SlopCodeBench

SlopCodeBench, a community benchmark measuring code erosion as agents iteratively extend their own solutions across checkpoints, has released its top 10 models. The leading model is 01GPT 5.5/Codex with a score of 28.1% and a code erosion metric of 0.494, followed by GPT 5.3-Codex/Codex at 26.0% and GPT 5.4/Codex at 25.5%. The benchmark evaluates coding agents through repeated requirement changes and extensions, tracking both correctness and code quality.

SlopCodeBench A community benchmark measuring code erosion as agents iteratively extend their own solutions across checkpoints. top 10 models full leaderboard → /leaderboard - 01GPT 5.5/Codex28.1%0.4940.269 - 02GPT 5.3-Codex/Codex26.0%0.6440.336 - 03GPT 5.4/Codex25.5%0.2780.193 - 04GPT 5.2-Codex/Codex21.9%0.7280.398 - 05Opus 4.6/Claude Code20.9%0.7370.318 - 06Opus 4.7/Claude Code20.9%0.7590.357 - 07KIKimi K2.6/Kimi CLI18.9%0.7640.399 - 08Opus 4.5/Claude Code17.3%0.6910.297 - 09Sonnet 4.6/Claude Code16.8%0.7410.316 - 10Composer 2/Cursor CLI16.3%0.7160.353 Featured Problems browse all → /problems Overview SlopCodeBench evaluates coding agents the way real software actually gets built: through repeated requirement changes and extensions. Each problem is a sequence of checkpoints — the agent implements an initial version, then extends its own solution as new requirements arrive. Evaluation is black-box: only a CLI or API contract is given, with no prescribed architecture, function signatures, or module boundaries, so early design decisions compound across the run. Beyond correctness, we measure code erosion — verbosity, dead branches, and redundant structure — to surface the agents that stay clean under sustained change instead of patching their way into slop. Supported by Special thanks to Snorkel AI https://snorkel.ai for supporting this work through the Open Benchmarks Grant.