Benchmark Almanac

SWE-Bench Pro V2

Claude Opus 5 99.4% leads since Sep 2026 Not in model cards

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.

Abstract of SWE-Bench Pro V2: A Cleaner, Harder-to-Game Coding Benchmark (arXiv:2610.24206, CC0)
Made by Scale AI (co-developed with Reflection) Released Sep 2026 Fixing real repositories All tasks public Last model added Oct 2026 Scores refreshed 2 Nov 2026
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.
16
Models scored on its own leaderboard

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. 1 Claude Opus 5 xhigh 99.4%
  2. 2 Gemini 4 Argon high 99.2%
  3. 3 Claude Fable 5.1 high 99.1%
  4. 4 Kimi K3 max 97.7%
  5. 5 GPT-6 Astra high 96.9%
  6. 6 Muse Wonder xhigh 96.7%
  7. 7 GPT-6.1 Sol xhigh 96.1%
  8. 8 Claude Sonnet 5.5 xhigh 95.8%
  9. 9 GLM-5.3 max 95.6%
  10. 10 GPT-5.6 Sol xhigh 95.5%
  11. 11 Muse Spark Open xhigh 95%
  12. 12 Gemini 3.8 Flash high 94.9%
  13. 13 Claude Sonnet 5 xhigh 93.2%
  14. 14 GPT-5.6 Terra xhigh 92.4%
  15. 15 Inkling xhigh 89.9%
  16. 16 Claude Haiku 5.5 high 87.2%
#ModelScoreSettingDateSource
1 Claude Opus 5 99.4% xhigh, Claude Code Jul 2026 Official leaderboard
2 Gemini 4 Argon 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 Muse Wonder 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 Muse Spark Open 95% xhigh, mini-SWE-agent Oct 2026 Official leaderboard
12 Gemini 3.8 Flash 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 Inkling 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.

Anthropic
Own leaderboard Passed 85% in Sep 2026, by Claude Opus 5

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.

On the benchmark’s own leaderboard Best so far
0% 20% 40% 60% 80% 100% 2026 Near the ceiling (85%) Claude Opus 5 Claude Sonnet 5 0% 20% 40% 60% 80% 100% 2026 Near the ceiling (85%) Claude Opus 5 Claude Sonnet 5

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.

Anthropic OpenAI
SWE-Bench Pro V2 Passed 85% in Sep 2026, by Claude Opus 5
SWE-Bench Pro Passed 85% in Sep 2026, by Claude Opus 5.5
SWE-Bench Pro V2 Passed 85% in Sep 2026, by Claude Opus 5

More in Coding

All 243 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