MaxxBench exposes DeepSWE overfitting in AI coding models
Original title: MaxxBench – "Nice DeepSWE score. But is it true?"
Mutations to DeepSWE tasks cause large performance drops in models due to benchmark overfitting.
MaxxBench mutates 89 DeepSWE tasks by changing requirements or starting code. Models that guessed test expectations fail more severely on revised tasks. Top models like Claude Opus 5.5 improve slightly while others drop over 20 points.
- DeepSWE scores cluster near 74% but spread widens to 48.3 points on MaxxBench
- Claude Opus 5.5 scores 76.4% on MaxxBench
- DeepSeek V4.1 Flash drops from 74.2% to 55.6%
From the original article
14 min read at maxxbench.unlap.ai
We changed requirements in a coding benchmark and found models talking themselves out of following them. Sometimes the agent understood the new requirement perfectly. It described what needed to change, considered an implementation, and then decided the hidden tests probably wanted something else.
By antiuniverse · maxxbench.unlap.ai
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