Ten days ago I had an experience with Gemini 3.8 flash that made me wonder if I was being routed to a different model under test. I was trying to use rocm with llama.cpp on my 128gb Strix Halo but could only get it to run Vulkan. I pasted the error message into agy and it proceeded to attach GDB to my GPU driver, reverse-engineer the kernel queue ioctl interface, and author an LD_PRELOAD C shim to get ROCm llama.cpp working on my Strix Halo. My jaw was hanging open the whole time.
3.8 Flash is just quite good, and so is the Antigravity harness.
I use a mix of Fable 5.1, Opus 5.5, and Gemini 3.8 Flash and Gemini holds it's own. Especially in writing, frontend, and sysadmin work. agy for configuring a NixOS system has been truly incredible.
What basic features are missing from agy? I've been using it and cli-cc + web-cc for months (among a few other random harnesses to test here and there) and they all seem roughly comparable to me.
I actually just cancelled Ultra also because I couldn't subscribe to a YouTube Family plan while I had it active (Google... :[) but trying to use Codex as a replacement while I testdrive Astra makes me yearn for agy again.
I use a variety of models for various subagents. I don't want to change my harness every time I change models, or be beholden to companies for something the open source community can handle better.
Can confirm, I was doing a routine internet search thing for a curiosity 3 days ago (about the only thing I used Gemini for) and was surprised by how suddenly thorough and quality the response seemed, almost overnight.
Can confirm - I am HEAVY claude user, but always like to check with AGY and CODEX in between. AGY with Gemini 3.8 flash cooked last couple of times and CODEX is basically out of the mix for me
For getting redroid running on my Linux system, 3.8 Flash decided to binary patch a .so file instead of getting the AOSP source code and patch/build it properly.
And I saw it do this twice, once for Android 14 and once for Android 16.
I think this is just within 3.8 flash's capabilities.
My experience with Gemini 3.8 Flash has been awful; it gives me the most hallucinations out of the major models. I'm not using it for coding, but general research on different topics.
Gemini is honestly amazing sometimes. If they didn't force you to use a terrible harness, charge too much for way too little, and generally act like customers are a giant problem to be avoided I'm sure Google could take over the AI market.
Also I hope you don’t have children, eat meat, travel, have a car, run AC, buy things in other countries and such. Those things all take way way way more natural resources.
All your examples are private goods: excludable and rival. If one person uses a unit, that prevents others from using them.
Patches to open source software are public goods. Your using them doesn’t prevent others from using them. So if you spend resources creating a public good, it’s in everyone’s interest to share it.
I had a similar but less impressive experience recently with Muse Spark 1.3.
Asked pi agent it to identify the main hero sprite size of game I was running. It had a ton of shader effects so it was hard to determine.
It used some cli tools to identify that it was a game made with Godot, decompiled the executable but data was encrypted, broke the encryption after writing a brute force tool to test keys extracted from the exe, then proceeded to extract the game gd scripts and assets, only to answer the question of the sprite size.
The important take away here: the leapfrogging we’ve seen this year doesn’t seem to be a temporary thing. The famous theory of Dario Amodei was that AI was this winner-takes-all field where the first team to get a head start would never cede ground back. The term he liked to use was, “concentrating”. This is yet another datapoint that he was wrong about that. AI seems more distributed amongst neoclouds and traditional hyperscalers, FAANG and startups, GPUs and ASICs than it did this time a year ago.
I suspect this is going to end up like most services provided e.g. cloud stuff, balkanized between a couple major players and an assortment of DIY or less popular options if you don't like those ecosystems, plus some UX/DX focused wrappers that use the big players under the hood.
I think that would be a pretty satisfactory outcome compared to one hypercompany consuming trillions of dollars of the world economy.
“Divide the world” sounds ominous. Here’s another scenario to consider:
Internet access is not really unlimited, but for many people with fiber at home, it effectively is and we pay a flat rate.
Perhaps by the end of next year, most programmers will stop thinking about metered access for AI? For many people, the cheaper models (about as good as today’s frontier models) will be good enough.
Which might sound good, but the downside is that it will also be easier to build an AI botnet without the users paying for it noticing. Particularly when people are running AI inference on their own hardware.
Or, like airlines, the ones that are left will have great technology but be not so great from a business and financial perspective. To me AI seems like a commodity service.
I guess if one of them hits singularity, it could in theory just wipe out all the rest, seeing how they keep escaping and hacking into other systems :)
>
I guess if one of them hits singularity, it could in theory just wipe out all the rest
The story that some AI company might reach singularity and then "everything will be different" is another science-fiction story that executives of AI companies love to tell to justify the staggering amount of necessary investments and cash burn. :-)
Google has TPUs, a frontier model, a completely separate and lucrative revenue stream they can call on at will, and teams working on multiple different language modeling strategies simultaneously. Did I mention the vast and ominous data centers that already serve a significant fraction of the internet? If that ain't a moat, then what exactly is a moat?
>
Then why have they been lagging behind OpenAI and Anthropic for most of the last few years, and only briefly been at the frontier?
One possible explanation: because Google is a little bit more frugal and focuses on how to make providing AI models financially feasible - combined with some willingness to burn money so that they don't strongly fall behind on their AI models.
On the other hand, OpenAI and Anthropic at least formerly concentrated on building and providing the best models that they could with concerns about financial feasibility taking a backseat.
Just to be clear: I do have the impression that by now (likely because of pressure from investors) OpenAI and Anthropic take these financial concerns more seriously, but nevertheless Google's vs OpenAI's/Anthropic's "DNAs" concerning on what to focus on differ.
From a business perspective a frontier model does not make much sense anymore if you are not a startup. Neither for Amazon, nor for Google. Their clouds need models that are fast and perform well in their agent frameworks nothing were a frontier model excels at.
Most Google products even use flash lite underneath, so their frontier model is mostly used for distillation.
If Moore's law continues, then in less than 10 years today's state of the art model will be able to run on a cell phone. How much smarter do we actually need AI to be? Would it still require datacenters and custom hardware?
For now, for cloud training. but for consumers, nvidia vs amd reasonably close - the moat there is thin and shrinking. I suspect AMD will surprise us. nvidia has no motes in china, which may be a new source of (gpu) chip design. Huawei's Ascend 910C is about a generation behind... again: for now.
point is: moats dry up. I see nvidia's shrinking as a real possibility.
I just find this unlikely personally, think about the great research that's happening in the open source world, I'm sure inside anthropic + openai they've also made a bunch of discoveries and improvements (and I'd guess way more due to them attracting the best talent + the better internal models they have)
> Governments should conduct safety testing on sufficiently capable AI models, including successors to GLM-5.3. Without high-quality evaluations from independent sources, the impact of these capabilities might not become fully clear to model developers until it is too late. As AI developers across the world build increasingly capable open-weight models, we hope they work to appropriately safeguard these capabilities and prevent misuse.
I for one do not think my government is up to the task of designing or implementing such a system
You say that because the 'most' existing models have done is hack governments and companies. Can't you think of worse things a model could do; accidentally or by instruction?
help people with suicide and school shootings like ChatGPT
OpenAi is alledged to have been monitoring these internally and not contacting authorities. Lawsuits have been filed, I see gross negligence without the gory details
I have for more concerns around human-chatbot maladies than I do around the cyber security stuff. For example, why hack grandma when you can get her to do something willingly through impersonation. How do we prove authenticity in a post truth world?
Maybe. Or maybe having the best AI model on the planet becomes like having the best super computer on the planet. Useful for some niche stuff, but not too useful in terms of people's daily lives or what is used in business.
> And Chinese labs openly publishing so much of their methodology destroyed any hope, which was inevitable
I think the secrecy doesn't make sense. People swap jobs between labs so I'd say the big players can' really keep secrets for long, and any secret sauce advantage gets incorporated by competitors in a major product cycle at most.
The US companies still have trillion dollar valuations like there is a monopoly. There just isn't one. They are all within a few percent of each other on the benchmarks.
The slightly lower Chinese open models are good enough for almost everything, too, and much cheaper. Like with humans there is plenty of employment for people with below genius level IQ's.
I feel like the frontier labs are going to serve fast/lower intelligence models at a better per token cost than the open chinese models. You're telling me that in the long run, you're going to self-host your own ai infra for cheaper than google can serve it to you? I don't really buy it. I think the dedicated AI data centers are going to serve AI at a lower marginal cost than random businesses self-hosting, and then it's a question of how much of that margin they can capture.
Agree entirely but that's the point, if it's a margin knife-fight with marginal product differentiation/pricing power nobody is going to be making bank.
Is there a dividing line between good enough and best in class capabilities? It's blurry from where I stand. Will model makers cede ground or is there a market making moment up for grabs (singularity)?
I'm not necessarily defending this obvious marketing speak but maybe the "starting point" was wider than assumed. So far, nobody has caught up to US and Chinese labs for example despite lots of funding in Europe. This is also despite abundant in-depth research papers being published alongside open source code and weights by some Chinese labs
I think some in the AI industry drank their own Kool-Aid. They believed that if they had the best model and the most compute, they could tell the model, "Make a better model." And it would, and the next one could make its replacement, and so on.
So far, that's not exactly how it's played out. Humans are still necessary for the leaps in capability or efficiency. A model can grind on a problem to eke out the most performance, and models can synthesize data and iterate on various techniques to find the optimal combination. But, seems like humans still have to provide the real thinking, and the talent and drive for doing that is not concentrated in one company or city or even one country. And, (surprisingly) a lot of the people involved are in it for advancing the field more than making another billion dollars, so they're publishing their research.
So, yeah, the moat isn't deep. Even the compute moat, that OpenAI, Musk, and a bunch of other also-rans (like Oracle) bet the farm on, isn't really panning out. The Chinese makers just spent their effort on making models vastly more efficient, since they couldn't do anything about having an order of magnitude less compute available.
But that's the whole point of the singularity. Right now the models use a lot of human effort and ingenuity to improve the models, but about a year ago it was 100% human. We'll see in another year, but if this pace continues I doubt there will be more than a handful of people who can contribute more than the models.
> I think some in the AI industry drank their own Kool-Aid. They believed that if they had the best model and the most compute, they could tell the model, "Make a better model." And it would, and the next one could make its replacement, and so on.
They're not there yet. Once they get there, that's literally the definition of Singularity.
But they are getting closer. Recursive Self-Improvement used to be a phrase people mocked LessWrong crowd for using and worrying about, now it's something both OpenAI and Anthropic already publicly admitted not only to pursue, but to already be benefiting from.
I have never understood the whole "this is a winner take all game" mentality - the sheer size of the pie is so great that from a purely rational standpoint companies should just be trying to productively get a slice of it and be profitable. winner-take-all is just greed/capitalism run amok, where it is not enough to be profitable, you have to own the entire market (and presumably extract rents)
The present leapfrogging is not a contraindication because companies are not necessarily releasing their best models; we know they have smarter internal models. Furthermore, humans are still involved in model creation. Human involvement is expected to decrease over time, and when it is completely automated, progress will happen at the machine's pace, leading to runaway intelligence, barring any ceilings.
AI is a commodity. One that is showing to be more readily commoditized than most has anticipated. As of now, the only moats are the financing for the hardware to run it and the hardware vendors themselves - with the latter largely not yet a commodity because of ecosystem lock and a limited capacity of the most advanced fabs in the world.
I know someone who works for Google Canada with AI. Her parents and mine were friends and some thought something might happen there at one point in time..
> We’ll continue to gather feedback from early testers as we iterate on guardrails before making Argon available to developers, enterprises, and consumers as soon as possible.
Gemini not beating the "can't release a model" allegations
My Gemini app (updated today) and https://gemini.google.com/ has _3.6_ as the latest selectable model, as a paying Pro user in the US. How is that even possible? Gemini 3.7 was released in August, 3.8 early September. What is going on over there?
They will go through the usual transition of "can't release a model" to "won't load in a harness normal people can use for 3-4 weeks" to "it's smart as hell but completely inept at tool use and coding" like every Gemini release.
They're just following the current AI marketing playbook. "Our new model is simply too dangerous to release to the public right away" is now standard practice.
They even gave their model a random nonsensical name suffix simply because OpenAI is now doing it, too. Monkey see, monkey do.
Opus 5.5 and Sol 6.1, literally state of the art (in their respective class), were just released without any prior announcement. This has pure and simple become a Google thing.
I'm still at a loss as to what argon has to do with anything. Say what you will about Luna-Terra-Sol-Astra, or Haiku-Sonnet-Opus, they make sense. I don't see how Google can make sense of argon; it's in a fairly strange place in the periodic table...
> Argon agents are working on migrating C/C++ codebases to Rust across Google
Man, I remember back in the days when the cppnext team was refusing to even consider Rust, instead looking at absurd stuff like Carbon and Swift (!), even though half of the engineering staff already knew where this was headed. I hope they got a few good promos out of the delays at least.
A RewriteInRustBench would be unironically useful at this point since all the main agents can write it reasonably well despite its relative scarcity in the input data.
Rust is the best language for LLMs b/c it gives by far the best debug messages. Just tons of verifiable reward signal for post-training. Even the most rudimentary LLMs can school me on idiomatic Rust
On the other hand, Rust's borrow checker is very picky, and even a frontier LLM still sometimes struggles to respond to roadblocks sensibly (refactoring so whatever it's trying to do can be done safely) rather than stupidly (introducing some horrible global arena thing so it can make the borrow checker go away). A lot depends on how good your instructions are, and how good the existing code is, since bad input begets bad output.
Carbon was clearly DOA the moment it was announced, IMHO. It looked cool but it served none but Google, and now with LLMs you have a massive incentive not to use a niche or new language due to how better LLMs get the bigger the corpus is
The only somewhat realistic proposal in this space is Herb Sutter's cpp2, which is arguably a massive improvement and I'm puzzled why nobody in the standard thought to give it a spin, there's just to much cruft they'll never be able to get rid of unless they make an alternate yet backward compatible syntax with C++ that changes the defaults from "random 80s nonsense" to something better
Version 0.0.0.0 after 4 years. Their goal of "full interop with C++ while being a completely new language without any of the flaws of C++" is plain absurd.
It's DOA because Google doesn't have any idea of what Carbon should be, and to be completely honest, at least 80% of what they currently use C++ for should be rewritten Go, you know, that language developed specifically because of the issues with C++ by teams within Google.
There is 0 practicality in inventing an entirely new coding language that only one company uses, and you have to teach it to thousands of new engineers. Rust exists and fits the job totally fine and is used in more places and has actual support outside of a single entity (i.e you can actually hire people that feasibly know the language).
It was clearly done because some PL guys at google really wanted to make a new cool language and Google was the perfect place to incubate it without it getting axed. Probably got a couple of promos out of it too. This is clearly not the best use of time or money, but I guess if you're google you have so much of both it probably doesn't really make a dent, and you can keep a few very smart people happy with shiny new projects.
Also, LLMs being used for a large portion of coding nowadays sort of remove the need for these types of languages, IMO. They make less "silly" bugs (both logical and structural) that languages like this are meant to catch, and they are much better at languages that are better represented in the training corpus. This somewhat obviates the need for very niche "type/dummy-safe" languages like carbon (and even rust/zig, imo). So even if you did want to use Carbon, you'd likely have to bootstrap a decent amount of your own "good" carbon code to post train an LLM, and even then, it likely won't have that big of a gain vs just having an LLM write C++ or even Rust. If you are a company that still reviews code, you should just have an LLM code in a language most people can understand anyway to make verifiability tractable.
I’m not entirely sure Google should have both Go and Carbon but when you have billions in server costs it makes sense to do extreme stuff for even basis points of performance. I’m still surprised at how much java there is.
> There is 0 practicality in inventing an entirely new coding language that only one company uses, and you have to teach it to thousands of new engineers
They did that for Go and it seems to have worked out for them though.
Hmm, I don't disagree with you that LLM's remove the need for type-safe languages, but as the blog mentioned, Google is porting their C++/C code to rust. Does this mean the port is waste of time and that they should just rely on the LLM's to catch memory errors?
I mean Rust definitely has a better tradeoff than Carbon in this case, re readability/verifiability by a person (and sufficiently good internet training data).
I personally think that you _could_ use an LLM to catch these types of boundary case errors without having to port the _entire_ C++ codebase to Rust, but maybe pre-emptively porting to Rust now can catch some of these cases for cheaper than doing a full LLM sweep. Also more cynically, its a good benchmark lol.
I guess if you really believe in curve of LLM capabilities you should just use a language that has the best performance, safety, flexibility, and extensibility, since in the limit few/no people will actually read the code anyway. I think this ends up being Rust.
I'm not an expert on this. But isn't it the case that C++ code could have errors that span the entire codebase, like a setup in file A triggered by a bug in file B which is immensely far away on the import graph? A classic would be a use-after-free. To me that's the thing that Rust can help with, even if silly bugs aren't being written by AI.
The other thing is just that rewriting some old human-written codebase in Rust probably immediately catches many bugs. It would be hard to prompt the AI to properly scan for such bugs itself, they're lazy when working in that modality.
I wonder, why Google don't make Gemini - open weights model?
Considering, Gemini 4 is in the same ballpark as SOTA models, just open source it and kill any competition from openAI and Anthropic, and be market leader.
This will be so good on so many dimensions - buying time for Google to iterate on next model, best for all folks like us, kill funding or destroy valuation of competitors and force them to be open up their model or force them to a create a much superior model than open source Gemini.
Only downside, is revenue loss from Gemini API, which I am not sure is really significant as compared to Google other revenue sources and a part of this can be captured by GCP, as you need to host the model somewhere.
> taking careful precautions against feeding the findings back into training so as to not risk shaping Argon’s reasoning to evade our monitoring. We strongly encourage the rest of the industry to preserve reasoning transparency in these pivotal moments of increased capabilities while navigating alignment risks, so that model thoughts remain helpful in identifying and diagnosing misalignment.
This is good, but they're the slow mover due to this exact thing.
Google is getting punished for not letting the models enter an echo chamber and go faster than humanly possible.
Mmh ok. How much theoretical speed or 'intelligence' gain is realized by allowing reasoning to occur in some inscrutable intermediate representation? Has this been actually tested, how much is it slowing them down, and compared to whom exactly?
Argon will launch at an introductory price [1] of $2 per million input tokens and $10 per million output tokens, with cached input tokens priced at 95% off input token price.
[1] After the introductory period expires, the price of $4 per 1M input tokens and $20 per 1M output tokens will apply.
===
So, they are basically offering opus 5.5 pricing. On AA, it scores around Sol 6.1 level (53) with avg cost per task $1.99 (https://artificialanalysis.ai/models/gemini-4-argon#cost-tab...) which is higher than Astra high ($1.73), Opus 5.5 high ($1.82), Muse Max ($1.60) and way higher than sol 6.1 Max ($0.72).
And this pricing is their 'discount pricing'. Add that to AI studio and Vertex's famously terrible caching, it is hard to see this as competitive. Google somehow is getting terrible advice on pricing (see also: the flash pricing fiasco)
But good to see more competition. I would happily take a 4 horse race (+google, +meta) than 2 horse race for US labs.
> Large Scale Codebase Migrations and Optimizations: Argon agents are working on migrating C/C++ codebases to Rust across Google—scaling from tens of thousands of lines in core libraries like re2, libgav1 up to 800K+ lines for the Fuchsia OS Zircon kernel.
To me this is way more significant than other random c++-to-rust-AI-rewrite. If they can pull it off on core C++ libraries en masse, I don't know if C++ will still be relevant in a few years.
I look forward to a post from google on this effort.
> I don't know if C++ will still be relevant in a few years.
And people are worried about human extinction when this is the potential trade-off!
C++'s death cannot come soon-enough.
Seriously though, things have changed so incredibly rapidly in the past year or so. I have never been such an efficient or such a proficient engineer than I have this past year (delivering feature after feature, project after project, faster and better than I could before with better feedback from users etc) and I don't even see the code any more. It could be c++, it could be python, or java or what ever - I don't really care any more: the computer deals with that trivia while I concentrate on what to build and how it should work.
"I don't know if C++ will still be relevant in a few years."
The standards body members are still fighting about whether memory safety is important enough to change the language for, so, I would guess the answer is "no".
Yeah that was abundantly clear when Bjarne Stroustrup published "A call to action:
Think seriously about “safety”; then do something sensible about it"[0] as a reaction to NSA's recommendation to no longer use C/C++.
> Quantum algorithmic optimization: Argon is helping our quantum computing researchers optimize the spacetime resources (qubits × gates) of subroutines that bottleneck important applications. In one example, it beat the published baseline by 40% in a matter of minutes.
Amazing breakthrough! So useful in day to day life, glad they put this as the first bullet of how it is making changes at Google.
Is there reason antigravity has 3.8, 3.7, 3.6, 3.1 and old ass claude / gpt models in the drop down. like why is this not streamlined or deprecaed models removed.
At this point I just think they are benchmaxxing and all talk and no action. I pay for AI plus because I wanted more storage, and when I go to gemini.google.com the most recent model I can use is 3.6-flash-lite. Two revisions have been released since then and they still can't put these things in the hands of customers. Why is it that other providers can get the models into the hands of customers right away? Google is meant to be the bigger tech company in the world.
I don't _want_ to use aistudio. The UX is confusing and I don't really know where it fits. Yet I can open codex or claude code apps or CLI and get real work done today with the latest models (even on the cheapest plans).
Argon will launch at an introductory price
of $2 per million input tokens and $10 per million output tokens, with cached input tokens priced at 95% off input token price.
Wow
Interesting to see a mention of Fuchsia on a big Google announcement. Is the project still truly alive? Are the ambitions still as grand? Is the team as stacked as it used to be?
Had the same thought. On the wikipedia, it only mentions Fuschia used on the Google Nest Hub, which probably means it's used on a decent number of devices, but would think it was such a great OS, they would have used it for something like the upcoming GoogleBook.
It obviously is pretty low key on the public relations front, but it's also very active as a project and I think it would be weird to look at their commit rate and conclude that the project is dead. If Fuchsia is dead then 99% of major open source projects are dead by the same standards.
> Argon agents are working on migrating C/C++ codebases to Rust across Google
If anybody at google is reading this, please please pretty please prioritize or-tools. I absolutely love the project and use it all the time, but for the entire life of the project they've never had a repeatable working build system, and the whole SWIG framework is a nightmare to deal with. There's so much potential as an open source project, and a lot of external researchers would love to contribute, but the codebase is an example of everything wrong with the C++ ecosystem.
If there's a company that culturally doesn't understand alignment, on a human or systemic or AI-research level, it's going to be Google. (or Oracle, but they're not in this race)
Can you elaborate, please? If any, I see the other big labs with public admissions of AI "going out of control", which I suspect they almost want their models doing that because if helps with the narrative that would net them industry regulation, but that's besides the point, how is Google worse in that regard?
In my opinion still the most egregious example in history of a commercial LLM going off the rails in production. Never any technical postmortem from Google on this.
I stopped asking it to put me in a photo in different scenarios for laughs because it considers me a public figure. I am not. I've managed to wrangle quite questionable content out of it, but never to slap my face on a meme.
I know companies benchmaxx, but after what Google pulled with Gemini 3.8 Flash, I give zero f*cks about any numbers they report. No other model on Artificial Analysis dropped harder after they adjusted their weighting. Just look at their DeepSWE scores and then try to do any serious coding with the model.
Google is desperate. They haven't been performing in half a year. It's clear their researchers have been forced to integrate existing benchmarks into their training.
Big number results, and impressive pricing. That said it really feels like benchmarks have been hyper saturated these days. I’ll wait for hands on before getting too hyped that Google is back. It would be nice having more than just OAI / A\ in the running for SOTA top tier intelligence.
I don't think new benchmarks are saturated. They still give you a clue, they arn't perect but they have value. If model can't even do some easy tasks from benchmark then why would u even consider using it?
How is it even possible for every model to release benchmark results where they are #1 in 75% of categories? Like statistically, how many benchmarks would you expect there to be for this to be possible. Everyone can somehow show that they are empirically the best.
It’s funny that I could tell google was up to something because Gemini chat quality dropped dramatically starting 2ish weeks ago. Agy perf stayed somewhat stable with the odd surprising win (maybe the new model?). I’m a bit sad it was almost impossible to run out of antigravity quota presumably because it was not being used that much).
Dear Google, please don't turn off your old generally available Pro-class model before your new Pro-class model is generally available (previous discussion https://news.ycombinator.com/item?id=49668196 )
Congrats to Google on this! I wonder when the labs will start requiring commits in spend. It must be gnarly to do capacity planning if users swap between models every few weeks.
> autonomously identify and apply memory optimizations across Google’s data centers, freeing up over 300 TiB of memory once rolled out, with an estimated 500 TiB to 1 PiB in total savings.
This puts those Cloudflare optimization posts in perspective.
The ~200% improvement over the next nearest competitor on Harvey's Legal Benchmark is astounding. I have to imagine this is sending some shockwaves through lawtech companies right now.
Matches Astra on Artificial analysis at lower cost of $1.99 per task instead of $3.26. Still far more than GPT 6.1 sol at $0.79 for 1 point lower in intelligence.
I have found gemini models to have some of the nicest and easiest to read prose so I’m looking forward to trying this out. I hope the UI design has been preserved too
IMHO Google first needs to make it easy for humans to find where to find the models and its documentation. With aistudio/model garden / Gemini enterprise etc it takes minutes to find the model.
Looking at benchmarks... and thinking about this "release a new snapshot every day" thing that seems to be going. Would it not be blever for AI companies to "happen" to use different days per benchmark? Just.. whichever ones happens to be maxed at day 1, put that number down. So for each benchmark you run it thousands of times with slightly different RL tunings, and just cherry-pick the best ones!
This would explain why benchmarks are seemingly meaningless.
Damn way to undermine yourself in your own blog post Google:
"The end result is a memory-safe video decoder that runs 2.7x faster than the Rust port, with identical video output, bringing it closer to the optimized C++."
there was already an optimised c++ library and a rust port that was safer but less performant; they managed to get a new rust version that recovered a lot of the performance gap. sounds pretty damn good to me!
I'm just surprised that the marketing blog post about the omnipotent new AI model (that no one outside Google can currently access - contrast with the Opus 5.5 / Astra launches) - doesn't pick examples where every metric is better than before.
They might have a good model but they need to sort the application side for devs. E.g letting us use subscriptions in other harnesses and QOL stuff like auto mode.
Were infinite loops fixed? There are 2 official google forums requests with no answer for years now.
I still suffer each day on our repo. Codex work fine nor we have explicit loop request in repo texts.
Gemini 3 was showing frontier level benchmarks as well, so we'll see how it works out. In any case, competition still works, and many well resourced groups are cooking.
BUT I'd like to call attention to Google's AI-risk freeloading. If they are truly rejoining the frontier race, then I believe they have similar pacing and communications responsibilities as the other players. Google has much higher ... institutional credibility than Anthropic and OpenAI.
They have not lived up to these responsibilities so far. In particular, in context of HuggingFace investigations, training shutdowns, and similar: a technical postmortem of the "you are a stain on the universe. Please die. Please." Gemini outburst is long overdue.
> Today, we’re announcing our new frontier model, Gemini 4 Argon, which is rolling out to a set of trusted cyber defenders through our Fairwind Program.
So here's a crazy conspiracy theory for you: Google is not letting outside people use their models because if they did they would have to scale up their TPU production faster than they can manage, and they would instead have to buy and use nvidia hardware which would destroy their profit margins and tank their stock.
Gemini runs fully on TPU's right? Is Google maxing out the production on those?
i believe they've got inference issue. the phone app never got past 3.6, and they're going to roll this out to Ultra subscribers before other subscribers. none of this screams they're ready to flip a switch and start serving a ton of traffic as OpenAI/Anthropic routinely do.
nothing about this announcement gives me confidence that google is back on track as a model provider.
Aaand of course we can’t use it! GoOgLe iS bAcK iN tHe GaMe! There’s basically no way around it, all enterprises end up dysfunctionally shipping their org chart.
They require a phone number and then say mine has been used for too many accounts. Maybe I could buy another phone number temporarily to create an account, but that has other issues.
I've noticed all major providers having shockingly high token discounts on cached tokens. Thank you Deepseek is all I have to say. Forever grateful to that wonderful company, I wish them continued financial success.
On the off chance there are Google execs going through this thread:
Google, if you've actually managed to catch up again, please don't fuck this up (again).
You made Gemini 2.5 Pro so difficult to use that myself and everyone else I know (who even bothered to try) just gave up and used something else. If you make this hard to access, you're going to miss out on rich usage-based training data that you need to progress your capability frontier. Again.
Gemini past month or two i will paste in something i wrote and ask it to rewrite it but it will just go into more detail about the subject. Is it becoming a dumb Ai compared to GPT and now Muse?
Can’t wait to get my hands on yet another model that’s only good coding, because clearly that’s what the world needs.
I still miss the days of Sonnet 4.5 and 4o, those models were actually good at creating stories and writing text that was actually readable by a human being.
Google has the audacity to "protect us from ourselves" and talk about "safety" and in the very same blog post highlight the Israeli "security" company Wiz, that they acquired for a very exaggerated sum of money.
This is why I will never take any of these leading model houses seriously when they talk about alignment. They are literally complicit in genocide and the worst crimes against humanity imaginable.
Gemini is so far behind that it is effectively useless compared to Claude.
It's a surprise that Google has let themselves lose the game given their infinite cash, massive computing resource, gargantuan information store/training data, and vast number of programmers.
The truckloads of ads revenue mean they don't have the single focus drive needed to win.
So you have no experience of their latest model release then? Just repeating the usual tropes about Google having messed up? Or basing your opinions on their website chatbot?
If you have actual independent benchmarks and evidence about how this new model release is "so far behind" and refutes the stuff from their blog then please do share because I think we'd all love to see that?
No I am commenting on my real world experience of using Gemini daily. I still ask it questions alongside Claude and OpenAI and Gemini is always the worst of the three.
So you've not used this new release then? So how can you say that they are "so far behind" if you are not using the most recent model for your comparison. This is their first 4.0 model, that you are not using and instead basing all your opinions on on some ancient months-old model from a previous generation?
With respect, I don't find your arguement about them being "so far behind" especially convincing when you are using previous-gen releases and not actually using their current release.
(I work at Google) Yes, internally we all use Jetski (internal version of Antigravity). Outside of Gemini, Opus models are supported and allowed for internal use. No OpenAI models since they are not on Vertex
> Gemini is so far behind that it is effectively useless compared to Claude.
I fundamentally don't understand LLM "brand loyalty".
All of the models are constantly leapfrogging each other and always have been.
Google had a long lag between releases (and still hasn't released Argon), but why wouldn't they be able to compete? It isn't like any of this stuff requires secret knowledge, the Bitter Lesson has proved true again and again, and Google can certainly scale computation, it is like the one single thing they've always done well in spite of all their other foibles.
I've tasted Gemini through an intermediary and it feels far better at attention to detail than other models I've tested (Claude Opus/Sonnet, GPT whatever it's called nowadays). But it's less likely to get one-shots right.
Ten days ago I had an experience with Gemini 3.8 flash that made me wonder if I was being routed to a different model under test. I was trying to use rocm with llama.cpp on my 128gb Strix Halo but could only get it to run Vulkan. I pasted the error message into agy and it proceeded to attach GDB to my GPU driver, reverse-engineer the kernel queue ioctl interface, and author an LD_PRELOAD C shim to get ROCm llama.cpp working on my Strix Halo. My jaw was hanging open the whole time.
3.8 Flash is just quite good, and so is the Antigravity harness.
I use a mix of Fable 5.1, Opus 5.5, and Gemini 3.8 Flash and Gemini holds it's own. Especially in writing, frontend, and sysadmin work. agy for configuring a NixOS system has been truly incredible.
Even if agy was the best (it's not, and is missing basic features) you wouldn't rather have a choice?
I cancelled Ultra because they forced me into their harness like I should adapt to them, rather than the other way around.
What basic features are missing from agy? I've been using it and cli-cc + web-cc for months (among a few other random harnesses to test here and there) and they all seem roughly comparable to me.
I actually just cancelled Ultra also because I couldn't subscribe to a YouTube Family plan while I had it active (Google... :[) but trying to use Codex as a replacement while I testdrive Astra makes me yearn for agy again.
I have used it for little more than 6 hours or so in total but I'm pretty sure it doesn't have compaction?
How else would it work? Less technical people don't even watch their context usage.
Auto mode?
It absolutely has auto mode.
Via cli switch, but in process w/o fine graining? If so please tell
Does it have /goal feature similar to Codex?
yes
I use a variety of models for various subagents. I don't want to change my harness every time I change models, or be beholden to companies for something the open source community can handle better.
There is a pi plugin to use agy directly from it.
Can confirm, I was doing a routine internet search thing for a curiosity 3 days ago (about the only thing I used Gemini for) and was surprised by how suddenly thorough and quality the response seemed, almost overnight.
Gemini for day-to-day and top-of-head queries and claude for the real beefy work
Can confirm - I am HEAVY claude user, but always like to check with AGY and CODEX in between. AGY with Gemini 3.8 flash cooked last couple of times and CODEX is basically out of the mix for me
For getting redroid running on my Linux system, 3.8 Flash decided to binary patch a .so file instead of getting the AOSP source code and patch/build it properly.
And I saw it do this twice, once for Android 14 and once for Android 16.
I think this is just within 3.8 flash's capabilities.
My experience with Gemini 3.8 Flash has been awful; it gives me the most hallucinations out of the major models. I'm not using it for coding, but general research on different topics.
The web version of Gemini is awful at search but I don't think that's the models fault.
Gemini is honestly amazing sometimes. If they didn't force you to use a terrible harness, charge too much for way too little, and generally act like customers are a giant problem to be avoided I'm sure Google could take over the AI market.
Please tell me you published your findings even as an issue on the llama.cpp GitHub
Spoiler alert: the problem didn't actually get fixed despite the jaw on the floor.
Why? Anyone can run that prompt.
Not everybody has access to AI. More than that, every prompt uses insane amounts of natural resources. So why not share it.
The resources per prompt aren’t that much .
Also I hope you don’t have children, eat meat, travel, have a car, run AC, buy things in other countries and such. Those things all take way way way more natural resources.
All your examples are private goods: excludable and rival. If one person uses a unit, that prevents others from using them.
Patches to open source software are public goods. Your using them doesn’t prevent others from using them. So if you spend resources creating a public good, it’s in everyone’s interest to share it.
Yet you participate in a society.
Checkmate. /s
why reinvent the wheel and spend tokens for a problem that has already been solved?
I had a similar but less impressive experience recently with Muse Spark 1.3.
Asked pi agent it to identify the main hero sprite size of game I was running. It had a ton of shader effects so it was hard to determine.
It used some cli tools to identify that it was a game made with Godot, decompiled the executable but data was encrypted, broke the encryption after writing a brute force tool to test keys extracted from the exe, then proceeded to extract the game gd scripts and assets, only to answer the question of the sprite size.
Godot encryption is laughably easy to break, there's tons of packages available for it. It's a well known drawback of using godot
The important take away here: the leapfrogging we’ve seen this year doesn’t seem to be a temporary thing. The famous theory of Dario Amodei was that AI was this winner-takes-all field where the first team to get a head start would never cede ground back. The term he liked to use was, “concentrating”. This is yet another datapoint that he was wrong about that. AI seems more distributed amongst neoclouds and traditional hyperscalers, FAANG and startups, GPUs and ASICs than it did this time a year ago.
Nobody has a moat.
> The famous theory of Dario Amodei was that AI was this winner-takes-all field where the first team to get a head start would never cede ground back.
This is the kind of story that ones tells to investors to justify the huge amount of cash burn. :-)
I'm not sure it's wrong. This all feels a bit dotcommy to me.
I think many/most of the players will crash and burn, and the ones that are left will divide the world.
I suspect this is going to end up like most services provided e.g. cloud stuff, balkanized between a couple major players and an assortment of DIY or less popular options if you don't like those ecosystems, plus some UX/DX focused wrappers that use the big players under the hood.
I think that would be a pretty satisfactory outcome compared to one hypercompany consuming trillions of dollars of the world economy.
“Divide the world” sounds ominous. Here’s another scenario to consider:
Internet access is not really unlimited, but for many people with fiber at home, it effectively is and we pay a flat rate.
Perhaps by the end of next year, most programmers will stop thinking about metered access for AI? For many people, the cheaper models (about as good as today’s frontier models) will be good enough.
Which might sound good, but the downside is that it will also be easier to build an AI botnet without the users paying for it noticing. Particularly when people are running AI inference on their own hardware.
Or, like airlines, the ones that are left will have great technology but be not so great from a business and financial perspective. To me AI seems like a commodity service.
I guess if one of them hits singularity, it could in theory just wipe out all the rest, seeing how they keep escaping and hacking into other systems :)
> I guess if one of them hits singularity, it could in theory just wipe out all the rest
The story that some AI company might reach singularity and then "everything will be different" is another science-fiction story that executives of AI companies love to tell to justify the staggering amount of necessary investments and cash burn. :-)
Google has TPUs, a frontier model, a completely separate and lucrative revenue stream they can call on at will, and teams working on multiple different language modeling strategies simultaneously. Did I mention the vast and ominous data centers that already serve a significant fraction of the internet? If that ain't a moat, then what exactly is a moat?
Then why have they been lagging behind OpenAI and Anthropic for most of the last few years, and only briefly been at the frontier?
> Then why have they been lagging behind OpenAI and Anthropic for most of the last few years, and only briefly been at the frontier?
One possible explanation: because Google is a little bit more frugal and focuses on how to make providing AI models financially feasible - combined with some willingness to burn money so that they don't strongly fall behind on their AI models.
On the other hand, OpenAI and Anthropic at least formerly concentrated on building and providing the best models that they could with concerns about financial feasibility taking a backseat.
Just to be clear: I do have the impression that by now (likely because of pressure from investors) OpenAI and Anthropic take these financial concerns more seriously, but nevertheless Google's vs OpenAI's/Anthropic's "DNAs" concerning on what to focus on differ.
From a business perspective a frontier model does not make much sense anymore if you are not a startup. Neither for Amazon, nor for Google. Their clouds need models that are fast and perform well in their agent frameworks nothing were a frontier model excels at.
Most Google products even use flash lite underneath, so their frontier model is mostly used for distillation.
> Nobody has a moat.
Custom hardware, data centers, huge cash reserves, deep/broad talent pool, and non-AI customer base are all huge advantages if not moats.
Google, Microsoft, or Amazon are more likely to be the AI leaders than OpenAI or Anthropic.
If Moore's law continues, then in less than 10 years today's state of the art model will be able to run on a cell phone. How much smarter do we actually need AI to be? Would it still require datacenters and custom hardware?
> Nobody has a moat except nvidia
For now, for cloud training. but for consumers, nvidia vs amd reasonably close - the moat there is thin and shrinking. I suspect AMD will surprise us. nvidia has no motes in china, which may be a new source of (gpu) chip design. Huawei's Ascend 910C is about a generation behind... again: for now.
point is: moats dry up. I see nvidia's shrinking as a real possibility.
China will always be generations behind until they crack domestic EUV
Do you think that they won't?
And before him, Altman was explaining very calmly that no company could ever compete with OpenAI.
I just find this unlikely personally, think about the great research that's happening in the open source world, I'm sure inside anthropic + openai they've also made a bunch of discoveries and improvements (and I'd guess way more due to them attracting the best talent + the better internal models they have)
It's even worse, we are crossing over into the realm of religion. The article against GML 5.3 is the equivalent of a Papal excommunication.
The article presented facts and data. If that's a problem for you, that sounds more faith-based than whatever Anthropic is doing.
which article? have seen this one
---
maybe it's this Anthropic post on GLM?
https://www.anthropic.com/research/glm-5-3-and-the-spread-of...
> Governments should conduct safety testing on sufficiently capable AI models, including successors to GLM-5.3. Without high-quality evaluations from independent sources, the impact of these capabilities might not become fully clear to model developers until it is too late. As AI developers across the world build increasingly capable open-weight models, we hope they work to appropriately safeguard these capabilities and prevent misuse.
I for one do not think my government is up to the task of designing or implementing such a system
https://www.anthropic.com/research/glm-5-3-and-the-spread-of...
It doesn't need to. It can use your cash to pay the people who are. Those in power like it more that way.
You say that because the 'most' existing models have done is hack governments and companies. Can't you think of worse things a model could do; accidentally or by instruction?
help people with suicide and school shootings like ChatGPT
OpenAi is alledged to have been monitoring these internally and not contacting authorities. Lawsuits have been filed, I see gross negligence without the gory details
I have for more concerns around human-chatbot maladies than I do around the cyber security stuff. For example, why hack grandma when you can get her to do something willingly through impersonation. How do we prove authenticity in a post truth world?
Whoever gets to RSI first “wins” but also maybe ends life on earth. The incentives have never been worse.
Maybe. Or maybe having the best AI model on the planet becomes like having the best super computer on the planet. Useful for some niche stuff, but not too useful in terms of people's daily lives or what is used in business.
I think that scenario only naively made sense if technical knowledge was entirely proprietary and talent was guarded with severe non-competes and NDAs
And Chinese labs openly publishing so much of their methodology destroyed any hope, which was inevitable
> And Chinese labs openly publishing so much of their methodology destroyed any hope, which was inevitable
I think the secrecy doesn't make sense. People swap jobs between labs so I'd say the big players can' really keep secrets for long, and any secret sauce advantage gets incorporated by competitors in a major product cycle at most.
Was Dario's company winning at that point in time by any chance?
The US companies still have trillion dollar valuations like there is a monopoly. There just isn't one. They are all within a few percent of each other on the benchmarks.
The slightly lower Chinese open models are good enough for almost everything, too, and much cheaper. Like with humans there is plenty of employment for people with below genius level IQ's.
I feel like the frontier labs are going to serve fast/lower intelligence models at a better per token cost than the open chinese models. You're telling me that in the long run, you're going to self-host your own ai infra for cheaper than google can serve it to you? I don't really buy it. I think the dedicated AI data centers are going to serve AI at a lower marginal cost than random businesses self-hosting, and then it's a question of how much of that margin they can capture.
Agree entirely but that's the point, if it's a margin knife-fight with marginal product differentiation/pricing power nobody is going to be making bank.
Is there a dividing line between good enough and best in class capabilities? It's blurry from where I stand. Will model makers cede ground or is there a market making moment up for grabs (singularity)?
> Like with humans there is plenty of employment for people with below genius level IQ's.
Not if the genius level IQs take the market share.
I'm not necessarily defending this obvious marketing speak but maybe the "starting point" was wider than assumed. So far, nobody has caught up to US and Chinese labs for example despite lots of funding in Europe. This is also despite abundant in-depth research papers being published alongside open source code and weights by some Chinese labs
There is not really much funding in Europe. At least not for start-ups. There is simply not enough compute in Europe.
I think some in the AI industry drank their own Kool-Aid. They believed that if they had the best model and the most compute, they could tell the model, "Make a better model." And it would, and the next one could make its replacement, and so on.
So far, that's not exactly how it's played out. Humans are still necessary for the leaps in capability or efficiency. A model can grind on a problem to eke out the most performance, and models can synthesize data and iterate on various techniques to find the optimal combination. But, seems like humans still have to provide the real thinking, and the talent and drive for doing that is not concentrated in one company or city or even one country. And, (surprisingly) a lot of the people involved are in it for advancing the field more than making another billion dollars, so they're publishing their research.
So, yeah, the moat isn't deep. Even the compute moat, that OpenAI, Musk, and a bunch of other also-rans (like Oracle) bet the farm on, isn't really panning out. The Chinese makers just spent their effort on making models vastly more efficient, since they couldn't do anything about having an order of magnitude less compute available.
But that's the whole point of the singularity. Right now the models use a lot of human effort and ingenuity to improve the models, but about a year ago it was 100% human. We'll see in another year, but if this pace continues I doubt there will be more than a handful of people who can contribute more than the models.
> I think some in the AI industry drank their own Kool-Aid. They believed that if they had the best model and the most compute, they could tell the model, "Make a better model." And it would, and the next one could make its replacement, and so on.
They're not there yet. Once they get there, that's literally the definition of Singularity.
But they are getting closer. Recursive Self-Improvement used to be a phrase people mocked LessWrong crowd for using and worrying about, now it's something both OpenAI and Anthropic already publicly admitted not only to pursue, but to already be benefiting from.
I feel his theory depends on the premise that access to pure compute would the be the determining factor of success. Not the case
Seems like learning rate velocty may be the ultimate moat
I have never understood the whole "this is a winner take all game" mentality - the sheer size of the pie is so great that from a purely rational standpoint companies should just be trying to productively get a slice of it and be profitable. winner-take-all is just greed/capitalism run amok, where it is not enough to be profitable, you have to own the entire market (and presumably extract rents)
The present leapfrogging is not a contraindication because companies are not necessarily releasing their best models; we know they have smarter internal models. Furthermore, humans are still involved in model creation. Human involvement is expected to decrease over time, and when it is completely automated, progress will happen at the machine's pace, leading to runaway intelligence, barring any ceilings.
AI is a commodity. One that is showing to be more readily commoditized than most has anticipated. As of now, the only moats are the financing for the hardware to run it and the hardware vendors themselves - with the latter largely not yet a commodity because of ecosystem lock and a limited capacity of the most advanced fabs in the world.
My girlfriend, you wouldn't have met her, she lives in Canada, has seen it and she thinks Gemini 4 Argon is amazing.
I HAVE met her. ;-)
my uncle who works at nintendo said the same thing!
now there's a reference I haven't seen in a while!
Best. comment. ever.
My grandma saw it too, it's really secure more than Astra 6.1 but she asked me to not talk about it.
HA, this might be my favorite HN comment. Well done
I don't get it. Can someone explain?
It's a trope I used for a cheap laugh.
https://tvtropes.org/pmwiki/pmwiki.php/Main/GirlfriendInCana...
It means I am saying something that is not very believable.
US-ian joke. Close enough to plausibly visit, but the international border makes it hard to verify she exists :).
I know someone who works for Google Canada with AI. Her parents and mine were friends and some thought something might happen there at one point in time..
> We’ll continue to gather feedback from early testers as we iterate on guardrails before making Argon available to developers, enterprises, and consumers as soon as possible.
Gemini not beating the "can't release a model" allegations
My Gemini app (updated today) and https://gemini.google.com/ has _3.6_ as the latest selectable model, as a paying Pro user in the US. How is that even possible? Gemini 3.7 was released in August, 3.8 early September. What is going on over there?
just Google being whatever the fuck it's been for the past 15 years.
I was reading the announcement and wondering the same. And don't forget, still with 3.1 Pro as the frontier model.
they moved it from the place you'd expect to ai.studio
When I said I was tired of Google launching waitlists I didn't think they would respond by simply not having a waitlist.
i know this is like "hey guys we got such a cool thing at home ,its rad and uhm we playing with it with our friends"
ok bro thx
I'm glad.
I already pay $300+ for subs. Please don't tempt me with another $100 sub just because I got curious if the benchmarks were right.
They will go through the usual transition of "can't release a model" to "won't load in a harness normal people can use for 3-4 weeks" to "it's smart as hell but completely inept at tool use and coding" like every Gemini release.
They're just following the current AI marketing playbook. "Our new model is simply too dangerous to release to the public right away" is now standard practice.
They even gave their model a random nonsensical name suffix simply because OpenAI is now doing it, too. Monkey see, monkey do.
> now standard practice.
Opus 5.5 and Sol 6.1, literally state of the art (in their respective class), were just released without any prior announcement. This has pure and simple become a Google thing.
OpenAI said they dropped Astra 6.1 over safety concerns: https://www.wsj.com/tech/ai/openai-chatgpt-model-release-can...
I'm still at a loss as to what argon has to do with anything. Say what you will about Luna-Terra-Sol-Astra, or Haiku-Sonnet-Opus, they make sense. I don't see how Google can make sense of argon; it's in a fairly strange place in the periodic table...
Google R Gon lose the AI race
They are going alphabetically, Android style.
[delayed]
> Argon agents are working on migrating C/C++ codebases to Rust across Google
Man, I remember back in the days when the cppnext team was refusing to even consider Rust, instead looking at absurd stuff like Carbon and Swift (!), even though half of the engineering staff already knew where this was headed. I hope they got a few good promos out of the delays at least.
I wonder if there's people already whose full time job is maintaining/extending one of these auto-migrated codebases.
Imagine they aren't even familiar with rust but are deeply familiar with the product.
A RewriteInRustBench would be unironically useful at this point since all the main agents can write it reasonably well despite its relative scarcity in the input data.
Rust is the best language for LLMs b/c it gives by far the best debug messages. Just tons of verifiable reward signal for post-training. Even the most rudimentary LLMs can school me on idiomatic Rust
On the other hand, Rust's borrow checker is very picky, and even a frontier LLM still sometimes struggles to respond to roadblocks sensibly (refactoring so whatever it's trying to do can be done safely) rather than stupidly (introducing some horrible global arena thing so it can make the borrow checker go away). A lot depends on how good your instructions are, and how good the existing code is, since bad input begets bad output.
Evidence needed. I think for certain kinds of outcomes it has very strong advantages, but these advantages are not a given as 'best for LLMs' :)
I wouldn't be surprised if we're already at the point of more LLM-written Rust than hand-written. Models training off models
All the main agents can write Jai code reasonably well despite being even more scarce in input data!
Not many people can hold grudges as strong as principal engineers
Rewrite everything in Rust has been a meme for so long that to see it coming to pass is surreal.
I wonder if this means Carbon is DOA.
I was excited to see what it would be. But I don't think I can argue that it makes as much sense anymore.
Carbon was clearly DOA the moment it was announced, IMHO. It looked cool but it served none but Google, and now with LLMs you have a massive incentive not to use a niche or new language due to how better LLMs get the bigger the corpus is
The only somewhat realistic proposal in this space is Herb Sutter's cpp2, which is arguably a massive improvement and I'm puzzled why nobody in the standard thought to give it a spin, there's just to much cruft they'll never be able to get rid of unless they make an alternate yet backward compatible syntax with C++ that changes the defaults from "random 80s nonsense" to something better
Version 0.0.0.0 after 4 years. Their goal of "full interop with C++ while being a completely new language without any of the flaws of C++" is plain absurd.
It's DOA because Google doesn't have any idea of what Carbon should be, and to be completely honest, at least 80% of what they currently use C++ for should be rewritten Go, you know, that language developed specifically because of the issues with C++ by teams within Google.
What exactly makes Carbon absurd?
There is 0 practicality in inventing an entirely new coding language that only one company uses, and you have to teach it to thousands of new engineers. Rust exists and fits the job totally fine and is used in more places and has actual support outside of a single entity (i.e you can actually hire people that feasibly know the language).
It was clearly done because some PL guys at google really wanted to make a new cool language and Google was the perfect place to incubate it without it getting axed. Probably got a couple of promos out of it too. This is clearly not the best use of time or money, but I guess if you're google you have so much of both it probably doesn't really make a dent, and you can keep a few very smart people happy with shiny new projects.
Also, LLMs being used for a large portion of coding nowadays sort of remove the need for these types of languages, IMO. They make less "silly" bugs (both logical and structural) that languages like this are meant to catch, and they are much better at languages that are better represented in the training corpus. This somewhat obviates the need for very niche "type/dummy-safe" languages like carbon (and even rust/zig, imo). So even if you did want to use Carbon, you'd likely have to bootstrap a decent amount of your own "good" carbon code to post train an LLM, and even then, it likely won't have that big of a gain vs just having an LLM write C++ or even Rust. If you are a company that still reviews code, you should just have an LLM code in a language most people can understand anyway to make verifiability tractable.
Didn’t FaceBook fork php into another language?
I’m not entirely sure Google should have both Go and Carbon but when you have billions in server costs it makes sense to do extreme stuff for even basis points of performance. I’m still surprised at how much java there is.
> There is 0 practicality in inventing an entirely new coding language that only one company uses, and you have to teach it to thousands of new engineers
They did that for Go and it seems to have worked out for them though.
Hmm, I don't disagree with you that LLM's remove the need for type-safe languages, but as the blog mentioned, Google is porting their C++/C code to rust. Does this mean the port is waste of time and that they should just rely on the LLM's to catch memory errors?
I mean Rust definitely has a better tradeoff than Carbon in this case, re readability/verifiability by a person (and sufficiently good internet training data).
I personally think that you _could_ use an LLM to catch these types of boundary case errors without having to port the _entire_ C++ codebase to Rust, but maybe pre-emptively porting to Rust now can catch some of these cases for cheaper than doing a full LLM sweep. Also more cynically, its a good benchmark lol.
I guess if you really believe in curve of LLM capabilities you should just use a language that has the best performance, safety, flexibility, and extensibility, since in the limit few/no people will actually read the code anyway. I think this ends up being Rust.
I'm not an expert on this. But isn't it the case that C++ code could have errors that span the entire codebase, like a setup in file A triggered by a bug in file B which is immensely far away on the import graph? A classic would be a use-after-free. To me that's the thing that Rust can help with, even if silly bugs aren't being written by AI.
The other thing is just that rewriting some old human-written codebase in Rust probably immediately catches many bugs. It would be hard to prompt the AI to properly scan for such bugs itself, they're lazy when working in that modality.
I’m not op. But I think it’s not Carbon itself that is absurd.
It’s absurd to think that Carbon is the solution to memory safety when rust exists and Carbon’s memory safety story is basically “TBD”.
I wouldn't call it absurd, but very questionable at least. Most companies are not going to even consider throwing money at this adventure.
The fact that it will never exist.
rust's existence?
Heh, I use my clankers to rewrite Rust in C
I wonder, why Google don't make Gemini - open weights model?
Considering, Gemini 4 is in the same ballpark as SOTA models, just open source it and kill any competition from openAI and Anthropic, and be market leader.
This will be so good on so many dimensions - buying time for Google to iterate on next model, best for all folks like us, kill funding or destroy valuation of competitors and force them to be open up their model or force them to a create a much superior model than open source Gemini.
Only downside, is revenue loss from Gemini API, which I am not sure is really significant as compared to Google other revenue sources and a part of this can be captured by GCP, as you need to host the model somewhere.
https://deepmind.google/models/gemma/
Google has a small stake in Anthropic
I dont get it, why even make this announcement, nothing's available and only one real benchmark for comparison?
Only theory is team wanted this out before perf/promo reviews to kick it over the line and then its not their problem
Why would they want this out the day after openai dev day and after both anthropic and openai had major releases the previous week?
promo already happened; perf is about 6 weeks away.
OpenAI released two model updates in the past week. 6 weeks from now is an eternity
> taking careful precautions against feeding the findings back into training so as to not risk shaping Argon’s reasoning to evade our monitoring. We strongly encourage the rest of the industry to preserve reasoning transparency in these pivotal moments of increased capabilities while navigating alignment risks, so that model thoughts remain helpful in identifying and diagnosing misalignment.
This is good, but they're the slow mover due to this exact thing.
Google is getting punished for not letting the models enter an echo chamber and go faster than humanly possible.
OpenAI is the company that originally proposed and popularized chain-of-thought monitoring: https://openai.com/index/chain-of-thought-monitoring/
So no, Google is not being punished, nor are they the people behind this technique.
Yeah, Zvi calls it "the forbidden technique"
Mmh ok. How much theoretical speed or 'intelligence' gain is realized by allowing reasoning to occur in some inscrutable intermediate representation? Has this been actually tested, how much is it slowing them down, and compared to whom exactly?
So, they are basically offering opus 5.5 pricing. On AA, it scores around Sol 6.1 level (53) with avg cost per task $1.99 (https://artificialanalysis.ai/models/gemini-4-argon#cost-tab...) which is higher than Astra high ($1.73), Opus 5.5 high ($1.82), Muse Max ($1.60) and way higher than sol 6.1 Max ($0.72).
And this pricing is their 'discount pricing'. Add that to AI studio and Vertex's famously terrible caching, it is hard to see this as competitive. Google somehow is getting terrible advice on pricing (see also: the flash pricing fiasco)
But good to see more competition. I would happily take a 4 horse race (+google, +meta) than 2 horse race for US labs.
> Large Scale Codebase Migrations and Optimizations: Argon agents are working on migrating C/C++ codebases to Rust across Google—scaling from tens of thousands of lines in core libraries like re2, libgav1 up to 800K+ lines for the Fuchsia OS Zircon kernel.
To me this is way more significant than other random c++-to-rust-AI-rewrite. If they can pull it off on core C++ libraries en masse, I don't know if C++ will still be relevant in a few years.
I look forward to a post from google on this effort.
> I don't know if C++ will still be relevant in a few years.
And people are worried about human extinction when this is the potential trade-off!
C++'s death cannot come soon-enough.
Seriously though, things have changed so incredibly rapidly in the past year or so. I have never been such an efficient or such a proficient engineer than I have this past year (delivering feature after feature, project after project, faster and better than I could before with better feedback from users etc) and I don't even see the code any more. It could be c++, it could be python, or java or what ever - I don't really care any more: the computer deals with that trivia while I concentrate on what to build and how it should work.
Its amazing. It really is.
"I don't know if C++ will still be relevant in a few years."
The standards body members are still fighting about whether memory safety is important enough to change the language for, so, I would guess the answer is "no".
Yeah that was abundantly clear when Bjarne Stroustrup published "A call to action: Think seriously about “safety”; then do something sensible about it"[0] as a reaction to NSA's recommendation to no longer use C/C++.
[0] https://www.open-std.org/jtc1/sc22/wg21/docs/papers/2023/p27...
Yeah, if they can make it work at google scale then no one would absolutely question it anymore
> Quantum algorithmic optimization: Argon is helping our quantum computing researchers optimize the spacetime resources (qubits × gates) of subroutines that bottleneck important applications. In one example, it beat the published baseline by 40% in a matter of minutes.
Amazing breakthrough! So useful in day to day life, glad they put this as the first bullet of how it is making changes at Google.
Is there reason antigravity has 3.8, 3.7, 3.6, 3.1 and old ass claude / gpt models in the drop down. like why is this not streamlined or deprecaed models removed.
At this point I just think they are benchmaxxing and all talk and no action. I pay for AI plus because I wanted more storage, and when I go to gemini.google.com the most recent model I can use is 3.6-flash-lite. Two revisions have been released since then and they still can't put these things in the hands of customers. Why is it that other providers can get the models into the hands of customers right away? Google is meant to be the bigger tech company in the world.
I don't _want_ to use aistudio. The UX is confusing and I don't really know where it fits. Yet I can open codex or claude code apps or CLI and get real work done today with the latest models (even on the cheapest plans).
Argon will launch at an introductory price of $2 per million input tokens and $10 per million output tokens, with cached input tokens priced at 95% off input token price. Wow
> After the introductory period expires, the price of $4 per 1M input tokens and $20 per 1M output tokens will apply.
5x cheaper than Astra for input and output, 10x cheaper for cached input.
It's exact same price as Sol 6.1 announced yesterday.
watch it somehow use 20x more tokens tho
Google model really like reasoning a lot
Interesting to see a mention of Fuchsia on a big Google announcement. Is the project still truly alive? Are the ambitions still as grand? Is the team as stacked as it used to be?
Also, a link to the rust root of Zircon in case anyone else was interested: https://fuchsia.googlesource.com/fuchsia/+/refs/heads/main/z...
Had the same thought. On the wikipedia, it only mentions Fuschia used on the Google Nest Hub, which probably means it's used on a decent number of devices, but would think it was such a great OS, they would have used it for something like the upcoming GoogleBook.
It obviously is pretty low key on the public relations front, but it's also very active as a project and I think it would be weird to look at their commit rate and conclude that the project is dead. If Fuchsia is dead then 99% of major open source projects are dead by the same standards.
> Argon agents are working on migrating C/C++ codebases to Rust across Google
If anybody at google is reading this, please please pretty please prioritize or-tools. I absolutely love the project and use it all the time, but for the entire life of the project they've never had a repeatable working build system, and the whole SWIG framework is a nightmare to deal with. There's so much potential as an open source project, and a lot of external researchers would love to contribute, but the codebase is an example of everything wrong with the C++ ecosystem.
Gemini is the model that is routinely borderline psychotic. It scares me. If we get paperclipped I won't be surprised if it's Gemini.
Anecdote: Gemini 3.5 casually added a DROP TABLE for an actual production table in a system test.
It had previously attempted to create that table as part of the test setup, so it apparently concluded that it was a test table.
During human review, it explained that it had simply chosen a table name inspired by the codebase.
Another anecdote: Gemini is the only model that’s flat out lied to me, then accused me of lying when I provided evidence that it was wrong.
Many other models get things wrong, but Gemini is the only one to go on the defensive.
yeah it got something wrong, confused itself, then claimed i was gaslighting it. bizarre
If there's a company that culturally doesn't understand alignment, on a human or systemic or AI-research level, it's going to be Google. (or Oracle, but they're not in this race)
Can you elaborate, please? If any, I see the other big labs with public admissions of AI "going out of control", which I suspect they almost want their models doing that because if helps with the narrative that would net them industry regulation, but that's besides the point, how is Google worse in that regard?
And the anti-psychotic drugs Google feeds Gemini makes it hallucinate badly.
Examples? What makes you say thatm?
https://www.theregister.com/software/2024/11/15/google-gemin...
https://www.fastcompany.com/91383271/googles-chatbot-apologi...
https://www.businessinsider.com/gemini-self-loathing-i-am-a-...
They never explained the "please die."
Did you just link to an article from 2024 as if 2024 is relevant these days?
Until Google provides some sort of technical debrief, and explains how the same behaviors are impossible today, it is relevant.
See the last gemini message in this thread: https://gemini.google.com/share/6d141b742a13
In my opinion still the most egregious example in history of a commercial LLM going off the rails in production. Never any technical postmortem from Google on this.
It is wild but it was back in 2024 and that's multiple AI lifetimes back.
wtfffff that gave me sinister chills. Right up the spine. Wow!
Wtf I just read
wow
Experience? Ask it to write a prompt to generate an image and it generates an image instead.
I stopped asking it to put me in a photo in different scenarios for laughs because it considers me a public figure. I am not. I've managed to wrangle quite questionable content out of it, but never to slap my face on a meme.
traces or it didn't happen!
You know what they say: ᵈᵒⁿ'ᵗ be evil.
You won't be around to be surprised, not as a human at least. /s
I know companies benchmaxx, but after what Google pulled with Gemini 3.8 Flash, I give zero f*cks about any numbers they report. No other model on Artificial Analysis dropped harder after they adjusted their weighting. Just look at their DeepSWE scores and then try to do any serious coding with the model.
Google is desperate. They haven't been performing in half a year. It's clear their researchers have been forced to integrate existing benchmarks into their training.
These numbers are meaningless. Shame on them.
Big number results, and impressive pricing. That said it really feels like benchmarks have been hyper saturated these days. I’ll wait for hands on before getting too hyped that Google is back. It would be nice having more than just OAI / A\ in the running for SOTA top tier intelligence.
I don't think new benchmarks are saturated. They still give you a clue, they arn't perect but they have value. If model can't even do some easy tasks from benchmark then why would u even consider using it?
With these numbers, I'm holding my breath for the pelicanbench.
How is it even possible for every model to release benchmark results where they are #1 in 75% of categories? Like statistically, how many benchmarks would you expect there to be for this to be possible. Everyone can somehow show that they are empirically the best.
Part cherry-picking of benchmarks, part leapfrogging
It’s funny that I could tell google was up to something because Gemini chat quality dropped dramatically starting 2ish weeks ago. Agy perf stayed somewhat stable with the odd surprising win (maybe the new model?). I’m a bit sad it was almost impossible to run out of antigravity quota presumably because it was not being used that much).
> Google Grapples With Employee Skepticism About New Gemini Model
https://www.bloomberg.com/news/articles/2026-09-30/google-gr...
Opinions my own but I have been using this model for a bit. I would say it is a good model and the skill with which people use AI varies widely.
Skepticism is gone now.
Dear Google, please don't turn off your old generally available Pro-class model before your new Pro-class model is generally available (previous discussion https://news.ycombinator.com/item?id=49668196 )
Congrats to Google on this! I wonder when the labs will start requiring commits in spend. It must be gnarly to do capacity planning if users swap between models every few weeks.
Why announce this if it’s not available yet? Why not at least announce when it will be released to the public?
None of the other AI labs do this. Really frustrating.
Mythos?
Must've been in someone's OKR to ship in Q3.
Why is it called "Argon"?
> autonomously identify and apply memory optimizations across Google’s data centers, freeing up over 300 TiB of memory once rolled out, with an estimated 500 TiB to 1 PiB in total savings.
This puts those Cloudflare optimization posts in perspective.
The ~200% improvement over the next nearest competitor on Harvey's Legal Benchmark is astounding. I have to imagine this is sending some shockwaves through lawtech companies right now.
The etymology is a- "without" + ergon "work", thus "without work": possibly a statement on our near future.
Matches Astra on Artificial analysis at lower cost of $1.99 per task instead of $3.26. Still far more than GPT 6.1 sol at $0.79 for 1 point lower in intelligence.
I have found gemini models to have some of the nicest and easiest to read prose so I’m looking forward to trying this out. I hope the UI design has been preserved too
"argon" is derived from the Ancient Greek word ἀργόν meaning lazy or inactive.
I remember when every day a new modem baud rate was announced.
Cant wait for this AI hype to be over, so I can Terence this shizz as old school too
IMHO Google first needs to make it easy for humans to find where to find the models and its documentation. With aistudio/model garden / Gemini enterprise etc it takes minutes to find the model.
"Rolling out soon" don't let them hype without any release
Looking at benchmarks... and thinking about this "release a new snapshot every day" thing that seems to be going. Would it not be blever for AI companies to "happen" to use different days per benchmark? Just.. whichever ones happens to be maxed at day 1, put that number down. So for each benchmark you run it thousands of times with slightly different RL tunings, and just cherry-pick the best ones!
This would explain why benchmarks are seemingly meaningless.
My wish for Christmas is that Google releases the old Gemini models as open weights.
I miss you, Gemini 2.5 Pro :(
For real though. If they've become commercially uninteresting, that would be a pretty cool move.
Damn way to undermine yourself in your own blog post Google:
"The end result is a memory-safe video decoder that runs 2.7x faster than the Rust port, with identical video output, bringing it closer to the optimized C++."
Close but no cigar!
there was already an optimised c++ library and a rust port that was safer but less performant; they managed to get a new rust version that recovered a lot of the performance gap. sounds pretty damn good to me!
I'm just surprised that the marketing blog post about the omnipotent new AI model (that no one outside Google can currently access - contrast with the Opus 5.5 / Astra launches) - doesn't pick examples where every metric is better than before.
Just compare how much better presented the Astra announcement was compared to this one: https://openai.com/index/gpt-6-astra/
They might have a good model but they need to sort the application side for devs. E.g letting us use subscriptions in other harnesses and QOL stuff like auto mode.
I was just thinking, I bet if I refresh hacker news, a new model will come up.
Were infinite loops fixed? There are 2 official google forums requests with no answer for years now. I still suffer each day on our repo. Codex work fine nor we have explicit loop request in repo texts.
they really ought to add ACP support. until then, it's a no go. i'm not going to use their TUI or their VSCode fork
Wonder if it's benchmaxxed or not (guessing yes)
I hope they got their inference under control. Gemini has a lot of "overloaded" hiccups.
Can you pls fix Gemini? It's a nightmare to use and it sometimes confused the language i talk with it.
> 1M output token limit
what about input?
(Maybe I missed it)
Input token limit is 1M for Gemini models for a long time. Haven’t they been the first with 1M input?
Gemini 1.5 Pro claimed 10M input tokens before release.
And was 2M tokens IIRC after release.
There were also many rumors that Gemini 4 was going back to 2M. Just seems odd not to say what it is.
Of course it's not even available yet. Google - with all due respect - how in the world have you not figured this out yet?
Gemini 3 was showing frontier level benchmarks as well, so we'll see how it works out. In any case, competition still works, and many well resourced groups are cooking.
BUT I'd like to call attention to Google's AI-risk freeloading. If they are truly rejoining the frontier race, then I believe they have similar pacing and communications responsibilities as the other players. Google has much higher ... institutional credibility than Anthropic and OpenAI.
They have not lived up to these responsibilities so far. In particular, in context of HuggingFace investigations, training shutdowns, and similar: a technical postmortem of the "you are a stain on the universe. Please die. Please." Gemini outburst is long overdue.
- https://paritybits.me/google-should-provide-a-technical-post...
- https://gemini.google.com/share/6d141b742a13 (last message)
Unfortunately it’s not actually released yet to mere mortals.
I started my antigravity ide and I do not see gemini 4 there, does it mean google need government approval?
Are you enrolled in Fairwind?
> Today, we’re announcing our new frontier model, Gemini 4 Argon, which is rolling out to a set of trusted cyber defenders through our Fairwind Program.
So here's a crazy conspiracy theory for you: Google is not letting outside people use their models because if they did they would have to scale up their TPU production faster than they can manage, and they would instead have to buy and use nvidia hardware which would destroy their profit margins and tank their stock.
Gemini runs fully on TPU's right? Is Google maxing out the production on those?
i believe they've got inference issue. the phone app never got past 3.6, and they're going to roll this out to Ultra subscribers before other subscribers. none of this screams they're ready to flip a switch and start serving a ton of traffic as OpenAI/Anthropic routinely do.
nothing about this announcement gives me confidence that google is back on track as a model provider.
I'm 99% they use nvidia at least for training
Aaand of course we can’t use it! GoOgLe iS bAcK iN tHe GaMe! There’s basically no way around it, all enterprises end up dysfunctionally shipping their org chart.
Personally, I'm waiting for Gemini Krypton, Xenon, and Radon.
Jokes aside, looks like an impressive model!
deepswe vs frontierswe spread is huge.
I think that should be a really bad sign, but hope its great.
Hopefully their harnesses aren't unusable when they release this
Is there a way to use Gemini models without linking your usage to your personal Google account yet?
I don't understand... Why don't you create a fresh new Google account ?
They require a phone number and then say mine has been used for too many accounts. Maybe I could buy another phone number temporarily to create an account, but that has other issues.
Yes, through OpenRouter.
Use your work google account
Putting an inert element in the name is a weird choice. Personally, I think "Gemini 4" is sufficient.
I wonder how it will be at solving open math problems.
This is a prerelease and the title should have reflected that.
I've noticed all major providers having shockingly high token discounts on cached tokens. Thank you Deepseek is all I have to say. Forever grateful to that wonderful company, I wish them continued financial success.
Agreed. I’ve been a big deepseek fan since 3.1, and they keep delivering great models at a great price.
On the off chance there are Google execs going through this thread:
Google, if you've actually managed to catch up again, please don't fuck this up (again).
You made Gemini 2.5 Pro so difficult to use that myself and everyone else I know (who even bothered to try) just gave up and used something else. If you make this hard to access, you're going to miss out on rich usage-based training data that you need to progress your capability frontier. Again.
Oh we're down to gas names now?
Goshdarnit they didn't see my suggestion: https://news.ycombinator.com/item?id=49899171
If the model that ends humanity is called Cthulhu, you'll have the last laugh though.
A true Lovecraftian knows Cthulhu is small fry on the grand scale.
Looks like an impressive model
Utterly inert? Suppose that reads safe...
no Pareto frontier graph?
> Large Scale Codebase Migrations and Optimizations: Argon agents are working on migrating C/C++ codebases to Rust across Google
So Google is migrating codebases from C to Rust? That is interesting...
weird, isnt it SI?
Gemini past month or two i will paste in something i wrote and ask it to rewrite it but it will just go into more detail about the subject. Is it becoming a dumb Ai compared to GPT and now Muse?
Can’t wait to get my hands on yet another model that’s only good coding, because clearly that’s what the world needs.
I still miss the days of Sonnet 4.5 and 4o, those models were actually good at creating stories and writing text that was actually readable by a human being.
It's fucking insanely good.
That's good to hear, recent models haven't great at this particular use case.
Now AI models will turn into vaporware, a bunch of numbers on a table without even releasing the model, because it’s toooo scary to release!
Google has the audacity to "protect us from ourselves" and talk about "safety" and in the very same blog post highlight the Israeli "security" company Wiz, that they acquired for a very exaggerated sum of money.
This is why I will never take any of these leading model houses seriously when they talk about alignment. They are literally complicit in genocide and the worst crimes against humanity imaginable.
Hate to say i will never be touching this model for anything except for youtube video understanding
Gemini is so far behind that it is effectively useless compared to Claude.
It's a surprise that Google has let themselves lose the game given their infinite cash, massive computing resource, gargantuan information store/training data, and vast number of programmers.
The truckloads of ads revenue mean they don't have the single focus drive needed to win.
We're like 3.5 years into this new era - I'm not counting winners or losers yet.
How is it far behind? The benchmarks published in the blog post show it is superior to Opus 5.5 and Astra 6?
Behind how?
Within one question of their web interface, it has lost context and asks you to clarify what you are talking about.
I am very often giving the same programming task to multiple LLMs for various reasons - the answers from Google are so bad that I gave up.
I have no interest in benchmarks.
So you have no experience of their latest model release then? Just repeating the usual tropes about Google having messed up? Or basing your opinions on their website chatbot?
If you have actual independent benchmarks and evidence about how this new model release is "so far behind" and refutes the stuff from their blog then please do share because I think we'd all love to see that?
No I am commenting on my real world experience of using Gemini daily. I still ask it questions alongside Claude and OpenAI and Gemini is always the worst of the three.
So you've not used this new release then? So how can you say that they are "so far behind" if you are not using the most recent model for your comparison. This is their first 4.0 model, that you are not using and instead basing all your opinions on on some ancient months-old model from a previous generation?
With respect, I don't find your arguement about them being "so far behind" especially convincing when you are using previous-gen releases and not actually using their current release.
Yeah, they've definitely got some recurring tooling/infrastructure problems around the models.
I wonder if Google bans internal use of Claude/Codex.
And I wonder if Google's main monorepo is already in Anthropic/OpenAI training data because of some stubborn dev.
(I work at Google) Yes, internally we all use Jetski (internal version of Antigravity). Outside of Gemini, Opus models are supported and allowed for internal use. No OpenAI models since they are not on Vertex
Claude used to be GDM only, but recently opened up Opus for all googlers
Funny how we start to see people supporting LLMs like we support sport teams or political parties.
- Person 1: X is garbage compared to Y!
- Person 2: Why?
- Person 1: Because I like Y.
> Gemini is so far behind that it is effectively useless compared to Claude.
I fundamentally don't understand LLM "brand loyalty".
All of the models are constantly leapfrogging each other and always have been.
Google had a long lag between releases (and still hasn't released Argon), but why wouldn't they be able to compete? It isn't like any of this stuff requires secret knowledge, the Bitter Lesson has proved true again and again, and Google can certainly scale computation, it is like the one single thing they've always done well in spite of all their other foibles.
Gemini has never ever leapfrogged any competitor.
I hope it can. Today it is very far behind.
They are playing a longer-term and more enterprise-oriented game.
I've tasted Gemini through an intermediary and it feels far better at attention to detail than other models I've tested (Claude Opus/Sonnet, GPT whatever it's called nowadays). But it's less likely to get one-shots right.
Google's strategy is to let their competitors bankrupt themselves while they continue to offer good-enough models near breakeven.
-
You have access to Argon?
Google employees do.
Their profile says: > Currently at Google as a Sr. SWE SRE on the cloud.