I'm pretty baffled by their choice of axes. I would have thought that the left was the cheapest, not the most expensive. I appreciate that this layout means that top right can be best, but it's still unintuitive to have this backwards cost axis IMO.

Putting that aside, I spend all day every day implementing very, very hard things right on the edge of what agents are (barely, sometimes) capable of, and I have had to keep Opus on max for things that need 'real validation' for a while now. And that has felt like 'the only way' to get Opus to perform even close to 5.5 xhigh. I'm only using Opus at all because GPT-5.5 in the subscriptions only has a small (400k, but 258k effective) context window.

The difference is that 5.5 xhigh is extremely fast in most practical cases, both efficiently implementing _overall_, and responding very quickly with great adaptive thinking if you ask it something that it doesn't have to think about. Opus 4.8 Max will needlessly chew on everything and can take hours to implement even simple things, so I can mostly only use it for planning/review.

Fable is much much better at adaptive thinking / responding quickly (although probably still worse than 5.5 xhigh), and... I think folks have said enough elsewhere about its strengths and weaknesses. Sadly still not a reliable implementor for my hard tasks though (that's still GPT's domain) – it tends to leave big, dangerous holes hiding inside implementations unless babied.

I'm a bit skeptical.

Cursor's benchmark finds that Cursor's model (Composer 2.5) is basically as good as Opus 4.8 max and GPT-5.5 xhigh, but at a fraction of the price.

Artificial Analysis' testing shows Composer 2.5 to be pretty far behind: https://artificialanalysis.ai/agents/coding-agents. You look at the DeepSWE benchmark (which is probably the hardest to game at this point) and GPT-5.5 xhigh gets a 64, Opus 4.8 max gets 56, and Cursor 2.5 gets 16.

I don't doubt that Cursor works well for some people. It's beating DeepSeek v4 Pro in the DeepSWE benchmark and that's a very capable model. But I'm skeptical of the claims that it's a competitor for Opus 4.8 and GPT-5.5. It just seems convenient that their model does so well on their own benchmark while third party benchmarks have it far behind. Maybe it's a really great benchmark and a better measure than third party ones - I'd love for a cheap model to do as well as the expensive ones.

It's hard to believe Composer 2.5 is that good. I tried to compare it with GLM 5.2 or Opus 4.6 and it lacked thinking about the problem and critical reasoning. It's great for executing plans made by other models, but even then it does some weird code manipulation that is far from how other files around actually work.

everytime a new benchmark appears, Chinese models are far lower than the level where they are supposed to be according to existing benchmarks. then after a while they recover :)

Do these benchmarks even add any value at this point? This one is basically Cursor saying that their model is as good as the frontier ones at a fraction of the price. The independent benchmarks are probably part of training data now and the models are pattern-matching against them all the time. The final test of a model (and the harness, probably) is how good it works FOR YOU - since most of the models can pretty much do most of our tasks on a daily basis - it boils down to which one has the least friction to its usage.

Would like to see wall times. I feel that’s the part that annoys me most, my tasks aren’t particularly challenging I want them done fast

I've used both Composer 2.5 and GPT 5.5 (both in Cursor and in Codex) extensively, and their claim that Composer 2.5 is anywhere close in performance to GPT 5.5 is absolutely farcical. It's faster, but it's nowhere near as good.

And given that you can only use Composer with a Cursor monthly subscription, cost comparisons are pointless since an equivalently priced OpenAI subscription gets you just as much usage of the better model.

backwards X axis? is there a reason for that? it looks ridiculous

I feel like this benchmark reiterates my disbelief that anyone uses the latest Anthropic models for any productive work. They seem to be the best at burning tokens and spawning unnecessary subagents even for well-defined and tightly scoped tasks.

Can we get a count of people that have had Claude read irrelevant documents or perform unnecessary web searches even when told not to from the beginning?

I'm starting to wonder if this increased token usage is inadvertently bleeding into how Anthropic actually trains their model, especially leading up to IPO. As older models are deprecated and users are forced onto newer models, if the default is less efficient and more token expensive that directly results in higher "profit" for Anthropic in terms of the consumption their users have to tolerate - lest they jump to a competitor.

is composer 2.5 that good at that pricepoint? Seems like the gemini flash playbook of trying to get most bang for the buck.

[flagged]