SWE-Bench Pro V2 Hard
Agents resolve the 51 SWE-Bench Pro V2 tasks that at least two of five model families failed. Graded with the V2 locked protocol.
The paper reports Hard scores from 52.9 to 98.0 while the full V2 set reaches 99.4. It is the hard config of the V2 dataset, and three of 54 candidates were removed after expert review.
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
- Research frontier Researchers, on open problems or tasks that take days
- 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 98%
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.
- 98.0 Claude Opus 5
- 92.2 Claude Fable 5.1
- 92.2
Gemini 4 Argon
- 90.2 GPT-6 Astra
- 90.2
Muse Wonder
- 88.2 Claude Sonnet 5
- 88.2 Kimi K3
- 86.3 GPT-5.6 Terra
- 84.3 GLM-5.3
- 82.4 GPT-5.6 Sol
- 82.3
Muse Spark Open
- 70.6 Claude Sonnet 5.5
- 58.8
Gemini 3.8 Flash
- 56.9
Inkling
- 52.9 Claude Haiku 5.5
- 25.5 Claude Haiku 4.5
- 1 Claude Opus 5 xhigh 98%
- 2 Claude Fable 5.1 high 92.2%
- 3
Gemini 4 Argon high 92.2%
- 4 GPT-6 Astra high 90.2%
- 5
Muse Wonder xhigh 90.2%
- 6 Claude Sonnet 5 xhigh 88.2%
- 7 Kimi K3 max 88.2%
- 8 GPT-5.6 Terra xhigh 86.3%
- 9 GLM-5.3 max 84.3%
- 10 GPT-5.6 Sol xhigh 82.4%
- 11
Muse Spark Open xhigh 82.3%
- 12 Claude Sonnet 5.5 xhigh 70.6%
- 13
Gemini 3.8 Flash high 58.8%
- 14
Inkling xhigh 56.9%
- 15 Claude Haiku 5.5 high 52.9%
- 16 Claude Haiku 4.5 xhigh 25.5%
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | Claude Opus 5 | 98% | xhigh, Claude Code | Jul 2026 | Official leaderboard |
| 2 | Claude Fable 5.1 | 92.2% | high, Claude Code | Sep 2026 | Official leaderboard |
| 3 | | 92.2% | high, mini-SWE-agent | Sep 2026 | Official leaderboard |
| 4 | GPT-6 Astra | 90.2% | high, Codex | Sep 2026 | Official leaderboard |
| 5 | | 90.2% | xhigh, mini-SWE-agent | Oct 2026 | Official leaderboard |
| 6 | Claude Sonnet 5 | 88.2% | xhigh, Claude Code | Jun 2026 | Official leaderboard |
| 7 | Kimi K3 | 88.2% | max, mini-SWE-agent | Jul 2026 | Official leaderboard |
| 8 | GPT-5.6 Terra | 86.3% | xhigh, Codex | Jul 2026 | Official leaderboard |
| 9 | GLM-5.3 | 84.3% | max, mini-SWE-agent | Aug 2026 | Official leaderboard |
| 10 | GPT-5.6 Sol | 82.4% | xhigh, Codex | Jul 2026 | Official leaderboard |
| 11 | | 82.3% | xhigh, mini-SWE-agent | Oct 2026 | Official leaderboard |
| 12 | Claude Sonnet 5.5 | 70.6% | xhigh, mini-SWE-agent | Sep 2026 | Official leaderboard |
| 13 | | 58.8% | high, mini-SWE-agent | Sep 2026 | Official leaderboard |
| 14 | | 56.9% | xhigh, mini-SWE-agent | Jul 2026 | Official leaderboard |
| 15 | Claude Haiku 5.5 | 52.9% | high, mini-SWE-agent | Oct 2026 | Official leaderboard |
| 16 | Claude Haiku 4.5 | 25.5% | xhigh, Claude Code | Oct 2025 | Official leaderboard |
Labs report scores for 1 model in their cards. We plot every source in the charts below.
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.
One lab put it in a launch post in the last six months
Launch posts from every lab that has listed it, one tick per post. Tall colored ticks include it, and short gray ones leave it out.
| Date | Launch post | Score | As the post writes it |
|---|---|---|---|
| 5 Oct 2026 | Beam 1.0 | 77.2 | SWE Bench Pro v2-Hard |
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