DeepSWE: A contamination-free benchmark for long-horizon coding agents
deepswe.datacurve.aiWhile 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.
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).
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.
What happened that placed Opus 4.6 on max reasoning below Sonnet 4.6 on a lowered reasoning level?