SWE-bench Verified
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. High benchmark score alone doesn't make a model the right pick — weigh it against pricing, API availability, and release date.
Models ranked
81
tracked on this benchmark
Score band
96.0 – 23.6
best → lowest tracked
Snapshot trend
+10.80
Jun 30 → Jul 24 · 1 models
Leaderboard
Tracked models ranked by % Resolved (higher is better).
Notes: DAT-5842: Independent Vals.ai SWE-bench Verified result; keep separate from Anthropic's SWE-bench Pro launch row.
Configuration: Adaptive thinking at maximum effort
Notes: Vendor-reported. Source: p.149, Table 8.1.A. Preserve the five-trial setting; do not treat single-trial rows as equivalent.
Notes: Official Anthropic launch benchmark.
Notes: DAT-5460 source reconciliation: vals.ai standardized harness reports GPT-5.5 at 82.6; do not use the non-comparable 88.7 self-reported row for /best/coding.
Notes: Medium confidence: GPT-5.5 Pro uses the same underlying weights as standard GPT-5.5; no Pro-specific SWE-bench Verified score was found.
Notes: Confidence: medium. DAT-4172 May 12 /best/ refresh; benchlm.ai May 11 snapshot showed Claude Opus 4.5 at 80.9%.
Notes: Verified via third-party source; official OpenAI benchmark may differ. Use directionally.
Notes: Confidence: high. DAT-4934 confirmed Anthropic self-reported this score and identified Composer 2.5's prior 80.8 SWE-bench Verified row as a Claude Opus 4.6 misattribution.
Notes: Confidence: high. DAT-4172 May 12 /best/ refresh; benchlm.ai and llm-stats both reported Qwen3.6 Plus at 78.8%.
How to read this benchmark
This benchmark scores models where higher is better. Scores are useful for directional filtering and shortlisting — not for universal quality ranking. Prefer benchmarks closest to your workload, then validate the linked model pages for pricing, context window, and provider availability.
Trust this score when
- There is a fresh timestamped snapshot (or multiple snapshots) for this benchmark.
- The model list covers the same version family you can actually deploy today.
- Top candidates overlap with your required routing and feature requirements.
Be cautious when
- There is only one benchmark snapshot or the dataset appears stale.
- The benchmark metric direction is opposite of your decision objective.
- The score difference between options is narrow and likely within implementation variance.
FAQ
What does the benchmark measure?
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. On this page it lists 81 tracked model variants where higher is better.
Is a higher SWE-bench Verified score always better?
For this benchmark, higher is better. A high score helps you shortlist, but confirm pricing, context window, and provider availability on each model page before committing — the top scorer is not always the right pick for your workload or budget.
How current is this SWE-bench Verified data?
This benchmark was last reviewed on Apr 15, 2026. The tracked score average moved +10.80 points across the last 3 snapshots.
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Last reviewed: Apr 15, 2026