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.
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.
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.
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'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.
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 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).
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.
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?
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.
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.
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.
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.
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)
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...
"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.
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.
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'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.
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.
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).
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.
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.
Astra High is slightly cheaper at $1.73 vs $1.82 for Opus 5.5
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
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?
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.
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.
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.
"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.
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.
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)
so its more intelligence than fable?
can anyone help me?
China who?