While this benchmark has interesting results, the "Contamination free" label only works for the initial release of the benchmark. It still has the same fundamental design issues of any other benchmark-- there's a single correct answer for tasks. It looks to be largely saturated upon release.
What they did well: normalizing the harness to mini-swe-agent -- models should be able to generalize to different tools at this point. When they struggle to do that (like most Google models), they're unlikely to be useful in practice. And that kind of generalization is an inherent part of intelligence.
For a benchmark that scales, you need to remove the ceiling and provide environments with measurable goals that are NOT a single correct answer, and sufficiently complex evaluation criteria to scale well beyond the current frontier.
We're still relatively unknown in the benchmarking space, but by rotating the pool of environments and ensuring the optimal strategies in the environments themselves are affected by other agents participating in the space, we expect we'll be able to resist contamination as major labs start investing more effort to climb the leaderboard. We've already seen Chinese labs taking an interest.
The fact that claude and gpt 5.5 have nearly the same scores tells me your benchmark is not capturing a significant gap in capability between these two. What the linked page says about Claude is true in my experience: It frequently forgets important instructions and likes to take lazy shortcuts. Gpt by contrast is much more attentive and takes its time when needed to deliver a complete and robust solution. I have tested both models on two private repos (c#, go) on two long-horizon tasks with well-defined stop conditions and observed the same pattern in both cases. Both models still require a large harness to reduce shortcuts and architecturally unclean code, but gpt performs much better, to the point where I find claude unusable for any significant work.
GPT 5.5 does significantly outperform Opus 4.7 in the coding parts of our evals.
We also incorporate live decision making on social games (where GPT 5.5 has actually regressed from earlier models, which shouldn't be a huge surprise if you ever tried talking it out of some of its nits).
We are still looking for a way to integrate "logical" intelligence with social intelligence in a less arbitrary way, so I'd take a look at the use case that applies to you (probably coding): https://gertlabs.com/rankings?mode=agentic_coding
This benchmark matches my experience with GPT (I occasionally go back to Claude when I run into limits and frequently run into forgotten requirements and reward hacking)
I do have two questions / critiques:
- The verifier doesn't seem to check for code quality / maintainability, which I would posit is one of the major qualms with SOTA coding models i.e. they lack code 'taste'. Ofc this is a difficult problem to solve at scale, but wanted to point that out nonetheless
- This almost feels written like a critique on SWE Bench Pro. Hopefully they fix the issues with that benchmark!
It seems like GPT here is failing due to an environment issue of connecting to chromium, even though its local unit tests passed. All the models failed 4/4 and checking Opus it ran into the same problem
I checked some other tasks and they seemed legit, although in general the prompts seem somewhat contrived vs. what a typical user would ask their coding agent (such is the difficulty of benchmark construction)
in small scale testing we found high effort on gemini 3.5 flash caused it to over think, generating large amounts of tokens without a substantive improve in performance.
Absolutely! We need new and better benchmarks like this.
I have a question: why not use the maximum available reasoning on each LLM? For example, I see that Opus 4.7 at `max` reasoning but Sonnet 4.6 at `high`. Wouldn't it be a fairer comparison if all were at max?
While this benchmark has interesting results, the "Contamination free" label only works for the initial release of the benchmark. It still has the same fundamental design issues of any other benchmark-- there's a single correct answer for tasks. It looks to be largely saturated upon release.
What they did well: normalizing the harness to mini-swe-agent -- models should be able to generalize to different tools at this point. When they struggle to do that (like most Google models), they're unlikely to be useful in practice. And that kind of generalization is an inherent part of intelligence.
For a benchmark that scales, you need to remove the ceiling and provide environments with measurable goals that are NOT a single correct answer, and sufficiently complex evaluation criteria to scale well beyond the current frontier.
We do this by running multi-agent simulations with large action spaces at https://gertlabs.com/rankings.
We're still relatively unknown in the benchmarking space, but by rotating the pool of environments and ensuring the optimal strategies in the environments themselves are affected by other agents participating in the space, we expect we'll be able to resist contamination as major labs start investing more effort to climb the leaderboard. We've already seen Chinese labs taking an interest.
1. your 'agentic coding' benchmarks are already saturated, with mimo #2? Cmon
2. game rl is fundamentally less useful than coding or work rl
The fact that claude and gpt 5.5 have nearly the same scores tells me your benchmark is not capturing a significant gap in capability between these two. What the linked page says about Claude is true in my experience: It frequently forgets important instructions and likes to take lazy shortcuts. Gpt by contrast is much more attentive and takes its time when needed to deliver a complete and robust solution. I have tested both models on two private repos (c#, go) on two long-horizon tasks with well-defined stop conditions and observed the same pattern in both cases. Both models still require a large harness to reduce shortcuts and architecturally unclean code, but gpt performs much better, to the point where I find claude unusable for any significant work.
GPT 5.5 does significantly outperform Opus 4.7 in the coding parts of our evals.
We also incorporate live decision making on social games (where GPT 5.5 has actually regressed from earlier models, which shouldn't be a huge surprise if you ever tried talking it out of some of its nits).
We are still looking for a way to integrate "logical" intelligence with social intelligence in a less arbitrary way, so I'd take a look at the use case that applies to you (probably coding): https://gertlabs.com/rankings?mode=agentic_coding
This benchmark matches my experience with GPT (I occasionally go back to Claude when I run into limits and frequently run into forgotten requirements and reward hacking)
I do have two questions / critiques:
- The verifier doesn't seem to check for code quality / maintainability, which I would posit is one of the major qualms with SOTA coding models i.e. they lack code 'taste'. Ofc this is a difficult problem to solve at scale, but wanted to point that out nonetheless
- This almost feels written like a critique on SWE Bench Pro. Hopefully they fix the issues with that benchmark!
Out of curiosity, I examined the worst task:
https://deepswe.datacurve.ai/data/trials/quill-shared-toolba...
It seems like GPT here is failing due to an environment issue of connecting to chromium, even though its local unit tests passed. All the models failed 4/4 and checking Opus it ran into the same problem
I checked some other tasks and they seemed legit, although in general the prompts seem somewhat contrived vs. what a typical user would ask their coding agent (such is the difficulty of benchmark construction)
70% at launch seems pretty saturated, why ship a benchmark frontier models are about to top out on?
sell data for them to hillclimb :)
I wonder why they didn't test Gemini 3.5 Flash (High).
in small scale testing we found high effort on gemini 3.5 flash caused it to over think, generating large amounts of tokens without a substantive improve in performance.
tysm for posting this! i'm charley, cofounder of datacurve, we created this benchmark and my team and i are here to answer any q's.
Did you try Opus-4.7 on a lower reasoning level? Looks like on `max` it's using far more tokens than the other frontier models.
Absolutely! We need new and better benchmarks like this.
I have a question: why not use the maximum available reasoning on each LLM? For example, I see that Opus 4.7 at `max` reasoning but Sonnet 4.6 at `high`. Wouldn't it be a fairer comparison if all were at max?
https://x.com/serenaa_ge/status/2059308400866111692
What happened that placed Opus 4.6 on max reasoning below Sonnet 4.6 on a lowered reasoning level?