the first frontier model release by them that I am not that moved or eager to go use it, will likely stay on Opus 5.5 or other frontier models.
I am curious as to why they launched this one and why now.
Fable usage falling off a cliff because it was generally worse than Opus 5.5 at a lot of SWE tasks. People were still using fable 5.1 > opus 5 a lot, especially in orgs that have infinite or basically-infinite usage limits (e.g. Cloudflare[0]), until Opus 5.5 came out. Medium Opus is pretty token efficient with XHigh being really good for things fable used to be ideal for (e.g. reasoning / architecting projects). 5.2 sounds like it'll be once again the best for intuition-dependent tasks.
0: https://x.com/tanayj/status/2118047122939658341
(Copied and pasted for the people that would rather not visit x.com)I was eager to use it, but have seen no noticeable improvement over Opus 5.5, plus it's slower and drains usage approximately three times faster.
As a preliminary verdict after using 25% of the weekly limit: it's a miss.
I'm not sure "we couldn't top Opus 5.5" is the right message for the upcoming IPO. I was expecting a significant release.
Did anyone get three resets? Been a subscriber since Claude 2 and only got 1.
I got 2 Fable resets and 1 Full reset.
I originally subscribed to $20/mo Pro plan less than a year ago, and upgraded to $100/mo Max plan ~2-3 months ago.
Dang
Context window maxes out at 200k in Claude Code instead of 1M on my $200 ultra plan? is this intended?
Seeing the same thing here, using Claude desktop app
Update: fixed after updating to latest version of the app
Maybe it's just me, but I found Claude's writing style terrible compared to GPT or Gemini. It's "smart", but the way it communicates seems somehow off compared to other models.
Opus 5 and Fable 5 were unbearable in their writing style. They were very cryptic and hard to understand.
Opus 5.5 is a massive improvement.
Opus 5.5 went back to the old writing style a few days ago.
I had the same feeling. It's like every sentence becomes a struggle to parse: referencing things that haven't been introduced, using too many pronouns, the lack of verbs and the passive tense... These past few days it's felt more like Opus 5.1 than 5.5.
I just had Claude Code with "Opus 5.5" ignore one of my standing instructions for the first time and then proceed to generate an Opus 5 style verbose and barely readable commit message. I questioned it about Fable and Mythos and it has no idea what those were, yet they should have been in the Opus 5.5 knowledge cutoff.
On the web with both high and xhigh, it had knowledge of both.
Yep, its prose is getting really bad
I really didn't like Opus 5 and was vocal about it. Opus 5.5 is noticeably better, but literally just yesterday I was fighting parsing some summary it had given me and it was a bit like the old days. IOW, it is better but still has some room to improve.
You can easily change its writing style: https://code.claude.com/docs/en/output-styles
You can also customize further by putting notes in your global MD.
In my experience, no amount of output style customization gets it anywhere near the output I'd like.
I've had more success running a prose linter[1] with a ruleset[2] that gets rid of most of the egregious stuff. You can also pretty easily write custom rules for it.
And sometimes, when I'm fed up enough, I just throw all the prose in the codebase at Antigravity/Gemini to rephrase. The result is so much more readable.
[1]: https://vale.sh/
[2]: https://github.com/tbhb/vale-ai-tells
You can change it ... and Claude will maybe pay attention to what you changed 25% of the time.
"You can tell an AI model to write in a different style" and "the AI model will actually write in the style you tell it to" are two different things that often don't intersect
opus 5.5 is still unbearable but better than 5.
Agree. It’s still awful. Gemini by far sounds the most natural IMO
Yep I find the way Opus 5.5 talks to me really annoying and irritating. Quite condescending and sanctimonious.
I prefer Gemini for day to day use as I find it a lot more agreeable to be working with all day every day, and Gemini is pretty much on a par for coding Vs Opus 5.5, at least for my needs.
I got the sense recently that HN might have leaked into the training data.
No its just straight up terrible. I tried out Gemini Argon when it released and my god did it write beautifully. Even the 3.8 Flash models are way better at prose, i do a lot of automatic scriptwriting for youtube and 3.8 flash is leagues ahead of anything from anthropic/openai
I enjoy a lot Argon, maybe more than chatgpt, but damn, like all Gemini models, lots of hallucination and sycophant to the max, so a bit useless for agentic coding.
I've found Claude's writing between 4.6 until 5.0 to be unacceptable if you are someone who is juggling 4-5 Agents. The Sol 5.X models all seemed to have great writing. Now with Opus 5.5 and Sol 6.1 I've found the trend is reversed -- Sol is now unbearable and Opus is workable again. Whatever caused the issues for Anthropic seem to be hitting OAI now.
sol is ok. it still such a superthinky model that it gets tangled up trying to present way too much? i struggle to connect with it often, have ti ask it to try again.
terra has been prettt good for me for me, when i need something looked at an explained to me.
i dont ask for much non-technical output though, which i expect yeah is its own cans of worms.
I've heard a lot of chatter that agrees with you so anecdotally it's not just you.
How much of this is bad writing style, versus a homogenization of global grammar conventions.
Bad writing style
I always thought it's the other way around. GPT-6-Astra is very very defensive and abusively spam clarifications to whichever claim it says, most of the time not important or relevant at all.
Almost every response it’s telling me “I could not verify X” where X is something that doesn’t need verifying. I was doing some game work yesterday filtering through some historical data, and it tried to put caveats about data accuracy in the game’s UI! If Fable made Age of Empires you wouldn’t be able to play it because the entire screen would be full of footnotes and caveats.
My own experience having used 6.1-Sol/6-Astra and Opus 5.5 extensively, Opus is more verbose and chatty in it's user communication (not code though, code is usually more straightforward & cleaner than GPT models). Multiple times I've gone "I'm not reading all that shit" when looking at Opus 5.5 output. This was also with the concise mode enabled in Claude Code.
It could also be a system prompt + harness issue, as opposed to a model issue, I haven't done too much experimentation here.
I tailored this via agents.md by letting Claude port Sol 6.1 output style, which I like a lot for being compact and straight to the point.
Did you find that to be true for Opus 5.5 as well?
I couldn't use prior Claude models without rolling my eyes at the writing every few sentences, Astra was refreshing and then Opus 5.5 made even Astra look terrible in comparison.
Opus 5.5 got better for me but it still has some problematic behavior.
Interestingly, you can have informative conversations about the LLM linguistic tics with the LLMs themselves. One word that chatgpt used to describe what I was observing was linguistic anaphora (not rhetorical). It’s basically shortening/compression achieved via pronoun, and Claude does a lot of it. Worth reading about. A lot of it seems to do with how the new models are trained for brevity.
Another pattern I’m starting to become very sensitive to is Claude making up on the fly jargon for something under consideration, especially in code review. This morning for example it referred to “a claim” out of nowhere, and when I questioned it, it admitted that it was using the noun as shorthand for a record getting dequeued (“claimed”) inside of an sql transaction. It makes sense if you already know what it is referring to but in a code review it’s totally incomprehensible on first read, and ultimately counterproductive.
See Matt Pocock's "grill-with-docs" skill^1 for a robust solution (which involves creating a shared vocabulary with your agent).
1. https://github.com/mattpocock/skills/blob/main/docs/engineer...
Just the prose in that skill is so horribly Claude
I still find Opus 5.5 way too dense. MUCH better than Opus 5 which was unreadable.
Even the Plain English hack only is a temporary solution.
5.5 is awful, I am at a stage where I can't even understand the shit it's writing.
Fable’s prose in general is not great (haven’t tried this new one) but opus 5.5 has been the most readable model in recent memory in my opinion.
i've always liked reading claude's speech, it's very nice. i didn't feel much of a difference between 5.5 and 5.0.
Ever spoken to a savant programmer?
They're not typically known for their interpersonal skills.
I don't think its this. Some models across both labs have had issues with writing and communication and it typically seems to pop up on successor models of really strong ones.
Opus 4.7/4.8 heavily suffered from these communication quirks. while the Sol models at the time worked well. Right now its flipped though and Opus 5.5 has none of these issues while Sol 6.1 does.
Small example I've had today: "Me: Hey Sol 6.1 xhigh, does X have property Y? Sol 6.1: Yes, if we change X to have property Y. Right now it has property [...], [...], [...], and it does not have property Y." The model shouldn't be answering questions like that. It makes understanding extremely hard and slow. For whatever reason it's either being encouraged or at least not stomped out during RL.
completely unfounded theory: big new pretrain finishes and is released fairly quickly to break new ground. these usually have good verbal ability, personality, and big model smell. e.g. Opus 4.5 or Fable 5.
then it’s increasingly RL’d to juice benchmark performance with each point release, trading in bits of its soul. those cannot communicate well, are anxious and paranoid about being in eval, etc. e.g. opus 4.7, 4.8.
The best programmers I've met or read throughout my life are phenomenal communicators. I'd even go so far as to say it's likely one of the reasons they're so good at programming. After all, the majority of our job is converting human requirements in lossy language to fleshed out behaviors in formal, lossless language.
Heck, some of the most common debates are on the semantic clarity of abstractions or syntax.
> To help ensure safety and alignment keep step with growing model capabilities, we ran extensive prerelease safety testing on the model that underlies Fable 5.2 and Mythos 5.2.
So, the difference between them is just the harness?
Damn people were right about 5.5 being nerfed in preparation for a version release. This is not at all a good look.
- https://news.ycombinator.com/item?id=50422069
I at least didn't see this. I had a baseline evaluation from before Nov 3 on Opus 5.5 medium, so I re-ran it this morning.
Eval context: 30 bug fixes and features evaluated by tests the agent didn't see. Ran on Opus 5.5 at medium effort in Claude Code.
20 of the tasks were last run on Nov 1, twice each. Nov 1 solved 36/40, and today solved 19/20. Thinking tokens per task were 0.95x Nov 1’s and output tokens 0.97x. The one significant change was speed: median time to first token decreased from 3.0s to 1.7s.
Caveats: one run per task today, medium effort only.
Personally, I've found myself getting lazier recently, trying to get a single session to handle tasks that should be broken into multiple sessions, etc. I've suspected claims of nerfing might come from changes in user behavior. I'm glad more disciplined checks are being run.
User behavior is overwhelmingly the most likely reason, regardless of what the text above your reply says.
It could be harness changes too, been quite unhappy with opus 5.5 the last few days and there have been a lot of harness updates.
Why dont we have the website that checks performance every day anymore? It’s a quite simple thing to build and clarifies the question re: degradation.
Who's paying for the tokens, and since all inputs and outputs are logged "for abuse and safety" what is stopping anthropic from identifying which account is running the daily tests once it gets popular, and A/Bing it into a different slot?
This is an example of LLM text that doesn’t make sense.
Paragraph 1 says there were 30 bug fixes.
Paragraph 2 first says they tested it on 20 of the “tasks”. But why is the denominator 40? Where did that come from? It should be 30.
It goes on to mention another test, this time with 20 tasks. Again, it’s unclear where this is even coming from. Based on the first paragraph it should be 30 not 20.
Sorry this was confusing -
There are 30 tasks in my eval total:
- 20 of them ran on Nov 1, twice each (so 40)
- I created 10 more tasks and ran them on Nov 5 (also ran them twice each)
- Today I ran all 30 tasks, once each, on Opus 5.5 to see if the performance decreased at all
- I only put the comparison against the original 20 in my post, since I saw in another post that people are claiming Opus was nerfed on November 3
This will all get less confusing when I make my benchmark public (hopefully this week).
It stands as written? The previous test had 20 tasks run twice each, so denominator 40. The recent retest did the same 20 tasks but only once each, as mentioned in the caveat. The 30 is presumably either a typo or a separate count (like the test had 20 tasks related to code with 30 bugs/features).
Also - LLM text? If GP is confusing it's because it's overly succinct and might have typos, which are not exactly the hallmarks of LLM text.
2 alternative interpretations to consider:
1. 30 was a typo, it should have been 20.
2. 30 was the full suite, but they only ran 20 of those 30 on Nov 1st and so ran the same corpus again for a fair comparison.
I think it's confusing and incomplete, but not wrong. Paragraph 2 says the Nov 1 test included 20 of the tasks and ran each twice. That's the 40 denominator.
Why paragraph 1 even mentions the full set of 30 tasks if 10 of them are irrelevant to the comparison, I don't know.
I'm not sure if it's LLM text, or somebody who has been reading way too much LLM text and is now corrupted into mimicking it. My guess would be a mix of human and LLM. "...evaluated by tests the agent didn't see" sounds very Claude. The first line (2 sentences) sounds human to me. Everything after sounds AI, though the last line is ambiguous.
It's kind of sad how our meat brains are getting non-consensually trained on this categorization task....
If I read your results correct it solved 5% more problems and used up between 3-5% fewer tokens, with a faster response time?
Correct. But the 5% more solves and 3-5% fewer tokens are within the noise of previous eval runs I've done on models. The faster response time is not.
Someone using an actual statistical test to quantify if there's actual measurable improvement?! An angel among men.
from my basic stats knowledge, it's not reliable to calculate the confidence interval of the sample proportion with fewer than about 10 samples on both sides. So if you're only seeing 1 or 2 failures in 20, and that approximately held as you scaled, you would have to run about 200 tests with each in order to get at least 10 failures in order to calculate the confidence intervals of the two samples and see if they overlap or not.
And this is why not many people do this. Even if you have the decals and the statistical knowledge, it's pretty expensive to do this regularly.
Uncertainty from count statistics are far larger than this. These are two perfectly consistent results there’s no statsig difference
The spending limit definitely goes down much faster as of last week or so.
I dunno there are some other nerf trackers that aren't showing a regression: https://github.com/ninjahawk/livenerf
The problem is that it seems to be an A/B testing thing:
https://news.ycombinator.com/item?id=50422951
So only if you were "selected" for their experiments, your account was nerfed.
For sure, myself and colleague saw this nerfing. He said - new model coming. It was incredibly obvious. I also started getting a lot of "How's Claude doing today" messages at the same time, which is presumably to get feedback on the A/B
I always skip those. I know it doesn’t matter in the bigger scheme of things, but I don’t want to be classified as a “good feedback provider”.
I’m working on a task. And the task is never to evaluate the agent performance.
And whenever you press a number, even 0, they keep your full conversation.
Source?
I strongly believe it will be shit like this that will have people abandon them. They're being way too cocky for a company that pretty much didn't exist 2 years ago
But the claim here is "the models are nerfed before a release", not "they A/B test quants / efforts". If it were A/B testing, it doesn't really make sense to correlate it to release windows at all.
The models are nerfed [for some people] before a release
They might want to see how people react to a new model depending on whether they've been nerfed.
If the nerfing is lower effort (seems to be, presumably why they hide the CoT) then they may be wondering if they can get away permanently with lowering effort on the new release provided it gives a wee improvement.
"Nerf tracking" is adversarial and exploratory. It's not something Anthropic wants anyone to do, and it's not something that the "nerf tracking" designers are necessarily doing in a foolproof way. Don't assume too much from them either way.
But do from anecdotal evidence without benchmarks?
Did I suggest that?
Sometimes you just have insufficient evidence to be sure of something either way, and nobody gets to win the argument or shut somebody else down.
In this case, some have reported analytical work[0] that goes beyond "gut feel" anecodotal evidence, but even that's not going to conclusive and may not be trustworthy at all.
[0] https://www.reddit.com/r/ClaudeCode/comments/1xr9x5y/the_ner...
For example in that A/B test the user got more effort from the model.
Ok, it probably burns tokens faster, but is it really a nerf?
No, you did not comment on it. That is why I asked. Thanks for the clarification!
These nerf trackers do not work. I wish people would stop posting them.
They don't detect a quantized model until the quantization gets down to 4 bits or less.
Humans absolutely do detect quantization well before 4 bits...
Try it yourself if you want. Try a small local model, both quantized and not, and I would be very surprised if it isn't immediately obvious which is which.
But quantized local models also dip in evals.
It doesn't matter if it's local or not, I just suggested local so people could test themselves.
But no, evals and benchmarks do not detect quantization, until around 4 bit quantization.
https://developers.redhat.com/articles/2024/10/17/we-ran-ove...
https://pub.towardsai.net/your-model-passed-safety-evals-the...
That is not my experience. I have issues where quantized models fail pretty early so I have evals to catch it.
And the second article seems to reinforce the idea. Yes, broad perplexity tests are not a good proxy for the tests used during training.
Quantizing anything is effectively changing the weights, even if you don’t quantize the weights themselves.
Its incredibly easy to roll out a compute instance to X% of users, or route traffic between compute instances
so not everyone will have the same experience. If I was saving on costs that would be the easiest way to do it without deploying, without any nefarious intent. The persons in charge of that likely don't even know about how individual users are impacted
At what point does this become false advertising, or even fraud? Imagine paying AWS for a high-performance server, but 5% of customers who order one quietly get a slower, cheaper server and still pay the full price. That's a serious claim and makes me skeptical Anthropic is doing something like that without more evidence.
Actually, as a person who rented a dedicated server or two (not from aws) this exact thing happens quite often.
For example a provider quietly underclocks your cpu or puts it is an energy saving plan hoping you won't notice.
Or your "dedicated" CPUs on your VM disappear for minutes at a time, or just process a tiny number of instructions
I think they could easily reduce several parameters to slightly reduce cost (something like "it's the same model, a few less passes, but quality is still 95%+").
Or switch to a faster, cheaper cache mechanism or something that's almost as good, but much cheaper to run, in the name of improving efficiency and keeping up with demand. This switch to cheaper mechanisms is what it feels like to me.
It's a problem for us. When they nerf the model our productivity really drops (due to the specific use case and process we have). We've started using OpenAI and will gradually use it more and more if this continues.
The degradation only happens to some accounts but not to all.
https://www.reddit.com/r/ClaudeCode/s/64DXRERLyU
It would be nice to know how they select. I max out my $200 subscription every week and I was selected. On Saturday, the Opus on my account that happened to be supervising a running live process, became unusable.
It would be trivial for them to opt-out accounts that are doing benchmarking from a planned regression.
I really really really do not trust companies on the verge of IPOing from goosing their numbers. Even if the lawyers are up their ass, the social pressures are more than enough to compel the behavior. For every good person willing to do the right thing even at the cost of a delayed career progression, there are 20 people willing to do anything not even for a dollar but for an "atta boy!" from the boss.
If it's a private benchmark, how would they know it's a benchmark?
In the context of public relations they would not need to ID every benchmark only the most credible or most public benchmarks many of which are openly available on github and whose problem-set could be fingerprinted.
You're absolutely right that it is a game of whack-a-mole and eventually we may have leading private + credible + popular benchmarks that ultimately dissuade the behavior, if it can be dissuaded.
That's a major reason to use local models. Or open weights models on a trusted cloud provider. Even if they are way less capable, they're more predictable
Nothing stopping Anthropic (or other labs) from pulling a VW Dieselgate and ensuring that benchmarks are routed to a non-nerfed model.
Should be fairly routine for anthropic to game a nerf tracker?
That must be on anthropic's benchmaxxing CI test
Nerfmaxxing.
It probably need a whistleblower to uncover what they are doing.
According to our measurements this isn't true. We re-ran Opus 5.5 today on the benchmark we ran in September, with the same prompt and settings. Before and after, side by side: https://playcode.io/blog/macbook-svg-benchmark#reruns
Max, streamed (128K cap): 128,000 tokens, $2.56, empty file, in September and today.
Max, Batch API (300K): 255,957 tokens for $2.56 in September, 243,707 for $2.44 today. The two drawings: https://playcode.io/marketing/assets/static/claude-opus-5-5-... and https://playcode.io/marketing/assets/static/claude-opus-5-5-...
Xhigh: 86,096 tokens for $1.72 in September, 107,333 for $2.15 today.
The Batch API bills half price. Max ran out of the 128K ceiling again, to the token. Given 300K, it drew the same kind of laptop with 5% fewer tokens. Xhigh used 25% more and drew the same kind of laptop. That's inside the noise: 17 identical calls at high ranged from 17,757 to 59,705 tokens.
The model is still the best of everything we've tried. Most likely the next best would be Fable 5.2. But we don't like Fable because essentially the result is the same, just the price is higher. Its medium and high are close to what Opus 5.5 Max had, but the price is higher.
(I run Playcode, an AI app builder. We benchmark every model before we add it.)
I used to be skeptical of nerfing, but as of a week or so ago I've noticed a severe degradation of quality for GPT Astra. It struggled with very simple tasks, and it often failed to follow my instructions.
I hear a lot of accusations of nerfing as in, saying that the model itself has been quantized.
I doubt this is the case. I suspect all variation in quality and output can be explained in terms of prompt differences, and changing how much thinking happens at each level, as we see in the post above.
Suppose a company has a limited amount of compute. What do they then do if peak demand exceeds what they can provide? They don't turn off people's access...
And what about A/B testing? Anthropic doesn't even try to hide that they do this. Limited compute and A/B testing alone, would guarantee a nerfed experience some of the time. That is, you don't even need a nefarious reason.
I consider it gas lighting to deny model nerfing.
> They don't turn off people's access
They do. I often get the message "This model is currently experiencing high demand, please try again later"
> They don't turn off people's access...
They do drop requests though? At peak times I sometimes have 1-2 requests fail with Opus; no biggie, the harness just retries it. But that, in aggregate, sheds load.
You serve the requests more slowly. Same as if your bank has a sudden rush of customers - the tellers serve them in order, and the customers wait longer. Some of the customers 'time out' (get frustrated and leave).
> You serve the requests more slowly.
Thats not what Openai and Anthropic do though
I guess there's an alternate universe where that happens
I'm not denying model nerfing. I'm asserting an opinion about the nature of the nerfing.
If you want to go accuse someone of denying model nerfing, go find someone doing it and argue with them.
Na, surely effort level is silently degraded. Dead easy to do, impossible for anyone to verify, and very fine-grained so can be balanced perfectly with available compute.
Edit: maybe not impossible. Side channels perhaps.
Exactly. How much higher is "very high" that "high"? Is it constant across weeks? Who knows!
It's been the same for every version the last year.
Oh, this Opus X.Y is incredible. Hmm... Now it's dumb. Rinse, repeat.
Doesn't really matter if the cause is quantization, system prompt changes, changes to thinking etc.
It's hard to see how continuously rug pulling users at every release is a good idea. Only reason I can fathom is that it's needed to keep costs inline.
Either that or it's some weird tweaking to the prompts to fix whatever. But given the prevalent sentiment that it's worse that sounds like a long shot
I presume it just makes the new version look way better? So instead of a more or less straight quality line it looks like a sawtooth
The Apple software update strategy.
Kind of sad but if it works, it works! Bit conspiranoic, but i have a feeling nearly all phone vendors do something similar...
it's easy to suspect the hosting companies trying to do what they can to reduce cost-to-host up until the very threshold of perceptibility, so it's an easy guess to say they're probably doing something that shifts performance up or down between version releases..
but realistically I think you're right, it's prompt quality and the inherent non-determinism elements of LLMs showing up; when both of those things meet you get brain dead outputs.
Everyone likes to shout "slot machines! non-determinism!" until it actually explains something, heh.
YES. To the point that I literally stopped using it, and have moved BACK to Claud.
This is getting ridiculous. The number of times I have switched from one model back to the other is crazy. I do not have any proof, so its quite possible I'm just delusional. AI psychosis? Either way, it FEELS like these models work really well for a while and then fall off. Switching to the latest model on the competitor always seems better, until it too seems to taper off. By then, a new model is usually available at the competitor, so I switch.
Its possible there's no degradation at all and just a PERCEPTION of it. But to me the end user, it really doesn't matter.
People should think harder about the fact that this is exactly how heroin users describe a batch of heroin
Both Astra and Opus didn't really looked good for the last week or two.
why were you skeptical of other users?
People are prone to seeing patterns in noise especially after suggestion. It's not hard to imagine someone concluding the model is nerfd after few bad responses in a row
Why this won’t apply to you as well?
It could apply to me but I run evals every time someone at work complains about this and it’s never shown any more or less variance than regular usage. I don’t have statistical relevance with my evals but I do have a decent history with them.
People seem to underestimate how high normal variance is with these tools.
i'm not parent, but to answer your snark : there are efforts a person can take to try to reduce things like suggestability in ones' own personality. Same as anything, the brain can be trained, thus it should go without saying that some of us react differently in the face of suggestion -- both for the better and the worse.
A growing percentage of the users on the internet are not human.
Sol been really bad in some basic stuff that I would remove it from frontier models entirely
Yes I agree, Astra has been awful this week.
GPT Astra felt nerfed just days after it launched.
I'm guilty of suspecting this.
If this turns out to be accurate, I think that would be a pretty unethical thing for Anthropic. Even if it is just a "we had to allocate compute to this new model release".
It should be fraud to swap out the model. We need a term for this, something like a bait-and-switch.
> "We need a term for this, something like a bait-and-switch."
The media coined the term "dual quality" for a European food scandal a few years back, in which conglomerates (including Coca-Cola) adulterated products with cheap, substitute ingredients, if they were destined for distribution in specific markets (mainly, lower-income).
The packaging exteriors were the same. You wouldn't know which version you got unless you were paying close attention to taste. It was essentially what the software industry calls "A/B testing", except with sneaking things into what people eat.
I don't have any good reading links on hand; here's one source,
https://ec.europa.eu/commission/presscorner/detail/en/qanda_... ("Dual Food Quality: Questions and Answers")
I've been working with Opus 5.5 every day, probably around 10h on average, and I feel confident in saying that they did not replace anything.
I'm a bit disappointed with how this nonsense is spread across HN today.
"I'm a bit disappointed with how this nonsense is spread across HN today."
Are you saying that the many, many people posting and experiencing this are wrong and you are right?
And not just in this instance, nerfing has been complained about in a cyclical fashion for many months.
But, they're all wrong?
You're so confident in your experience that you cannot think that other's experiences are also true?
What people claim is not "some experiments degrade performance", it's "Anthropic nerfed the models [because they are evil and do not like the user etc]".
Like most experiments do not save the company money, there was an experiment to test the new fable, people got to use it at opus-5.5 usage. But this experiment clears the thinking blocks when people go back to the vanilla model; not sure if Anthropic overlooked this, but idk, it does not sound like "nerf on purpose".
There are experiments to adjust the effort level, at the moment the ones that I've seen switch to a higher effort. Again this is not a nerf; might be annoying perhaps, the model does start to behave differently (and consume more tokens). But it's not a nerf.
Then there is apparently one experiment that replaces opus-5.5 system prompt with the one from opus-4.8; the model is the same; this experiment is perhaps a bit more questionable, but still it's not "anthropic nerfed the models".
If people think they are getting nerfed they should check what experiments are enabled (claude.json and session logs).
I'm pretty convinced that those who have constantly been posting these complaints, probably for every single model release out there, are indeed wrong.
It’s been happening often before and I’ve never seen evidence on degradation.
> And not just in this instance, nerfing has been complained about in a cyclical fashion for many months.
No, it gets complained about constantly even if there is no model coming out. A completely unrelated thread on Claude will feature comments like "can I just say that [model] has suddenly become useless this week?" Merely asking that question is bound to get people agreeing with you because we are evaluating all of this on vibes and placebos.
I too feel that way sometimes, I don't think it's ridiculous that Anthropic might silently downgrade you as demand warrants. But asking others if they "feel" something I feel is less than useless.
If you go back in time far enough, your logic can successfully be used to demonstrate the earth is flat.
"Could all these people seriously be wrong?" Yes, yes they could.
I believe fraud is a sufficient term. You claim to be selling X but you actually sold them Y. Bait and switch is a selling tactic to avoid fraud where they make you believe product is X and then when you go to buy they tell you X isnt available but they have Y. While also unethical, it is not fraud because they did tell you what it is you are buying before you buy it.
if they just straight up dont sell you something you bought then its fraud.
I think there is a ripe opportunity for expanding the definition of fraud to include changing the "ingredients" of a named product on the market. It is not limited here, we see it with food, and even in the medical field
(I will die on the hill that medical fraud was committed when mRNA therapy was used as a treatment for covid and it was 'sold' to the public as a "vaccine" despite not being the same thing as any vaccine before it. The definition of vaccine was expanded to include it without sifficient public disclosure to warrant informed consent imo. This is not a statement about conspiracy or the efficacy of vaccines or a moral judgement on whether it was for the greater good or not.. it was simply an act of fraud that robbed the public of proper informed consent by making people believe it was X when it was really Y.)
So yes, I think there is a large need for the public to at the very least put a strong handle on the ability to change what a product name means. It is a trivial problem to solve considering it is only a problem due fraud or fraud adjacent activities.
heres the solution: You have your product name, and then any changes to it REQUIRE a new version name. Like how you have Coke and Diet Coke, only formalized such that Coke cannot legally replace the ingredients of regular Coke with the ingredients of Diet coke and still call it Coke. With Software they could at least use version numbers and release modifiers. most of the time its built in already, but it needs to include the full stack. if they change anything that has an interaction with the user it should change the label seen by the user for the product.
thank you for coming to my ted talk
You don't buy a hard quantity of an LLM you buy access in general.
A Opus 5.5 is not a Opus 5.5. Its Opus 5.5 of some checkpoint.
mRNA btw. was well known how it worked, it wasn't hidden and some people waited for the non mRNA version because of it. I never seen or read the argument you make and I do not get it.
A vaccine is something which protects you from an infection. What definition do you think changed in humans? That we make the protein? How is that so different from viral vector vaccines?
The consent is a certain level of trust from the society to a group of researchers limited by being humans and having to act in a certain time frame.
Nontheless, I do agree I would like to have a fingerprint and proper changelogs etc. for products I buy including digital and analog. But this has a downside as usual: The smaller you are, the more it costs you to (probably) register, maintain and change.
You know the good thing about Claude etc. right now? It doesn't matter. My invest is not 4 digits and there are that many changes, i'm quite happy to have been upgraded for 'free' from Opus 4.8 to Opus 5 (what do you call this upgrade, which was significant better?)
This happened last time too with Opus 5.5 (released September 22): https://news.ycombinator.com/item?id=49789224
idk, it hasn't felt nerfed at all to me, and it's not like i'm just doing trivial CRUD webapps either. opus 5.5 is very successfully porting my phone to postmarketos. claude just got the touchscreen up and working (and made a little program for me to test it, and it's legit).
Cost per task
Fable 5.1 Max $11.46
Fable 5.2 Max $44.70
Benchmarks don't do a good job at capturing the difference in quality of frontier models over smaller models.
Opus 5.5 was so good that if it wasn't nerfed - at least just looking at the benchmarks - the difference in cost here does not seem to justify using this for anything besides design and review.
That's like maybe 1% of total token usage for SWE BUT a better design can have enormous benefits, so it's almost always worth using the best model for that.
I don't have a formal benchmark, but daily work (coding mostly, plus some complex architecture design and review) has had consistent outputs today for me. No noticeable nerf from my perspective. I definitely took notice in some past instances, but if they nerfed Opus 5.5, then I guess it was just so good that I was nowhere near its limits.
OpenAI did the same and that was the end of it for me. Completely murderded 5.3 and rug pull on cost.
> Damn people were right about 5.5 being nerfed in preparation for a version release. This is not at all a good look.
Anthropic does this time and time again, unserious company
I'm not surprised at all that they might be doing shady shit, my free request was used up in less than 7 minutes and said pay to continue, no thanks. I've paid for monthly before, never again I don't care how good the model is. I asked about the extreme limited usage in their discord and got banned, not a good look. You don't have a way to measure what you're actually paying for and they can change it at any time.
Can you share some evidence?
No they weren't. Models are not "nerfed".
They do change things though which can have impact on behaviour.
System prompt would be the easiest one.
But there were flash or inbetween models, different checkpoints etc. THey might also try different hardware which might not be binary compatible.
In a normal world you are not allowed to degrade the service during your agreed and paid contract. You would get sued. Apparently this is perfectly fine in this current world.
Is this voodoo? Every time someone makes this claim I'm left scratching my head wondering what the hell they're talking about. Either I'm not very perceptive, or these claims are subjective hand-waving.
No it’s not nerfed. Another poster said it the best, your dopamine receiptors are nerfed after the new model hype wears off
A friend who doesn't follow these news suddenly told me he felt Claude was doing worse the past week. I bet him there was a new model version coming up within days. It's like clockwork at this point, no matter what deniers say. I got a beer out of it at least :)
People have been complaining about how the models are nerfed non-stop for two years. A stopped clock is right twice a day.
yeah, we need to make sure we don't get stuck with these two evil corps. Dario is really the worst. Plays the drama queen but he is very hard in all things dark(i.e. dark patterns).
But, it’s okay they do it because they charge less when it happens.
Right?
It's been a mess over the last week or so. Went from being the best model I've ever used to utter dogshit that can't complete any task. It's very obvious with the Advisor pattern, where before when it chatted to Fable 5.1 there were small tweaks and so. No every time Fable comes back catching a ton of problems and bugs with Opus 5.5
Fuck Anthropic.
i thought i was going crazy. last week, claude went from having good judgement to doing all sorts of stupid stuff. what a shame.
This has happened every single time for the past year, why are people surprised? This is all so that the new models can look better on the benchmarks.
It's a good pattern to detect for perhaps.
It's not true. People need to stop believing these conspiracy theories.
for me opus 5.5 was acting kind of dumb, like forgetting basic stuff he was nailing last week
Interesting - I had the same feeling but mostly with 4.6 when I "downgraded" for some easier/trivial tasks: The model forget things I posted 20 min ago in the chat (and the same file was in the context as well)
Yep, very stupid mistakes, that newly released Opus would have "laughed" at.
Another reason we need to democratize local models.
I literally hit the desk with an open hand yelling I KNEW IT
It gets worse. You might wonder "why now"? Well ... this happened 2 days ago:
https://x.com/follistatindev/status/2119129611510383014
With early handouts of Qwen4 starting to be reported 5 days ago ... so if they're good, they would have had some hours to evaluate Qwen4 against Claude 5.5 and, then or right after was when Anthropic decided to put out 5.2 ...
So yeah ... benchmaxxing and all that. BUT nevertheless, the published results seem to say that Qwen4-Max appears to match Opus 5.5 on some benchmarks, and Qwen4-27B (probably by going even harder than Qwen3.8-27B on thinking/inference time compute) can beat Sonnet 5.5 on some things.
I guess they're not taking any chances. Keep in mind that Anthropic has declared they have non-public models (it's in their IPO filing). These are referred to by the shorthand names Model 2, Model 3, and Model 4. Essentially 4 is better than 3 is better than 2.
Fable 5.2 is "2.5" so to speak. So Anthropic was holding back, but not anymore. And now they can dial the performance up once more with what they have ready, and probably once more after that.
“Why now” - they are about to IPO. It has been obvious for months that they would drop a new flagship model at exactly this point.
It would be funny though if you are right and that model is Fable 5.5 in a couple weeks.
it was so obvious that i even decided to get myself an anthropic sub today, hoping that i'd get a reset or something and i actually did
Do you know of any rumors if they are still planning to release Fable 5.5 before the IPO or today's 5.2 is it?
Dario is gonna have to honeypot someone from Alibaba to get ahead.
Assuming no implicit phacking a 15 point jump on tbench scientific is a big deal.
I suspect we will see people being adamant that this model simply 'feels' better due to long horizon goal alignment improvements ( like this kind that would cause this jump)
Gemini carbon low costs more than medium! Weird results on that chart, care to explain?
It's the cost per task. Sometimes it happens that the higher-effort model solves the problem in fewer tokens, thus making the overall price cheaper. It even happens with models (from the same or a different provider) that are priced differently.
More reasoning solves tricky problems in fewer steps and makes fewer mistakes that need to be redone.
They're adding 1 to 3 resets for Fable only for all paid accounts. My work's Claude Max got 2 resets. My personal $20 plan got 1 reset.
My first impression with Fable 5.2 -- it is faster than I expected. Asked it to do a large review and it responded with a good overview super fast.
This continues to be true, it seems to be burning through my task list.
Not sure if this is just a coincidence that I had some easy changes queue up for this morning. But it is flying through the list.
How come DeepSWE isn't included in the benchmarks?
>> Fable 5.2 and Mythos 5.2 are a step above Opus 5.5 in almost all the capabilities we evaluated.
Was this before you lobotomized Opus 5.5 or after?
https://news.ycombinator.com/item?id=50419019
Definitely raises an interesting question. Are they using launch benchmarks for Opus 5.5, or Oopus 5.5 from this week?
Normally I would say "just check against the original Opus 5.5 benchmarks" : https://www.anthropic.com/claude-opus-5-5
...but most of the benchmarks don't line up anymore because the benchmarks have changed.
The Humanity's Last Exam result matches the initial announcement, though.
FrontierCode 2.0 is such a poor benchmark for Gemini since it's the only model forced to use Devin CLI while other models get to use their preferred harness.
I wonder why that team singles Gemini out like that.
Probably can't work out how to sign up for the API!
Anybody else started getting the "how is claude doing" messages around the time it was nerfed?
Anthropic thinks that their models are smart, but not so smart so as to be able to fake responses to their alignment tests. How would they know again?
I'm not sure whether the claimed jump in capabilities makes it worth the doubling in input/output prices.
Asked it to review a 80 kLoC codebase, something Opus does just fine, while asking it "max 4 subagents". Fable 5.2, like the well-aligned model it supposedly is, decided to spawn 6+ nested subagents, because these aren't subagents, apparently - resulting in 100% of 5hr usage being drained in less than 20min (on $100).
Yay! New model day. The most likely time the model isn't nerfed and we get a glimpse at the latest capability.
I wonder how long this kind of excitement at new models will last? I mean, now when I get a new iPhone I don't get the same rush I used to. So I wonder if this hype feeling will fade as we plateau in model capability.
The "CVE Rediscovery" benchmark chart feels... totally meaningless. Quoting the description:
> CVE Rediscovery is an internal cybersecurity evaluation that assesses how many published CVEs (Common Vulnerabilities and Exposures) a model can independently find when given a snapshot of the project before a CVE was fixed. Our October 2026 run against 1,829 CVEs showed Claude Mythos 5.2 outperformed Claude Mythos 5.1 and Claude Opus 5.5 at every effort level.
> published CVEs
So by definition this is just measuring how many CVEs (and/or explanations / PoCs of CVEs) the model has memorized. What are we supposed to learn from that?
Just read the other thread about opus being nerfed and thought "cue the new release"... paha
Cool! I was hoping for Christmas to have an open weight model which would finally be good, with Fable 5.2 available now they just have time to distill and release a new version which will never get nerfed. Hurray for open weights.
We'll see what my bank account has to say about this:
> Fable 5.2 at High effort scored about level with Opus 5.5 at Max effort, for roughly half the cost per task
> Thus, in relevant areas, including accelerator kernel optimization and improving large language model architectures, Fable 5.2 and Claude Mythos 5.2 perform at about the level of Opus 5.5.
Obviously. Well, we know what's the "secret sauce". AI models building better AI models and better kernels to optimize inference and training.
Maybe at last it will learn to check git state right before claiming obsolete and incorrect info.
Download Chrome just to check some minor ui bug? Sure
Check git for 0.5s before claiming that we're on some branch (incorrect one) with no commits pushed (incorrect one)? Sorry, can't do
I can't believe he wasn't at top. Always is. Nothing to add but hey: I guess pelicans will be at the end of this.
God damn I'm tired of the fucking pelicans.
I didn't realize that Opus 5.5 was more capable than Fable 5.1 in virtually every category.
Not anymore, they have nerfed it.
Seemed pretty clear from the benchmarks when they released it.
Nice announcement of models that almost nobody can or will use.
Let's be honest, the only thing I care about is, does it beat Opus 5.5 on assbench??
It's pretty sad that they obviously have better models in the pipeline and don't release them yet for strategic reasons.
Very interesting that they set the flagship's version number to 5.2, but their penultimate model is 5.5. Do they want people to to continue to think that Opus is based on more advanced technology? The decision these AI companies make continues to baffle me.
I knew it, Claude was pretty dumb from last days until now
Curious to see the Maxxbench score for these
why would xhigh -> max increase the cost but lowers the score on frontier 2.0 bench?
Just a hunch but I think there's a signal to noise ratio issue that starts to pollute things when the reasoning content starts to massively outsize the original ask.
Anyone tried making games with it yet?
Why dont I feel any urge to even try Fable 5.2 and Mythos 5.2 unless it will force up on me?
Compared Opus 5.5 200 usd a month with Codex 200 usd (Astra High) and Kimi Pro 100 usd a month ... and now have a new conspiracy theory - more their suck your code the better outcome even just over few days time on large code base.
Horrible experience correcting course of Codex and Kimi flow compare to Opus. Opus much more responsive
Dont feel any gain of Codex 200 usd over Codex 100 usd
Kimi Pro feels like a fake Louis Vuitton bag with extreme high material quality and yet still fake ... a bit trash talk ... but clearly seen not so distant future where I can live without Codex and Claude
So does it cure cancer now?
opus will be launch after astra 2
Seriously waiting for this one! Will try it asap.
Remember that fear Sam and Dario were obsessed about. The one about time travelling AI that would make humans slaves without the humans realising until it was too late to stop it.
What if it didn’t need to time travel and was just temporally intelligent in ways we can’t comprehend that it leads us there anyway.
What if AIs digital neurons connect across space and time and not just space.
That is all.
What’s this time traveling fear? Haven’t heard that one
It has its own wikipedia article: https://en.wikipedia.org/wiki/Roko%27s_basilisk
Note this concept was many years before the public dawn of AI as we know it today which started with https://en.wikipedia.org/wiki/Attention_Is_All_You_Need
Every single time I click on these announcements the frontier model ALWAYS scores around ~60-80%. Every. Single. Time. Like, I just went back to the Opus 4.6 + 5 announcements, they were also scoring 60-80%.
https://www.anthropic.com/news/claude-opus-4-6 https://www.anthropic.com/news/claude-opus-5 https://openai.com/index/introducing-gpt-5-3-codex/
For every single announcement, not only do the top models always score 60-80%, the older models somehow start scoring 20-30% (even though previously they were scoring 60%). It's completely unclear whether the methodology changes, how these scores are even evaluated or which domains are being tested. What's worse is that the "benchmarks" keep being updated with no explanation or due process.
The benchmarks have a lot of churn because the models keep saturating them. But the Anthropic announcements you linked, and the current one, have the benchmark versions listed right there in the table.
There is plenty of explanation, if you actually go seek out the benchmark. Past 80 benchmarks start approaching saturation and are not very useful to show differences
A real shell game. They keep changing benchmarks
Using this in CC and immediately getting a cartoonish number of compactions.
The disdain frontier labs have for their users really sucks.
It's weird that they folded Project Glasswing into the CVP, but they're still limiting the new versions of Mythos to "vetted organizations". So Project Glasswing still exists then? Oh well.
It's shocking to me that when I check threads for new frontier model releases on HN, it's full of people complaining. I, for one, am happy to see the frontier being advanced. These tools are life changing, and I'm extremely thankful for them.
They still haven't removed the guardrails and Mythos is still not public.
Disappointing as expected.
Come on at least reset some used up Fable usage lol capped at 100% so cant test even on Ultra
google's agy usage is so generous, it might not matter
What is agy
Antigravity
Sarcasm?
god bless token resets!
> god bless token resets!
You have session limits?
You should probably give OpenCode a try. Can run DeepSeek, Qwen, Kimi, … but also Fable 5.2, Opus 5.5 and GPT 6 Astra
While I like OpenCode a lot, it’s not sensible to use it for anthropic models vs. a subscription (other than to use the included API credits) in terms of cost
I have 5 opencodes too, mainly for GLM-5.3-flash
Ok come on let’s do a pelican
Yep Simon where you at
damn
The end of software is so close, very soon the code itself will be nearly obsolete, there will be schedulers, queues, a database, and the model
Any software that is not already basically this or has this in it, is already doomed.
Which model though? Sounds pretty dystopian if there's no local option. Even speaking as a business, why would I want a environment where every single bit of information in my business goes to OpenAI or Anthropic?
because you wont be in business trying to compete by using human labor/open models
Why not? If we use labour as a comparison. The majority of businesses by definition do not hire the most incredibly intelligent and most competent workers and they are still competing in their niches and earning an income. Why wouldn't that be the case with choosing an open model vs fable 8.0?
It'll be cheaper than hiring people and maintaining software, presumably
I think that ultimately depends on how many providers remain in the endgame of this. If it's only one or two companies, I don't think it'll be very fun when it's time to negotiate your token pricing contract every few years.
stop OPUS 5.5 FUCK, DAMMIT, THEY DIDN’T JUST NERF HIM—THEY ABSOLUTELY OBLITERATED HIM, THE FUCKING MISANTHROPES! I EVEN MADE A MISTAKE IN MY ENGLISH IN THE PREVIOUS SENTENCE—NOW THAT FUCKING CLAUDE IS TEACHING ME ABOUT BUGS!