LLM Reference
activeCoding

SWE-bench Verified

Metric: % ResolvedIntroduced: 2024

About

500 human-validated GitHub issue resolution tasks from SWE-bench, created with OpenAI in August 2024. The standard evaluation for agentic coding systems. Top performers (2026) exceed 78% resolved.

How to read this benchmark

This benchmark uses a scoring system where higher is better. Scores are useful for directional filtering and model shortlisting, not for universal quality ranking.

Interpretation checklist: prefer benchmarks that are closest to your workload style, then validate against the linked model pages for pricing, context window, and provider availability.

Trust when:

  • There is a fresh timestamped snapshot (or multiple snapshots) for this benchmark.
  • Model list covers the same version family you can actually deploy today.
  • Top candidates overlap with your required routing and feature requirements.

Don't trust when:

  • There is only one benchmark snapshot or the dataset appears stale.
  • Benchmark metric direction is opposite of your decision objective.
  • The score difference between options is narrow and likely within implementation variance.

Current modeled score band for tracked entries is roughly 93.9 – 80.9.

Window

May 1 to May 21

last 3 snapshots

Mean delta

-13.10

score points

Coverage

1

models in latest snapshot

Leaderboard preview (top 5)

  1. 1. Claude Mythos Preview93.9
  2. 2. Claude Opus 4.787.6
  3. 3. GPT-5.3-Codex85.0
  4. 4. GPT-5.582.6
  5. 5. Claude Opus 4.580.9