SWE-Bench Pro V2
Agents resolve 642 repaired issue tasks from the SWE-Bench Pro public split in Go, Python, JavaScript and TypeScript. A locked protocol re-grades each patch in a clean container.
Audits and expert review repaired or retired 480 of the 731 v1 public tasks and dropped 89. The agent can reach only the model endpoint. V2 scores are not comparable with v1. The dataset default config is V2 since 2026-09-22.
In the authors’ words
SWE-Bench Pro is a widely used benchmark of long-horizon software engineering tasks for coding agents. As agents improve, they reach task issues earlier agents never touched and interact with the evaluation environment in ways the original harness did not anticipate. We present SWE-Bench Pro V2, a revised public split built to keep the benchmark accurate as capabilities advance. Agentic audits, expert adjudication and blind re-implementation repaired or retired 480 of the 731 tasks: 23 contracted engineers spent 404 hours, each working in a language they write professionally, and every revised instruction was verified by a second engineer solving the task from the text alone. A hardened protocol restricts network access to the model endpoint and re-grades every patch in a clean container, so agents cannot retrieve upstream fixes or get credit for changes outside their patch. On the 642 V2 tasks frontier agents resolve up to 99.4% while other systems stay clearly separated, and a 51-task Hard split spreads systems from 52.9% to 98.0%. We release the benchmark and distill lessons for maintaining agent benchmarks over time.
- Level
- Expert Specialists with years of training, often over hours of work
- Scored
- Checked automatically A program checks the result against an answer key, unit tests or the expected final state.
- Answers
- Code A program or patch run against tests.
Claude Opus 5 leads with 99.4%
Best score per model from the benchmark's own leaderboard, read on 2 Nov 2026. Where a model ran with several settings or harnesses, we keep its best.
- 1 Claude Opus 5 xhigh 99.4%
- 2
Gemini 4 Argon high 99.2%
- 3 Claude Fable 5.1 high 99.1%
- 4 Kimi K3 max 97.7%
- 5 GPT-6 Astra high 96.9%
- 6
Muse Wonder xhigh 96.7%
- 7 GPT-6.1 Sol xhigh 96.1%
- 8 Claude Sonnet 5.5 xhigh 95.8%
- 9 GLM-5.3 max 95.6%
- 10 GPT-5.6 Sol xhigh 95.5%
- 11
Muse Spark Open xhigh 95%
- 12
Gemini 3.8 Flash high 94.9%
- 13 Claude Sonnet 5 xhigh 93.2%
- 14 GPT-5.6 Terra xhigh 92.4%
- 15
Inkling xhigh 89.9%
- 16 Claude Haiku 5.5 high 87.2%
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | Claude Opus 5 | 99.4% | xhigh, Claude Code | Jul 2026 | Official leaderboard |
| 2 | | 99.2% | high, mini-SWE-agent | Sep 2026 | Official leaderboard |
| 3 | Claude Fable 5.1 | 99.1% | high, Claude Code | Sep 2026 | Official leaderboard |
| 4 | Kimi K3 | 97.7% | max, mini-SWE-agent | Jul 2026 | Official leaderboard |
| 5 | GPT-6 Astra | 96.9% | high, Codex | Sep 2026 | Official leaderboard |
| 6 | | 96.7% | xhigh, mini-SWE-agent | Oct 2026 | Official leaderboard |
| 7 | GPT-6.1 Sol | 96.1% | xhigh, mini-SWE-agent | Sep 2026 | Official leaderboard |
| 8 | Claude Sonnet 5.5 | 95.8% | xhigh, mini-SWE-agent | Sep 2026 | Official leaderboard |
| 9 | GLM-5.3 | 95.6% | max, mini-SWE-agent | Aug 2026 | Official leaderboard |
| 10 | GPT-5.6 Sol | 95.5% | xhigh, Codex | Jul 2026 | Official leaderboard |
| 11 | | 95% | xhigh, mini-SWE-agent | Oct 2026 | Official leaderboard |
| 12 | | 94.9% | high, mini-SWE-agent | Sep 2026 | Official leaderboard |
| 13 | Claude Sonnet 5 | 93.2% | xhigh, Claude Code | Jun 2026 | Official leaderboard |
| 14 | GPT-5.6 Terra | 92.4% | xhigh, Codex | Jul 2026 | Official leaderboard |
| 15 | | 89.9% | xhigh, mini-SWE-agent | Jul 2026 | Official leaderboard |
| 16 | Claude Haiku 5.5 | 87.2% | high, mini-SWE-agent | Oct 2026 | Official leaderboard |
Number one over time
Lab claims count from the day the card came out, independent runs from the model’s release.
The best score passed 85% in Jun 2026
All the scores we have, one dot each, so a model tested at several settings appears more than once.
No lab model card reports it yet
We read the headline results of 479 launch posts from 51 labs, and none of them list this benchmark.
Versions
We switch the top row to each new version when it comes out. Open it for one row per version. An old version stays in play while people still test it, so whoever first takes it past 85% gets credit for closing it out.
More in Coding
- SWE-bench Verified In use 500 human-validated GitHub issues from 12 Python repos. Agent writes a patch that must pass hidden tests. Claude Opus 4.7 83.5%
- SWE-bench Multilingual In use 300 GitHub-issue tasks across nine programming languages in SWE-bench format. Patch graded by repository tests. Claude Opus 5.5 93.9%
- LiveCodeBench v6 In use Competitive programming problems collected after model cutoffs from LeetCode, AtCoder, Codeforces. Scored pass@1 on hidden tests.
DeepSeek-V4-Pro 93.5%
- DeepSWE In use 113 original long-horizon engineering tasks across 91 repos and five languages, written from scratch. Hand-written verifiers accept alternative solutions. GPT-6 Astra 74.1%
- SciCode In use Scientist-curated research coding problems split into subproblems across natural sciences. Code checked against test cases.
Seed2.1 Pro 59.8%
- Codeforces rating In use Model solves recent Codeforces contest problems. An Elo-style rating is computed from simulated contest performance.
DeepSeek V4.1 Flash 3,471