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.
I've had no problems like the ones you've mentioned while using Opus 4.8. It does overthink stuff with higher effort levels but that's kind of expected.
Now that enterprise customers are pay-as-you-go with tokens I suspect we'll see renewed interest in OpenAI and their focus on token efficiency. At least I hope so if the alternative is abandoning the tools entirely.
I'm also using it as my daily driver. I've been trying Opus 4.8 this week to see if I was missing something but haven't noticed a meaningful difference.
I'm working on a fairly routine full stack web app that isn't doing anything incredible. Once I had the patterns I wanted in place, it's been very capable of following those with new work. I also don't ever give it long running tasks, it's always focused and small chunks.
My typical work flow is
1. /grill-me feature description
2. Create a plan
3. Manually review plan and tweak as needed (usually very little to none)
4. Build the plan
All with Composer 2.5. Earlier on in the project I used Claude and GPT for #1 and #2.
I find it really hard to justify the other models for the performance/cost I'm getting with Composer 2.5. Maybe it's not as strong as the frontier models, but it's been plenty good enough for my use cases.
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.
A brainwave: perhaps GLM or DeepSeek could be integrated into the mix for the purposes of red-teaming the code. Fable has been blinded to security by design, and the open models are pretty decent at it.
I definitely use GPT-5.5 as a counterpart to validate these exact sorts of things in Anthropic models' implementations, in the (now-rarer) cases where I allow Anthropic's models _to_ implement.
And yeah, it's a bit depressing to think that 5.6 might be similarly nerfed. Less secure software for us all, I guess... except BigCorps. :(
> it tends to leave big, dangerous holes hiding inside implementations unless babied
it's fascinating that I used these same exact words to express my distaste for Composer and my preference for Opus. I suspect, the domains and problems we are trying to solve need to be shared. I wrote about it here: https://news.ycombinator.com/item?id=48766275
Would love to reach out to discuss more, if you're ok with it, or absolutely feel free to do the same as my email's in the profile like yours!
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.
I primarily use composer. I wanted to build something from scratch recently and, thinking I was missing out on something, I got Opus to build it. I wasn't blown away. I gave the same prompts to composer and the code it came up with different but similar in quality. I ended up progressing with the composer code because it was easier to progress with improvements due to its faster response time.
It’s starting to feel like people need to say what language/stack and problem space they’re working in. It would be interesting to see why we’re seeing such wild variance.
Not hard to understand what's going on here. They RL'd around patterns in their data and specific capabilities, so of course they'd construct a benchmark that's aligned with the training set.
Ironically, their benchmark might be more accurate than artificial analysis for a narrow slice of things that Cursor's Eigencustomer is really interested in. Otherwise I'd take it as just another data point.
(I work at Cursor) CursorBench includes many evals from actual engineering tasks from the Cursor team, which include our private codebase. This codebase is held-out from training so models haven't seen it, including Composer.
(I work at Cursor) When Composer 2.5 launched, we initially scored very competitively on AA's composite benchmark. I believe 3rd place overall. They have recently updated to use DeepSWE, which has more of a focus on very long-horizon tasks, and Composer isn't as good at those yet. We're aware and working on this for our next model.
Overall, some benchmarks show Composer doing well, others not so much. We think the model is very capable at the given price point. There's lots to improve! If you see any specific behaviors or places the model isn't very good, lmk here or can email me lrobinson at cursor.com.
How does it compare to a $100 Claude subscription at $60? Especially in terms of how much of it I can use, because I havent found anything that is in the US that can get me similar usage as Claude at $100 per month or less, really open to alternatives.
Grok build only gave me roughly 10 hours of use for $40 for the entire month...
I don't even care about long horizon, can I use it a reasonable amount of time through the month? I use AI for hobby projects, Claude gets me quite far, but I tire of dropping $100 every month. I'm not sending my money to some Chinese firm that now has access to my computer.
Even with the new benchmark, Composer 2.5 seems to be just a bit worse than Opus 4.7. So I assume it's going to be about similar with Sonnet 5.0 at 1/6 of the cost.
> We think the model is very capable at the given price point.
The "price point" comparison is a lie though because Composer is only available with a monthly Cursor subscription, and Cursor's external-model-per-token charges for other models are not representative of what other models' monthly subscribers get. An OpenAI $200 subscription gets you at least as much GPT 5.5 as a $200 Cursor subscription gets you Composer 2.5.
> 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.
Your skepticism is well-founded IMHO. I have found that if you are one-shotting a Django/Next CRUD app, a typical React/Vue UI, shell scripts or GitHub Actions, Composer 2.5 is fantastic!
But for anything outside the median of the last decade's web development - like free-body physics, kinematics, or optimization - Composer is horriblyunpredictable.
That's what makes it _dangerous_ IMHO.
It isn't universally trash! Rather, it confidently makes subtle, incorrect assumptions. It will hallucinate formulas that don't appear in your specification and design docs. Then write tests that pass it.
It inserts tiny footguns that require you to scrutinize every single token it generates. At that point, I would rather be coding by hand.
Opus 4.8 max, on the other hand, refuses to guess, atleast the way I have set it up. If there's any ambiguity about the implementation or how tests should be written, it stops and asks me for clarification. I actually trust the output without worrying about hidden disasters and ticking timebombs. I can confidently review the test suite, add a few edge cases on my own, spot check the code and be comfortable knowing there are no disastrous footguns lurking in the shadows only to come out in the darkness of production deployments.
Let me repeat - Opus 4.8 max stops and asks me for clarification. It writes the tests I would have written. It writes tests that fail, exposing gaps and errors, that then allows me to iterate.
Composer 2.5 OTOH will run with whatever it decides I meant and write something that steals productivity, not add to it.
Same harness (Cursor), same rules, same prompts, vastly different outcomes!
Yes, Opus is far more expensive, but it's worth it for the time saved on review and refactors, which are our current blockers.
The real friction is that Cursor's marketing is so aggressive that the people paying the bills look at my Opus usage and demand to know why I'm not using the cheaper alternative!
It's an impossible argument to win when the rest of the company's devs are happily building standard web apps on Composer without issue, blissfully unaware of how the model not only falls apart but is just unreliable on harder engineering problems.
Fable 5 is on a league on its own. If history in the LLM space is any predictor of the future, in ~6 months (Q1 2027) we should have open weight models that are competitive with Fable 5. Without considering what it will take to run such a thing, I would be extremely excited to have open access to such a capability. Great times ahead!
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.
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.
I read these and think it is just the jagged edge. I do not doubt your personal experience, I have used Composer 2.5 (via Grok and the credits I get with my X premium account) the past month.
I am not building rockets, but have been quite impressed. All the models do dumb things sometimes, it has done the work I have asked it to pretty well though and has done to me some impressive work.
It is fast on Grok, for other models I have worked extensively with I think it is better than gemini 3.1 (3.5 and antigravity for me is worse than the prior gemini cli). And is comparable to Opus 4.6. (Have not used the more recent models in Claude Code.)
I'm not using Cursor at the moment, but when I did (not too long ago) my experience was similar. Plan with Opus, implement with Composer, clean up with Opus.
Composer did a competent but not amazing job with a good plan. What I really liked though is it was fast! Opus could take 30 minutes to do something Composer would get done in 5-10 minutes. Of course the output wasn't perfect, but that's why I'd do a cleanup pass using Opus or Codex.
It's all a balance though, constantly changing and completely dependent on the problem you're solving. I just remain flexible and adapt my process to what's working best in the moment.
Interesting. If I may: What was this "clean up" pass? A code review? A code review with specialized prompt? A focused review to check for edge cases / logic errors / api misuse? Or, something else specific to the codebase?
Have you settled on what the clean up pass should look like? Or, do you keep experimenting with it?
In case one might not have been aware: Composer 2 was Kimi Base 2.5 post-trained (RL'd) by Cursor: https://news.ycombinator.com/item?id=48507474. Composer 2.5 might be something totally different.
I end every project with a long interrogation session. Why did you do this? Is there a better approach you didn't consider? Do the naming conventions follow the project idioms? Justify the decision you made here, providing evidence. I disagree with your approach, etc. etc.
Doesn't matter which model wrote the code, they all make mistakes. This is the same stuff I'd do with any junior engineer's PR and it leads to better quality outcomes (something I care about and am finding hard to let go).
Fable is particularly good at having this back and forth I'm discovering.
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.
Cursor’s model excels at Cursor’s benchmark; news at 11.
The other models however are reasonably where I’d expect them to be from experience piloting all of them. Fable is outclassing everything at most things at 10x the cost, but sometimes it isn’t a choice between cheap and expensive, but expensive and possible; I’ll need to learn where that boundary is just as it was the case with other models.
I wish all these sites would show pareto frontier graphs of cost/performance. That's the main 2 things that matter (I guess you could make it 3D with a speed param as well). https://paraplouis.github.io/llm-pareto-frontier/ is the best of these graphs I've seen but it doesn't update as frequently as I'd like.
That site is useless though because thinking tokens (and caching) and the efficiency thereof aren't accounted for. GLM5.2 is promoted by every 50 Cent Party the PLA can muster on the internet but it falls short because of its extremely verbose thinking. Anthropic models have the same problem but starting from a much higher base of real intelligence.
Which is exactly why every credible comparison now represents cost associated with completing a task, not arbitrary input and output token costs.
Not sure how much "real intelligence" is to be found in Mythos & Sol, but at this point, ignoring the intelligence gap, I find it totally impressive that the likes of GLM, Kimi, Qwen, MiMo hold their own at 2x to 4x less cost, and work for my use case just the same.
Why would anyone take this benchmark seriously? Cursor is obviously biased here. They can design it and its presentation however they want to tell the story they want to tell.
(I work at Cursor) We score well on Terminal-Bench and SWE-bench Multilingual. DeepSWE, not so great yet, as it's more for very long-horizon tasks. We're planning to include more public benchmarks in our next model release.
Interesting that Opus 4.7 does better than 4.8. Too bad they didn't test 4.6, too. I witnessed a man here mocked yesterday for insisting it was better than its successors!
Although, the benchies are always tricksy ... On DeepSWE, GPT-5.5 beats Opus-4.8, by a fair margin, but on FrontierCode, the situation is the other way around.
The only benchmark you can trust is your actual workload!
Fable is using less tokens to achive that same tasks compared to sonet and opus. If so that is a good thing. It feels like we for a while there was spitting out tokens to get a better result. If the model themselves are getting better without generating more tokens that feels like a real win.
Q1: Why is number of steps relevant in this graph? What does it tell us?
Q2: and why have they flipped the horizontal graph so that 0 is to the right and not at origo? Is that some kind of new smart thing? can't say i have seen it before
Very skeptical about the composer accuracy. I have been using it for 6 months now and it is very fast, especially compared to anthropic models, but the result it produces, especially with more difficult tasks is very shallow. It feels like it just finds the cheapest way to deliver the task.
Composer 2.5 is really effective at some tasks, but doesn't do as well on higher complexity tasks from my own experience.
With that being said, Cursor Bench is nonetheless a fantastic gauge of LLM quality and performance. The most interesting outcome of Cursor Bench is Fable 5 to GPT 5.5, its almost a perfect continuous line. As of the report, Fable to GPT might be the new standard of agentic programming/building
I did my own rounds of tests for a lot of models.
I made multiple rounds of comparisons for issues in my open source project using Fable 5, GPT 5.5, Opus 4.8, and Composer 2.5
Made them work on various complexity issues
Here are the reports, i recommend using a Sonnet 5 or better model to summarize them because its quite a bit of information to consume across all the various tests: https://www.richkuo.com/#llm-battles
My takeaway from Composer 2.5 is that its better to use Opus or GPT for planning, and then Composer to build, use Opus/GPT to pr review, and have Composer fix findings, and loop that process. Incredibly token efficient, fast, and gets the same quality as if you worked the whole thing with Fable/Opus/GPT.
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[ 3.0 ms ] story [ 78.3 ms ] threadCan 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.
I'm working on a fairly routine full stack web app that isn't doing anything incredible. Once I had the patterns I wanted in place, it's been very capable of following those with new work. I also don't ever give it long running tasks, it's always focused and small chunks.
My typical work flow is 1. /grill-me feature description 2. Create a plan 3. Manually review plan and tweak as needed (usually very little to none) 4. Build the plan
All with Composer 2.5. Earlier on in the project I used Claude and GPT for #1 and #2.
I find it really hard to justify the other models for the performance/cost I'm getting with Composer 2.5. Maybe it's not as strong as the frontier models, but it's been plenty good enough for my use cases.
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.
A brainwave: perhaps GLM or DeepSeek could be integrated into the mix for the purposes of red-teaming the code. Fable has been blinded to security by design, and the open models are pretty decent at it.
And yeah, it's a bit depressing to think that 5.6 might be similarly nerfed. Less secure software for us all, I guess... except BigCorps. :(
To put their own model out in front?
it's fascinating that I used these same exact words to express my distaste for Composer and my preference for Opus. I suspect, the domains and problems we are trying to solve need to be shared. I wrote about it here: https://news.ycombinator.com/item?id=48766275
Would love to reach out to discuss more, if you're ok with it, or absolutely feel free to do the same as my email's in the profile like yours!
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.
Didn't notice yours until now.
Vim gives you that by highlighting the lines and running `:sort` for free
Ironically, their benchmark might be more accurate than artificial analysis for a narrow slice of things that Cursor's Eigencustomer is really interested in. Otherwise I'd take it as just another data point.
Overall, some benchmarks show Composer doing well, others not so much. We think the model is very capable at the given price point. There's lots to improve! If you see any specific behaviors or places the model isn't very good, lmk here or can email me lrobinson at cursor.com.
Grok build only gave me roughly 10 hours of use for $40 for the entire month...
I don't even care about long horizon, can I use it a reasonable amount of time through the month? I use AI for hobby projects, Claude gets me quite far, but I tire of dropping $100 every month. I'm not sending my money to some Chinese firm that now has access to my computer.
1) Fork VSCode and pretend that they made some kind of "AI IDE"
2) Fork a Chinese model and pretend that they trained their own model
Company with zero moat and terrible products that keeps lying to everybody
The "price point" comparison is a lie though because Composer is only available with a monthly Cursor subscription, and Cursor's external-model-per-token charges for other models are not representative of what other models' monthly subscribers get. An OpenAI $200 subscription gets you at least as much GPT 5.5 as a $200 Cursor subscription gets you Composer 2.5.
Your skepticism is well-founded IMHO. I have found that if you are one-shotting a Django/Next CRUD app, a typical React/Vue UI, shell scripts or GitHub Actions, Composer 2.5 is fantastic!
But for anything outside the median of the last decade's web development - like free-body physics, kinematics, or optimization - Composer is horribly unpredictable.
That's what makes it _dangerous_ IMHO.
It isn't universally trash! Rather, it confidently makes subtle, incorrect assumptions. It will hallucinate formulas that don't appear in your specification and design docs. Then write tests that pass it.
It inserts tiny footguns that require you to scrutinize every single token it generates. At that point, I would rather be coding by hand.
Opus 4.8 max, on the other hand, refuses to guess, atleast the way I have set it up. If there's any ambiguity about the implementation or how tests should be written, it stops and asks me for clarification. I actually trust the output without worrying about hidden disasters and ticking timebombs. I can confidently review the test suite, add a few edge cases on my own, spot check the code and be comfortable knowing there are no disastrous footguns lurking in the shadows only to come out in the darkness of production deployments.
Let me repeat - Opus 4.8 max stops and asks me for clarification. It writes the tests I would have written. It writes tests that fail, exposing gaps and errors, that then allows me to iterate.
Composer 2.5 OTOH will run with whatever it decides I meant and write something that steals productivity, not add to it.
Same harness (Cursor), same rules, same prompts, vastly different outcomes!
Yes, Opus is far more expensive, but it's worth it for the time saved on review and refactors, which are our current blockers.
The real friction is that Cursor's marketing is so aggressive that the people paying the bills look at my Opus usage and demand to know why I'm not using the cheaper alternative!
It's an impossible argument to win when the rest of the company's devs are happily building standard web apps on Composer without issue, blissfully unaware of how the model not only falls apart but is just unreliable on harder engineering problems.
Fable 5 is on a league on its own. If history in the LLM space is any predictor of the future, in ~6 months (Q1 2027) we should have open weight models that are competitive with Fable 5. Without considering what it will take to run such a thing, I would be extremely excited to have open access to such a capability. Great times ahead!
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.
I am not building rockets, but have been quite impressed. All the models do dumb things sometimes, it has done the work I have asked it to pretty well though and has done to me some impressive work.
It is fast on Grok, for other models I have worked extensively with I think it is better than gemini 3.1 (3.5 and antigravity for me is worse than the prior gemini cli). And is comparable to Opus 4.6. (Have not used the more recent models in Claude Code.)
Composer did a competent but not amazing job with a good plan. What I really liked though is it was fast! Opus could take 30 minutes to do something Composer would get done in 5-10 minutes. Of course the output wasn't perfect, but that's why I'd do a cleanup pass using Opus or Codex.
It's all a balance though, constantly changing and completely dependent on the problem you're solving. I just remain flexible and adapt my process to what's working best in the moment.
Have you settled on what the clean up pass should look like? Or, do you keep experimenting with it?
In case one might not have been aware: Composer 2 was Kimi Base 2.5 post-trained (RL'd) by Cursor: https://news.ycombinator.com/item?id=48507474. Composer 2.5 might be something totally different.
Doesn't matter which model wrote the code, they all make mistakes. This is the same stuff I'd do with any junior engineer's PR and it leads to better quality outcomes (something I care about and am finding hard to let go).
Fable is particularly good at having this back and forth I'm discovering.
The other models however are reasonably where I’d expect them to be from experience piloting all of them. Fable is outclassing everything at most things at 10x the cost, but sometimes it isn’t a choice between cheap and expensive, but expensive and possible; I’ll need to learn where that boundary is just as it was the case with other models.
Which is exactly why every credible comparison now represents cost associated with completing a task, not arbitrary input and output token costs.
Not sure how much "real intelligence" is to be found in Mythos & Sol, but at this point, ignoring the intelligence gap, I find it totally impressive that the likes of GLM, Kimi, Qwen, MiMo hold their own at 2x to 4x less cost, and work for my use case just the same.
Although, the benchies are always tricksy ... On DeepSWE, GPT-5.5 beats Opus-4.8, by a fair margin, but on FrontierCode, the situation is the other way around.
The only benchmark you can trust is your actual workload!
Fable is using less tokens to achive that same tasks compared to sonet and opus. If so that is a good thing. It feels like we for a while there was spitting out tokens to get a better result. If the model themselves are getting better without generating more tokens that feels like a real win.
Q1: Why is number of steps relevant in this graph? What does it tell us?
Q2: and why have they flipped the horizontal graph so that 0 is to the right and not at origo? Is that some kind of new smart thing? can't say i have seen it before
Compared with how much it can do in such little time, it's still far less than even a junior engineer.
With that being said, Cursor Bench is nonetheless a fantastic gauge of LLM quality and performance. The most interesting outcome of Cursor Bench is Fable 5 to GPT 5.5, its almost a perfect continuous line. As of the report, Fable to GPT might be the new standard of agentic programming/building
I did my own rounds of tests for a lot of models.
I made multiple rounds of comparisons for issues in my open source project using Fable 5, GPT 5.5, Opus 4.8, and Composer 2.5
Made them work on various complexity issues
Here are the reports, i recommend using a Sonnet 5 or better model to summarize them because its quite a bit of information to consume across all the various tests: https://www.richkuo.com/#llm-battles
My takeaway from Composer 2.5 is that its better to use Opus or GPT for planning, and then Composer to build, use Opus/GPT to pr review, and have Composer fix findings, and loop that process. Incredibly token efficient, fast, and gets the same quality as if you worked the whole thing with Fable/Opus/GPT.