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Coding Agents Have Converged: Why the SWE-bench Leaderboard Can No Longer Order Its Top Entries, and What to Measure Instead

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TL;DR - An audit of 254 SWE-bench submissions finds that small score gaps cannot reliably rank leading coding agents because their successes overlap heavily and results depend on model-scaffold pairings. The authors propose reporting statistical resolution, grouping sensitivity, and model-scaffold provenance instead of treating aggregate scores as definitive rankings.

  • The top two SWE-bench Verified entries both solve 396 of 500 tasks; none of the 29 adjacent top-30 pairs differ significantly under exact paired McNemar tests at α=0.05.
  • Top-ten systems share 285 successes and 51 failures, with a median solution-set nesting of 0.935 versus a score-implied baseline of 0.774.
  • Within-model scaffold performance ranges reach 29.8 percentage points, exceeding the top-30 score spread of 8.8 points, though the observational analysis does not establish causality.
  • The larger Test split distinguishes 14 of 23 adjacent pairs, indicating that evaluation-set size materially affects leaderboard resolution.

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Coding Agents Have Converged: Why the SWE-bench Leaderboard Can No Longer Order Its Top Entries, and What to Measure Instead

arXiv cs.SE Fengshuo Liu, Ying Liu, Ruize Sun, Lie Luo, Siyuan Guo 2026-09-15 arXiv:2609.17394
Public signals Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-09-25 14:19:25.869130 UTC

TL;DR - An audit of 254 SWE-bench submissions finds that small score gaps cannot reliably rank leading coding agents because their successes overlap heavily and results depend on model-scaffold pairings. The authors propose reporting statistical resolution, grouping sensitivity, and model-scaffold provenance instead of treating aggregate scores as definitive rankings.

  • The top two SWE-bench Verified entries both solve 396 of 500 tasks; none of the 29 adjacent top-30 pairs differ significantly under exact paired McNemar tests at α=0.05.
  • Top-ten systems share 285 successes and 51 failures, with a median solution-set nesting of 0.935 versus a score-implied baseline of 0.774.
  • Within-model scaffold performance ranges reach 29.8 percentage points, exceeding the top-30 score spread of 8.8 points, though the observational analysis does not establish causality.
  • The larger Test split distinguishes 14 of 23 adjacent pairs, indicating that evaluation-set size materially affects leaderboard resolution.
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