Benchmark Almanac

SWE-bench Verified (hard subset)

Claude Sonnet 4.6 48.2% leads since February 2026
Only in system cards

Agents solve a subset of SWE-bench Verified tasks estimated to take a human engineer more than one hour.

Anthropic RSP autonomy evaluation built on public SWE-bench Verified tasks.

Made by
Anthropic
Released
February 2025
Kind
Machine learning engineering
Level
Expert
Scored
Checked automatically
Answers
Code
Tasks
Kept private
Strongest model tested
Claude Opus 4.6 #37 on the capability index
Last in a system card
February 2026
Last model added
February 2026
9
Models scored in lab cards
9
System cards report it, from 1 lab

Claude Sonnet 4.6 leads with 48.2% among models released by February 2026

Best score per model as labs report them in their own cards. Labs pick their own settings, so these compare less cleanly than independent runs.

The newest model with a score here came out in February 2026, and nobody has reported a newer one. Among the 20 highest-ranked models on the capability index, these have no score yet:

  1. 48.2 Claude Sonnet 4.6
  2. 47.2 Claude Opus 4.6
  3. 46.7 Claude Opus 4.5
  4. 45.3 Claude Sonnet 4.5
  5. 43.8 Claude Opus 4.1
  6. 39.5 Claude Opus 4
  7. 36.7 Claude Sonnet 4
  8. 36.6 Claude Haiku 4.5
  9. 23.0 Claude 3.7 Sonnet
  1. 1 Claude Sonnet 4.6 48.2%
  2. 2 Claude Opus 4.6 47.2%
  3. 3 Claude Opus 4.5 46.7%
  4. 4 Claude Sonnet 4.5 45.3%
  5. 5 Claude Opus 4.1 43.8%
  6. 6 Claude Opus 4 39.5%
  7. 7 Claude Sonnet 4 36.7%
  8. 8 Claude Haiku 4.5 36.6%
  9. 9 Claude 3.7 Sonnet 23%

Source: Labs’ model cards

#ModelScoreSettingDateSource
1 Claude Sonnet 4.6 48.2% Feb 2026 Lab model card
2 Claude Opus 4.6 47.2% Feb 2026 Lab model card
3 Claude Opus 4.5 46.7% Nov 2025 Lab model card
4 Claude Sonnet 4.5 45.3% Sep 2025 Lab model card
5 Claude Opus 4.1 43.8% Aug 2025 Lab model card
6 Claude Opus 4 39.5% May 2025 Lab model card
7 Claude Sonnet 4 36.7% May 2025 Lab model card
8 Claude Haiku 4.5 36.6% Oct 2025 Lab model card
9 Claude 3.7 Sonnet 23% Feb 2025 Lab model card

7 models have held number one in labs’ own numbers since its release in February 2025

Lab claims count from the day the card came out, independent runs from the model’s release.

Anthropic
Lab claims Not tested since Feb 2026
Claude Sonnet 4.6 48.2%

Source: Labs’ model cards

Scores over time

All the scores we have, one dot each, so a model tested at several settings appears more than once.

Reported by the lab in its model card Best so far
0% 20% 40% 60% 80% 100% 2025 2026 Near the ceiling (85%) Claude Sonnet 4.6 Claude Opus 4.5 Claude Opus 4.1 Claude Opus 4 Claude 3.7 Sonnet 0% 20% 40% 60% 80% 100% 2025 2026 Near the ceiling (85%) Claude Sonnet 4.6 Claude Opus 4

Source: Labs’ model cards

Labs report it only in system cards

We read the headline results of 522 launch posts from 52 labs, and none of them list this benchmark. Some system cards do, below.

9 system cards report it, from 1 lab

Besides launch posts, we read the system cards, model cards and technical reports labs publish with a release. They report far more, including safety and frontier-risk tests.

DateDocumentScoreAs the document writes itSection
Feb 17, 2026 Claude Sonnet 4.6 system card 48.2 SWE-bench Verified (hard subset), 21.7/45 problems solved on average over 10 trials (6.3.2) AI R&D risk
Feb 5, 2026 Claude Opus 4.6 system card 47.2 SWE-bench Verified (hard subset), 21.24/45 problems solved on average over 10 trials (8.3.2) AI R&D risk
Nov 24, 2025 Claude Opus 4.5 system card 46.7 SWE-bench Verified (hard subset), 21/45 problems solved, averaged over 10 trials (7.3.1) AI R&D risk
Oct 15, 2025 Claude Haiku 4.5 system card 36.6 SWE-bench Verified hard subset, 16.45/45 problems solved on average, pass@1 (6.3) AI R&D risk
Sep 29, 2025 Claude Sonnet 4.5 system card 45.3 SWE-bench Verified (hard subset), 20.4/45 problems passed on average over 10 runs (9.3.2) AI R&D risk
Aug 5, 2025 Claude Opus 4.1 system card addendum 43.8 SWE-bench Verified (hard subset), 18.4/42 problems solved on average, pass@1 (6.4.1) AI R&D risk
May 22, 2025 Claude Sonnet 4 system card 36.7 SWE-bench Verified (hard subset), 15.4/42 problems passed on average over 10 runs (7.3.1) AI R&D risk
May 22, 2025 Claude Opus 4 system card 39.5 SWE-bench Verified (hard subset), 16.6/42 problems passed on average over 10 runs (7.3.1) AI R&D risk
Feb 24, 2025 Claude 3.7 Sonnet system card 23 SWE-bench Verified (hard subset), average of 9.65/42 problems passed, stated as a 23% success rate (7.2, 7.2.2) AI R&D risk

More in AI research

All 104 in AI research
  • PaperBench In use Agent replicates ICML 2024 papers from scratch. LLM judge grades against author-written hierarchical rubrics. Qwen 3.8 Max 93%
  • MLS-Bench In use Agent must invent one algorithmic change in an ML research scaffold whose gain transfers across settings. Scored relative to human baselines. Claude Fable 5.1 50.3%
  • PostTrainBench 1.2 In use CLI agent gets a base model, one H100 and a time budget to post-train it for a target benchmark. Scored on normalized gain. Claude Fable 5.1 44.6%
  • MLE-bench In use Kaggle-style ML engineering competitions. Agent trains models and is scored against competition leaderboards. GPT-6 Astra 93.8%
  • KernelBench In use Replace PyTorch operators with faster custom GPU kernels. Scored on correctness and speedup. MiniMax-M3 28.8%
  • PMPP-Hard In use Agents write GPU kernels for 69 tasks that must be correct, and on 48 of them also run within 1.25 times the reference time. The score is the area under the solved-tasks curve over a 10-hour budget, out of 690 task-hours. Inkling 2 Small 494.3