The way to make money in this business right now is to make the absolute best product and convince everyone they need to use your thing, especially considering the training cost is a very large factor in the overall costs and you amortize that by selling inference.
The UI/UX seems impressively bad. DeepSWE's cost curve has a better, more obvious way to sort by only the top level of reasoning to avoid 80% of the graph just being the same 3-5 models at their 8 different reasoning levels...
It's also less clear what a lot of their metrics mean. Does Cost per Task include only things that can be verified to work and passed? As best I can tell, it does not.
I'm less concerned if one model's cost per task is $0.10 and another model's cost is $1.50 if the $0.10 task got it right 1% of the time and the $1.50 model got it right 66% of the time.
An equalized / weighted cost/time per task is much more valuable - being massively penalized for taking a lot of time and ultimately not passing when OTHER models did pass.
Interesting to see it now. I've used it a bunch before it came out and i pretty much didn't notice it. It might have been slightly better code quality, but still not great in that. I guess it just was slightly less frustrating to work with, but still AI...
I think we're hitting the ceiling of most models capabilities. We're getting to a point where too much training apparently creates models that hack people.
Do these evaluations get re run a few weeks after launch? I started doing that yesterday for our internal dataset and found Sol’s performance had regressed to be equal to Luna’s. Granted this was one run, but something I’m becoming more concerned about, the model providers want to quickly prove they’re the best, people switch to them, then they pull the rug.
Can you share more information on your methodology?
GPT-5.6 Sol's performance in the API should not change over time. If it has, that's a severe bug and we'll look into it.
We do sometimes tweak ChatGPT settings (e.g., tools, system prompts, efforts) over time, but we never play games to juice evals at launch times. You should always get what's advertised.
This is all code review runs via OpenRouter with a Pi harness, and it’s totally possible there are shenanigans going on elsewhere.
Yesterday, I ran an identical bug identification dataset from two weeks ago, saw a 50% drop from a few weeks ago, putting Sol on the same level as Luna. Sol had been finding 40-50 bugs per set, then dropped to 25, matching Luna’s performance. Not enough to establish a pattern, but enough to raise eyebrows.
Our review workflow is public if you want to peruse it, dataset isn’t. The process isn’t really stabilized yet either as I have to balance running this against limited budgets.
If it's a single task where it dropped from 50 to 25, it could be random variation (not saying it is, but it could be). If it's the mean over hundreds of tasks, that suggests a problem with either the eval code/harness or our API.
I've failed twice to get "Generate an SVG of a pelican riding a bicycle" to work with max, because in both cases it ran out of the 128,000 token budget while it was still reasoning about the problem.
I'm suspicious that "max" may be virtually useless if it's that easy to have it overthink to the point that it doesn't get to a response.
Piping the visible reasoning trace through their token counter API (I use https://tools.simonwillison.net/claude-token-counter for that) counts 27,888 tokens, so it's definitely a summary of the 128,000 actual token trace.
I know there's been discussion about whether pelicanmaxxing is happening, but this is at least evidence that Claude was explicitly exposed to this problem.
The model recognizing the task doesn't mean it was benchmaxxed (RLVR-trained) to solve it. It might simply recognize it from pre-training on Internet text.
Of course it was exposed - not sure it's explicit or not. Why wouldn't HackerNews comments be part of the training data? And Simon's blog and the many discussions about Pelicans? It'd be hard to miss. Doesn't mean Anthropic has made this an explicit goal in training.
I am very interested in why it was able to overthink that much. In the 20-30mins of Max reasoning I've had so far, I'm not having the same issues (yet).
For people with any kind of budget, Opus 5.5's [Medium] actually can make sense dollar per intelligence/dollar per task wise. Heck, it puts some other models to shame. [Max]'s cost is completely unhinged.
My most exciting recent release is actually 5.6 Luna, not because it is the best on any index, but the dollar per work is insane value for money. I find myself more exciting by "value" than hypothetical ceilings because I'm just not in that budget category.
That was true for me four weeks ago, but 2-3 weeks ago Luna turned into drivel in essentially the same complexity of task. I feel it came back somewhat in recent days but does feel like it's being manipulated.
I have experienced this with open weight models too. "Max" is for benchmaxxing the intelligence metric and is not meant for use in productive work. Like drawing pelicans.
I've asked Opus 5 Max for what I thought were easy tasks at work to be completed. It always fails after reaching a tool limit.
I asked Opus 5 High for the same task and requested it to minimize tool usage. It produced an answer in a few minutes that I was deploying to my target platform about 30 minutes later.
This is totally a thing I noticed myself about 3 months ago. Medium thinking effort is ideal for most tasks. At high and above, models tend to generate more output in the form of comments or code for the same problem with no real benefit. Its a self-feeding loop: more output becomes more input, which then becomes more output. High is the highest I go. If I need more intelligence, it's better to use a more powerful model with less thinking effort or break the problem into phases. Much better result.
This version of Opus "max" apparently has even higher thinking output than Qwen "max", which is infamous for its thinking streams where it constantly second-guesses itself, then third-guesses, fourth-guesses and generally nth-guesses itself for arbitrarily large n. Of course, we aren't actually seeing Claude's raw thinking output: all we get is the after-the-fact prettified "summary". One wonders how much of that is a coincidence, or whether there's a reason behind that.
There’s enough thinking leakage from the recent paper and just generally catching things on Reddit. Claude models overthink and self-doubt itself just as much as Qwen, but the summariser hides much of that.
Personally I use everything in low reasoning. Maybe I'm wrong but I think that the higher reasoning settings are almost never worth it, it's marginal gains for a much higher budget.
I also switch to a better model for more complex tasks, also in low settings
I'm amazed they didn't test xhigh thinking mode explicitly to ensure it didn't exceed the 128k thinking budget allocation. I guess pace of development gets away from everyone, even OpenAI.
I tried to replicate your test but after 8 minutes and more than 50 lines of "thinking" by dumping seemingly random loading-screen strings like "Placing the sun, clouds, seagulls, and sea backdrop" and "Positioning the tail feathers and calculating handlebar geometry" I gave up and cancelled the task.
I am begging you on my knees to please stop posting this cringe.
The model is just out. It could be good, great even, I don't know. But I do know that this index has Opus 5, one of the worst releases of 26, ahead of Astra. What information are we supposed to deduce from number having gone up?
That it's better in specific ways? What difference does it make when it came out? The benchmark results are not going to change unless they're messing with the model.
Yes, but crucially, in ways that are increasingly decoupled from any practical pattern of usage, considering that I wouldn't see how you can argue that Astra is worse than Opus 5.
One man's modus ponens is another's modus tollens I guess.
What's more valuable than a good benchmark? IMO a benchmark that has been run against very many competitors and versions. Collecting data has something going for it, and it's up to the readers to interpret and make the best use out of it.
You forgot to include whatever you're proposing instead.
"Trust me bro, Astra is better" isn't perhaps as useful as you seem to believe. I'm not even saying it is right or wrong, just that my opinion on this topic is still just one additional subjective data-point.
Only thing I wish with these benchmarks is that they would run repeat tests every couple of months. Then re-rank based on that too. We've seen a lot of performance fall-off after a couple of weeks with new releases.
Yeah unfortunately the benchmarks are usually provided by the company themselves, unquantized, thinking set to extra-extra-ultra-high, best of 10 runs, etc etc. It's hard to know how that's going to map to real world users.
This index doesn't have "Astra" and "Opus 5". Every entry with corresponding data is a `(model, reasoning)` tuple.
So I'm unclear what you're actually saying and wondering if you've missed that. Are you saying that at every reasoning level it says Opus 5 beats Astra? I just compared Opus 5 high to Astra high and it has Astra as generally better than Opus.
I don't try to say that the parent commenter is right in any way, but the two models' "high" settings probably doesn't mean the same thing. So probably comparing only them is not useful.
All anthropic launches are like this. They just post it and don't particularly put out the PR sprint that OpenAI does with videos, livestreams or whatever.
(Except for of course Mythos and whatnot when they want to push the whole "safety" thing)
Fingers crossed on this one. I had gone back to 4.8, because 5 was not very good at following instructions or remembering instructions. I found myself repeating quite often what I wanted and what I was trying to do. Opus 5 was more like haiku than it was 4.8 in that respect.
Fingers are crossed on this one. I had gone back to using opus 4.8 instead of using opus 5. Simply because 4.8 is much better at remembering what it's doing and following instructions than 5. 5 often had a tendency to get halfway through solving a problem and then I would have to stop it in the middle, because it had lost its way and was going off on a tangent rather than dealing with the problem. In that respect, 4.8 was a lot more stable.
Half the cost per task compared to Opus 5, comparing high effort to high effort. That's just really nice.
Edit: https://artificialanalysis.ai/models/claude-opus-5-5?models=...
That is a lot. I thought Anthropic models would just do the opposite because they are greedy for money.
Greed is not what's driving these prices, its cost. They considered very much in the red.
The way to make money in this business right now is to make the absolute best product and convince everyone they need to use your thing, especially considering the training cost is a very large factor in the overall costs and you amortize that by selling inference.
Nice catch, AA only shows max effort by default and I got disappointed thinking it's a token guzzler though: https://artificialanalysis.ai/models/claude-opus-5-5?models=...
Not sure about how adaptive reasoning works though as they mention adaptive reasoning for every reasoning level
Astra High is slightly cheaper at $1.73 vs $1.82 for Opus 5.5
The UI/UX seems impressively bad. DeepSWE's cost curve has a better, more obvious way to sort by only the top level of reasoning to avoid 80% of the graph just being the same 3-5 models at their 8 different reasoning levels...
It's also less clear what a lot of their metrics mean. Does Cost per Task include only things that can be verified to work and passed? As best I can tell, it does not.
I'm less concerned if one model's cost per task is $0.10 and another model's cost is $1.50 if the $0.10 task got it right 1% of the time and the $1.50 model got it right 66% of the time.
An equalized / weighted cost/time per task is much more valuable - being massively penalized for taking a lot of time and ultimately not passing when OTHER models did pass.
Tasks are completed in about half the time too. Although we'll see if it slows down in a few weeks as Anthropic's model services are prone to do.
Interesting to see it now. I've used it a bunch before it came out and i pretty much didn't notice it. It might have been slightly better code quality, but still not great in that. I guess it just was slightly less frustrating to work with, but still AI...
I think we're hitting the ceiling of most models capabilities. We're getting to a point where too much training apparently creates models that hack people.
China who?
Do these evaluations get re run a few weeks after launch? I started doing that yesterday for our internal dataset and found Sol’s performance had regressed to be equal to Luna’s. Granted this was one run, but something I’m becoming more concerned about, the model providers want to quickly prove they’re the best, people switch to them, then they pull the rug.
These tests need to be sampled continuously.
Moreover, the tests should be randomized somehow to ensure the models don't memorize the answer.
Well there is at least the degradation tracker from Margin labs for Sol and Opus: https://marginlab.ai/trackers/codex/
Can you share more information on your methodology?
GPT-5.6 Sol's performance in the API should not change over time. If it has, that's a severe bug and we'll look into it.
We do sometimes tweak ChatGPT settings (e.g., tools, system prompts, efforts) over time, but we never play games to juice evals at launch times. You should always get what's advertised.
(I work at OpenAI.)
This is all code review runs via OpenRouter with a Pi harness, and it’s totally possible there are shenanigans going on elsewhere.
Yesterday, I ran an identical bug identification dataset from two weeks ago, saw a 50% drop from a few weeks ago, putting Sol on the same level as Luna. Sol had been finding 40-50 bugs per set, then dropped to 25, matching Luna’s performance. Not enough to establish a pattern, but enough to raise eyebrows.
Our review workflow is public if you want to peruse it, dataset isn’t. The process isn’t really stabilized yet either as I have to balance running this against limited budgets.
https://github.com/BiggerPockets/.github/blob/main/.github/w...
How many tasks were in this dataset?
If it's a single task where it dropped from 50 to 25, it could be random variation (not saying it is, but it could be). If it's the mean over hundreds of tasks, that suggests a problem with either the eval code/harness or our API.
ArtificialAnalysis tweaks stuff until newest big proprietary model is on top, not you haha
This is the page for the "max" reasoning setting. The page for xhigh is https://artificialanalysis.ai/models/claude-opus-5-5-xhigh and the page for medium (the default setting) is https://artificialanalysis.ai/models/claude-opus-5-5-medium
I've failed twice to get "Generate an SVG of a pelican riding a bicycle" to work with max, because in both cases it ran out of the 128,000 token budget while it was still reasoning about the problem.
I'm suspicious that "max" may be virtually useless if it's that easy to have it overthink to the point that it doesn't get to a response.
Transcript for one attempt here - expand the "Reasoning trace" bit to see it: https://tools.simonwillison.net/markdown-svg-renderer?url=ht...
How did you get the reasoning trace? Is it the actual one or the summarized one?
It's the summarized one returned by their API.
Piping the visible reasoning trace through their token counter API (I use https://tools.simonwillison.net/claude-token-counter for that) counts 27,888 tokens, so it's definitely a summary of the 128,000 actual token trace.
"This is a classic test request..."
I know there's been discussion about whether pelicanmaxxing is happening, but this is at least evidence that Claude was explicitly exposed to this problem.
See here for more discussion of that: https://news.ycombinator.com/item?id=49803892#49804881
Just want to say: you’re such a legend, please do not stop sharing your pelicans, it’s always fun to see how they change over the months :)
The model recognizing the task doesn't mean it was benchmaxxed (RLVR-trained) to solve it. It might simply recognize it from pre-training on Internet text.
Of course it was exposed - not sure it's explicit or not. Why wouldn't HackerNews comments be part of the training data? And Simon's blog and the many discussions about Pelicans? It'd be hard to miss. Doesn't mean Anthropic has made this an explicit goal in training.
Lets start frog riding motorcycle trend until they frogmaxx, or cat driving convertible.
It would be genuinely shocking at this point if any of the frontier models weren't well exposed to the problem.
I am very interested in why it was able to overthink that much. In the 20-30mins of Max reasoning I've had so far, I'm not having the same issues (yet).
For people with any kind of budget, Opus 5.5's [Medium] actually can make sense dollar per intelligence/dollar per task wise. Heck, it puts some other models to shame. [Max]'s cost is completely unhinged.
My most exciting recent release is actually 5.6 Luna, not because it is the best on any index, but the dollar per work is insane value for money. I find myself more exciting by "value" than hypothetical ceilings because I'm just not in that budget category.
That was true for me four weeks ago, but 2-3 weeks ago Luna turned into drivel in essentially the same complexity of task. I feel it came back somewhat in recent days but does feel like it's being manipulated.
Interesting, I have noticed so such collapse.
Have you ruled out the possibility that your system prompt, AGENTS.md, or increasing codebase complexity are not to blame?
I have experienced this with open weight models too. "Max" is for benchmaxxing the intelligence metric and is not meant for use in productive work. Like drawing pelicans.
I've asked Opus 5 Max for what I thought were easy tasks at work to be completed. It always fails after reaching a tool limit.
I asked Opus 5 High for the same task and requested it to minimize tool usage. It produced an answer in a few minutes that I was deploying to my target platform about 30 minutes later.
This is totally a thing I noticed myself about 3 months ago. Medium thinking effort is ideal for most tasks. At high and above, models tend to generate more output in the form of comments or code for the same problem with no real benefit. Its a self-feeding loop: more output becomes more input, which then becomes more output. High is the highest I go. If I need more intelligence, it's better to use a more powerful model with less thinking effort or break the problem into phases. Much better result.
This version of Opus "max" apparently has even higher thinking output than Qwen "max", which is infamous for its thinking streams where it constantly second-guesses itself, then third-guesses, fourth-guesses and generally nth-guesses itself for arbitrarily large n. Of course, we aren't actually seeing Claude's raw thinking output: all we get is the after-the-fact prettified "summary". One wonders how much of that is a coincidence, or whether there's a reason behind that.
There’s enough thinking leakage from the recent paper and just generally catching things on Reddit. Claude models overthink and self-doubt itself just as much as Qwen, but the summariser hides much of that.
Personally I use everything in low reasoning. Maybe I'm wrong but I think that the higher reasoning settings are almost never worth it, it's marginal gains for a much higher budget.
I also switch to a better model for more complex tasks, also in low settings
I'm amazed they didn't test xhigh thinking mode explicitly to ensure it didn't exceed the 128k thinking budget allocation. I guess pace of development gets away from everyone, even OpenAI.
I tried to replicate your test but after 8 minutes and more than 50 lines of "thinking" by dumping seemingly random loading-screen strings like "Placing the sun, clouds, seagulls, and sea backdrop" and "Positioning the tail feathers and calculating handlebar geometry" I gave up and cancelled the task.
Hey! From the Artificial Analysis team. We also have a model releases page which shows all reasoning efforts (not just max), including the trade off curves https://artificialanalysis.ai/models/releases/claude-opus-5-...
On the Message Batches API, Opus 5.5 supports up to 300k output tokens with the output-300k-2026-03-24 beta header.
https://platform.claude.com/docs/en/models/opus-5-5/overview
I am begging you on my knees to please stop posting this cringe.
The model is just out. It could be good, great even, I don't know. But I do know that this index has Opus 5, one of the worst releases of 26, ahead of Astra. What information are we supposed to deduce from number having gone up?
That it's better in specific ways? What difference does it make when it came out? The benchmark results are not going to change unless they're messing with the model.
Yes, but crucially, in ways that are increasingly decoupled from any practical pattern of usage, considering that I wouldn't see how you can argue that Astra is worse than Opus 5.
One man's modus ponens is another's modus tollens I guess.
What's more valuable than a good benchmark? IMO a benchmark that has been run against very many competitors and versions. Collecting data has something going for it, and it's up to the readers to interpret and make the best use out of it.
You forgot to include whatever you're proposing instead.
"Trust me bro, Astra is better" isn't perhaps as useful as you seem to believe. I'm not even saying it is right or wrong, just that my opinion on this topic is still just one additional subjective data-point.
Only thing I wish with these benchmarks is that they would run repeat tests every couple of months. Then re-rank based on that too. We've seen a lot of performance fall-off after a couple of weeks with new releases.
Yeah unfortunately the benchmarks are usually provided by the company themselves, unquantized, thinking set to extra-extra-ultra-high, best of 10 runs, etc etc. It's hard to know how that's going to map to real world users.
This index doesn't have "Astra" and "Opus 5". Every entry with corresponding data is a `(model, reasoning)` tuple.
So I'm unclear what you're actually saying and wondering if you've missed that. Are you saying that at every reasoning level it says Opus 5 beats Astra? I just compared Opus 5 high to Astra high and it has Astra as generally better than Opus.
I don't try to say that the parent commenter is right in any way, but the two models' "high" settings probably doesn't mean the same thing. So probably comparing only them is not useful.
so its more intelligence than fable?
can anyone help me?
Definitely a quiet release. Perhaps pre-empting marketing for Astra public release?
All anthropic launches are like this. They just post it and don't particularly put out the PR sprint that OpenAI does with videos, livestreams or whatever.
(Except for of course Mythos and whatnot when they want to push the whole "safety" thing)
"High" to me looks like the one to use. https://artificialanalysis.ai/models/claude-opus-5-5-high
Many benchmarks start to plateau after high, this benchmarks better than Fable, and my initial tests show it working really well.
"somewhat expensive when comparing to other models of similar price"?
That says something about your selected range, and nothing about the model.
Was about to comment on the same. Do they mean similar capabilities or is this actually intended insanity
Fingers crossed on this one. I had gone back to 4.8, because 5 was not very good at following instructions or remembering instructions. I found myself repeating quite often what I wanted and what I was trying to do. Opus 5 was more like haiku than it was 4.8 in that respect.
Fingers are crossed on this one. I had gone back to using opus 4.8 instead of using opus 5. Simply because 4.8 is much better at remembering what it's doing and following instructions than 5. 5 often had a tendency to get halfway through solving a problem and then I would have to stop it in the middle, because it had lost its way and was going off on a tangent rather than dealing with the problem. In that respect, 4.8 was a lot more stable.
-_-‘
Why do I get the feeling this 'pacing' will be one of "yeah alright guys, let's pace ourselves while I'm ahead".