Prompt was "read and update the config file with new data". This work on 4.6 takes <2
minutes to read the file, parse the new data, and patch.
Opus 5 Result: 43 minutes of pulling containers, running sandboxes, creating testing suites, which included evaluating the entire repo beyond the scope of the config file.
Opus 5 in xhigh can't do basic math as well.
They dumbed it down to a point where I just cancelled my subscription yesterday.
I used to be a $200 subscriber, dropped to $20 after the fable shenanigans, and use it only when I have no usage left with Codex.
/on The prose is load-bearing unbearable — every sentence feels like it was engineered to sound profound rather than to be read.
Wouldn't be surprised if there are knobs that get turned as a function of the revenue they might expect you to generate.
I was a 4.6 acolyte from April til the fable drop, lost that quick, cancelled and took a break, came back a month later, tried opus 5 and liked it, so unpinned 4.6.
Results were great at first, and they're still not terrible, but I have noticed a regression in accuracy, so to speak, where I am pointing out issues that are quite obvious in review.
I pretty much use sonnet 5 low/medium when I have a plan to solve a simple problem and depending on scope, opus low/medium for more complex/bigger scope implementation, and only go high when it's very complex or I'm spitballing architecture/solutions and iterating plan. Never go xhigh or max.
The verbosity is insane though, opus 5 documents everything and just regurgitates whatever lead it to the design choice in there, which makes it more opaque because it's talking about something that was discussed once in a session that no one else can see (except their backend ofc)
I don't even try to steer it away from that with harness, because it doesn't work and just ends up agonizing over whether it should write some comment. Three paragraphs waffling on that on verbose output
I did however have it write a script that basically is git add -A -p for comments though, haha.
I'm $20/month, have all my telemetry toggles off, don't really over engineer prompt/context, just some basic skills for repeated patterns.
It was the wrong time for the GP to drop that subscription from $200 to $20, because $200 gets you a metric assload of cognition while $20 gets you nothing beyond what a local model running on your own graphics card can deliver.
I had $310 in (free) credit that I used on fable, and I still had a part of the $200 subscription at that time. You know, subscriptions don't end the moment you click on cancel.
With how competitive the LLM field is, it would surprise me greatly if any of these players were doing anything other than trying to make the best possible product. I certainly do not believe they are intentionally training the models to use more tokens unnecessarily.
Theyre training the system to minimize compute,so most likely theyre dynamically downgrading quants in the first few turns hoping to find the cheapest model to run. The side effect may be excessive token gen
This is only true if they can't saturate token production with a model that does less superfluous things. Given that they can (they're hilariously compute strained), having a model that solves tasks more quickly adds way more perceived value to users.
Agreed although the differences between the effort and reasoning is massive. I generally ship 40 hours in three with AI. I could not figure out why my delivery was behind until I started going through the logs. The thinking was extensive, the effort was beyond the original request by a magnitude of 50x
Recently got approved at work for ChatGPT Pro so I could use Codex.
Blown away by the speed. It feels like using Claude Code for the first time again. I don't think Codex is doing anything revolutionary, just better handling of which requests should go to which model, and having faith in some of the "less powerful" models for more than you would think.
It seems the TUI coding experience is very much an open race. This is motivating me to look at other agents / harnesses as well (maybe Gemini, OpenCode, etc).
Not specifically Anthropic but why are we allowing billing to take place in tokens that are nebulous and fully controlled by the operators who have no aligned incentives?
If I have a user input and then sanitize and inject that into a prompt to do something, I have no idea how much that is going to cost at all and no real way to measure this properly. A parallel example is digital ocean or aws, i can go and measure/limit my compute/fs/memory/startup times/etc and while it can be impossible to get down to the last flop of money allocated - i can run things on a real budget with real constraints, opposed to an LLM where I have to .. prerun a sanitized user prompt through a tokenizer and then ask an LLM to guess what it may do and give token consumption estimates and then act on those in any sane manner for the user?
Perhaps i'm missing something to do realistic and static rails on things but I don't see a serious way at scale to use the token billing model handling things requiring a users free text input short of having to go pander to VC money to throw money at it until someone else figures it out.
*to clarify my rambling...
We should be billed and given controls based on resource usage itself and not an opaque token concept on top of not being able to spin any knobs that control it's resource usage.
The model providers are quite aligned with concerns like customer retention. These arguments only work if there is no competition. We exist in a marketplace of black boxes. There's not just "the one" you must suffer. You have options. You can build your own too.
“Claude, spend the next 10 hours trying to solve the Reimann Hypothesis”.
I agree that incentives are misaligned but there’s several competing model providers. If one gets funny with their costs people will jump ship, especially if the gap between the top 2 labs and everyone else keeps shrinking.
“I can take current sources and tell you how solved this is, but I am not willing to work to a timeframe or to solve things that aren’t yet solved by mathematicians or science”
These safeguards already exist when they get a whiff that you might be using Claude to fix security issues. Doesn’t seem farfetched given the incentives I outlined that they would apply to this kind of abuse.
How loose those controls are becomes a market force.
It's an interesting conversation - because at what point do you call it an abusive relationship, right? Maybe even ancillary to anthropomorphising an inanimate object - I've cancelled my Claude sub and I've shot question after question at it now (during the cancellation period), resulting in almost every reply with me asking it to "please speak normally". I will most definitely not be renewing my sub. I have no desire to engage with a non-human somehow managing to speak down to you, without answering the question.
EDIT: my honest opinion; Anthropic is building a person, whereas everybody else (it seems) is building a tool.
Is it actually entirely a prompt-based information? I’d assume that some of it is the harness part of the agent setting reasoning token budget and compacting reasoning etc.
In that case, the agent will respond incorrectly because it has no visibility into what reasoning mode it’s in.
IIRC responding to effort level settings appropriately is part of the (post)-training. In that case it could be considered another instance of the Bitter Lesson. Uplifting.
Does anyone know what setting effort means for models like these? Do they allow longer thinking sessions? Some kind of system prompts? What’s stopping someone from getting max effort output from low effort setting?
My understanding is that the current effort settings is a part of the system prompt, and that the levels and their intended results are a part pf the training process. The effect is more or less tokens spent reasoning before the model outputs a stop token. However it is as consistent as any other aspect of LLM behavior is..
I suspect it's not just this, there's plenty of 'optimization' around rubberbanding usage limits as well as routing to a different model in the backend. The incentives are too strong.
I submitted an application for Anthropic's Cyber Verification Program.
I was approved.
3 months later, my approval was degraded into "in review" (revoked). I'm sure my account was flagged based on contents of debugging/researching firmwares/etc.
I opened a support ticket. No response. I opened another support ticket. No response.
1-2 weeks later, I got a response that I will not be re-approved and I need to reapply. No problem.
The page to reapply on does not allow me to re-apply because it my account is stuck in an "in review" status.
The community thinks it's a bug. I'm 95% sure it's not and a bunch of us who were previously approved had it revoked due to flagged content and will not be reapproved.
I switched to Codex + got TAC approved instantly and have not looked back. It's a shame. That's 100% separate from whatever the heck the quality of Opus 5's outputs are. The way it talks... insane. I would bet a good amount of money their next release will focus "reduced simplified responses" if I had to guess.
* Anthropic's Cyber Verification Program // Codex + gotTAC approved*
Meanwhile the Chinese models are "go ham dude"...
If it was not for capacity issues, Chinese models have a higher change to just dominate.
> $2t company by the way
It used to be that OpenAI and Anthropic had such a moat around them, that such a valuation was worth it. But these days, its gross overvalued (like so many).
The more stuff is being pulled like cyber verifications, downgrading effort levels, downgrading usage (OpenAI), the more people move to those Open Weight Chinese models.
When DeepSeek Flash 0731 came out and provided a massive jump in cheap inference capability. It resulted in a 10x increased OpenCode token usage.
It took a 2.5x to 5.0x price increase AND a reduction by 4x usage (later to 2x) usage, and several cheaper models + a free model, to push the traffic down.
Traffic towards open weight models is increasing, even if providers can not keep up with the influx of new customers. This is not something you want to see as two companies, trying to go for IPOs.
So the idea of stonewalling cyber capabilities, when the rest of the world is just doing whatever with open weight models, on their own hardware even! This entire strategy from Anthropic never made any sense.
I have been as well. Based on my own sessions, Max vs Max, same 1M context window size, the literal majority of the cost overhead of Opus vs Sonnet comes from Opus being chattier. So I started using Low reasoning instead of falling back to Sonnet, and I've been really happy with the results. Way better quality at a comparable spend. I also rarely go past High lately, which was another major cost save.
I don’t know if this is still the case but while using Copilot if you looked through the chain-of-thought output you would see it reasoning about a “budget”. i.e. “since I’m close to the session budget I should…”. So it could be possible
Effort level is actually controlled entirely by system prompt (as I understand it, the model is trained on that format but still), so actually this is a valid way to check I think
OpenAI models also work this way, as evidenced by full cache blowout when changing reasoning level. Every single open-weight model I've seen also works this way (your "reasoning_effort" argument just changes a small section of the system prompt in the chat template). I would have to see some evidence to believe Anthropic were doing anything different.
Then model can say it's Opus, but really it is some old Sonnet. This seems to be happening less often, but some weeks ago I had to give models some test problems to gauge whether I am getting Opus or something knee-capped.
The problem is that Anthropic seems to be getting away with selling one thing and delivering another. You pay for Opus, you get something else etc.
Saw this in npx ccusage@latest claude output. Had only used opus but showed sonnet. Can't remember if the jsonl retains which model is doing what, but meh
Whatever Opus 5 is doing should not happen.
Prompt was "read and update the config file with new data". This work on 4.6 takes <2 minutes to read the file, parse the new data, and patch.
Opus 5 Result: 43 minutes of pulling containers, running sandboxes, creating testing suites, which included evaluating the entire repo beyond the scope of the config file.
Both: one file modification
Opus 5 in xhigh can't do basic math as well. They dumbed it down to a point where I just cancelled my subscription yesterday. I used to be a $200 subscriber, dropped to $20 after the fable shenanigans, and use it only when I have no usage left with Codex.
/on The prose is load-bearing unbearable — every sentence feels like it was engineered to sound profound rather than to be read.
Don’t $200 and $20 levels steer you to effectively different models?
Wouldn't be surprised if there are knobs that get turned as a function of the revenue they might expect you to generate.
I was a 4.6 acolyte from April til the fable drop, lost that quick, cancelled and took a break, came back a month later, tried opus 5 and liked it, so unpinned 4.6.
Results were great at first, and they're still not terrible, but I have noticed a regression in accuracy, so to speak, where I am pointing out issues that are quite obvious in review.
I pretty much use sonnet 5 low/medium when I have a plan to solve a simple problem and depending on scope, opus low/medium for more complex/bigger scope implementation, and only go high when it's very complex or I'm spitballing architecture/solutions and iterating plan. Never go xhigh or max.
The verbosity is insane though, opus 5 documents everything and just regurgitates whatever lead it to the design choice in there, which makes it more opaque because it's talking about something that was discussed once in a session that no one else can see (except their backend ofc)
I don't even try to steer it away from that with harness, because it doesn't work and just ends up agonizing over whether it should write some comment. Three paragraphs waffling on that on verbose output
I did however have it write a script that basically is git add -A -p for comments though, haha.
I'm $20/month, have all my telemetry toggles off, don't really over engineer prompt/context, just some basic skills for repeated patterns.
Yes. You don't get Fable at the $20 level.
It was the wrong time for the GP to drop that subscription from $200 to $20, because $200 gets you a metric assload of cognition while $20 gets you nothing beyond what a local model running on your own graphics card can deliver.
I had $310 in (free) credit that I used on fable, and I still had a part of the $200 subscription at that time. You know, subscriptions don't end the moment you click on cancel.
The decision to leave is genuinely yours.
The business bottom line depends on tokens, shareholders want to see exactly that.
With how competitive the LLM field is, it would surprise me greatly if any of these players were doing anything other than trying to make the best possible product. I certainly do not believe they are intentionally training the models to use more tokens unnecessarily.
Are you implying they can’t do both?
Yes, those things are mutually exclusive.
Theyre training the system to minimize compute,so most likely theyre dynamically downgrading quants in the first few turns hoping to find the cheapest model to run. The side effect may be excessive token gen
Sure. That’s possible. But that’s not what I was talking about.
Yep, they’re lighting tokens on fire with that thing.
AI companies have a financial incentive to burn more tokens than the task actually needs
This is only true if they can't saturate token production with a model that does less superfluous things. Given that they can (they're hilariously compute strained), having a model that solves tasks more quickly adds way more perceived value to users.
Agreed although the differences between the effort and reasoning is massive. I generally ship 40 hours in three with AI. I could not figure out why my delivery was behind until I started going through the logs. The thinking was extensive, the effort was beyond the original request by a magnitude of 50x
Only if the customer is paying per token. If it's by subscription they're burning their own money
A.k.a. theft.
Thought I was going to be on Claude Code forever.
Recently got approved at work for ChatGPT Pro so I could use Codex.
Blown away by the speed. It feels like using Claude Code for the first time again. I don't think Codex is doing anything revolutionary, just better handling of which requests should go to which model, and having faith in some of the "less powerful" models for more than you would think.
It seems the TUI coding experience is very much an open race. This is motivating me to look at other agents / harnesses as well (maybe Gemini, OpenCode, etc).
Gotta milk the cows.
and ending with: "One thing I need to tell you:" and bunch of AC, R1 and §
Not specifically Anthropic but why are we allowing billing to take place in tokens that are nebulous and fully controlled by the operators who have no aligned incentives?
If I have a user input and then sanitize and inject that into a prompt to do something, I have no idea how much that is going to cost at all and no real way to measure this properly. A parallel example is digital ocean or aws, i can go and measure/limit my compute/fs/memory/startup times/etc and while it can be impossible to get down to the last flop of money allocated - i can run things on a real budget with real constraints, opposed to an LLM where I have to .. prerun a sanitized user prompt through a tokenizer and then ask an LLM to guess what it may do and give token consumption estimates and then act on those in any sane manner for the user?
Perhaps i'm missing something to do realistic and static rails on things but I don't see a serious way at scale to use the token billing model handling things requiring a users free text input short of having to go pander to VC money to throw money at it until someone else figures it out.
*to clarify my rambling... We should be billed and given controls based on resource usage itself and not an opaque token concept on top of not being able to spin any knobs that control it's resource usage.
> the operators who have no aligned incentives
The model providers are quite aligned with concerns like customer retention. These arguments only work if there is no competition. We exist in a marketplace of black boxes. There's not just "the one" you must suffer. You have options. You can build your own too.
there are roughly three of them and they all use the same pricing model. I am also not in the position to build a frontier model company these days.
How else would they bill tho? Their operating cost is per token.
Charge on the input tokens, then you will naturally optimise for fewer output tokens.
Theoretically.
In reality, one sessions output tokens become the next sessions input tokens (at least if you continue the topic) so, its not as aligned as all that.
But the parent is right, when incentives are not aligned, friction will happen. Its inevitable.
“Claude, spend the next 10 hours trying to solve the Reimann Hypothesis”.
I agree that incentives are misaligned but there’s several competing model providers. If one gets funny with their costs people will jump ship, especially if the gap between the top 2 labs and everyone else keeps shrinking.
“I can take current sources and tell you how solved this is, but I am not willing to work to a timeframe or to solve things that aren’t yet solved by mathematicians or science”
These safeguards already exist when they get a whiff that you might be using Claude to fix security issues. Doesn’t seem farfetched given the incentives I outlined that they would apply to this kind of abuse.
How loose those controls are becomes a market force.
NeuralWatt just does it on energy consumption.
And tokens can be metered reliably. Unlike something like "task completion".
For OAI and Anthropic at least you can set a spend limit per response. Also tokens are well-defined.
LLM users don't want to put in effort, so they offload tasks to LLM.
LLM doesn't seem to be keen to put in effort either!
Is this AGI?
Anthropic's Generated Income
It's an interesting conversation - because at what point do you call it an abusive relationship, right? Maybe even ancillary to anthropomorphising an inanimate object - I've cancelled my Claude sub and I've shot question after question at it now (during the cancellation period), resulting in almost every reply with me asking it to "please speak normally". I will most definitely not be renewing my sub. I have no desire to engage with a non-human somehow managing to speak down to you, without answering the question.
EDIT: my honest opinion; Anthropic is building a person, whereas everybody else (it seems) is building a tool.
Is it actually entirely a prompt-based information? I’d assume that some of it is the harness part of the agent setting reasoning token budget and compacting reasoning etc.
In that case, the agent will respond incorrectly because it has no visibility into what reasoning mode it’s in.
IIRC responding to effort level settings appropriately is part of the (post)-training. In that case it could be considered another instance of the Bitter Lesson. Uplifting.
Does anyone know what setting effort means for models like these? Do they allow longer thinking sessions? Some kind of system prompts? What’s stopping someone from getting max effort output from low effort setting?
My understanding is that the current effort settings is a part of the system prompt, and that the levels and their intended results are a part pf the training process. The effect is more or less tokens spent reasoning before the model outputs a stop token. However it is as consistent as any other aspect of LLM behavior is..
I suspect it's not just this, there's plenty of 'optimization' around rubberbanding usage limits as well as routing to a different model in the backend. The incentives are too strong.
I've been using the API (shameless plug: via alyph.ai) and the difference is crazy.
The chat-based models are obviously being lobotomized based on personal usage and general load (e.g. PST business hours are worst).
API doesn't seem to be affected by this.
Don't know what is happening, but had to start using GLM-5.3 to fix Opus 5 errors even on primitive backend changes.
What’s currently the best pattern if I want to combine Fable and GPT if a workflow but keep using subsidized tokens?
Roll your own harness or use an open source harness with a Codex subscription.
I maintain a Claude subscription for Fable but seldom use it.
So glad I switched away from Anthropic. I'm certainly running into problems with OpenAI but nothing quite on the level of Anthropic's insufferability.
I mean whatever models I use (with Claude code) sub agents seem to use absurd amounts of tokens for trivial (or at least small) tasks.
Leaving thinking on extra high for a simple task is user mistake but they’re gonna try to fix it on their side.
I submitted an application for Anthropic's Cyber Verification Program.
I was approved.
3 months later, my approval was degraded into "in review" (revoked). I'm sure my account was flagged based on contents of debugging/researching firmwares/etc.
I opened a support ticket. No response. I opened another support ticket. No response.
1-2 weeks later, I got a response that I will not be re-approved and I need to reapply. No problem.
The page to reapply on does not allow me to re-apply because it my account is stuck in an "in review" status.
https://github.com/anthropics/claude-code/issues/84352
The community thinks it's a bug. I'm 95% sure it's not and a bunch of us who were previously approved had it revoked due to flagged content and will not be reapproved.
I switched to Codex + got TAC approved instantly and have not looked back. It's a shame. That's 100% separate from whatever the heck the quality of Opus 5's outputs are. The way it talks... insane. I would bet a good amount of money their next release will focus "reduced simplified responses" if I had to guess.
$2t company by the way
* Anthropic's Cyber Verification Program // Codex + gotTAC approved*
Meanwhile the Chinese models are "go ham dude"...
If it was not for capacity issues, Chinese models have a higher change to just dominate.
> $2t company by the way
It used to be that OpenAI and Anthropic had such a moat around them, that such a valuation was worth it. But these days, its gross overvalued (like so many).
The more stuff is being pulled like cyber verifications, downgrading effort levels, downgrading usage (OpenAI), the more people move to those Open Weight Chinese models.
A fun recent event ... https://opencode.ai/data/
When DeepSeek Flash 0731 came out and provided a massive jump in cheap inference capability. It resulted in a 10x increased OpenCode token usage.
It took a 2.5x to 5.0x price increase AND a reduction by 4x usage (later to 2x) usage, and several cheaper models + a free model, to push the traffic down.
Traffic towards open weight models is increasing, even if providers can not keep up with the influx of new customers. This is not something you want to see as two companies, trying to go for IPOs.
So the idea of stonewalling cyber capabilities, when the rest of the world is just doing whatever with open weight models, on their own hardware even! This entire strategy from Anthropic never made any sense.
I have been as well. Based on my own sessions, Max vs Max, same 1M context window size, the literal majority of the cost overhead of Opus vs Sonnet comes from Opus being chattier. So I started using Low reasoning instead of falling back to Sonnet, and I've been really happy with the results. Way better quality at a comparable spend. I also rarely go past High lately, which was another major cost save.
...the evidence, as best I can tell from the tweet, is that they asked Claude what effort level it was set to. But how would the model even know that?
Not convinced here.
I don’t know if this is still the case but while using Copilot if you looked through the chain-of-thought output you would see it reasoning about a “budget”. i.e. “since I’m close to the session budget I should…”. So it could be possible
Effort level is actually controlled entirely by system prompt (as I understand it, the model is trained on that format but still), so actually this is a valid way to check I think
I know that's true for Qwen but I don't think most models work that way?
OpenAI models also work this way, as evidenced by full cache blowout when changing reasoning level. Every single open-weight model I've seen also works this way (your "reasoning_effort" argument just changes a small section of the system prompt in the chat template). I would have to see some evidence to believe Anthropic were doing anything different.
Then model can say it's Opus, but really it is some old Sonnet. This seems to be happening less often, but some weeks ago I had to give models some test problems to gauge whether I am getting Opus or something knee-capped.
The problem is that Anthropic seems to be getting away with selling one thing and delivering another. You pay for Opus, you get something else etc.
Saw this in npx ccusage@latest claude output. Had only used opus but showed sonnet. Can't remember if the jsonl retains which model is doing what, but meh