Claw-SWE-Bench
General-purpose agent harnesses such as OpenClaw do multilingual SWE-bench-style coding tasks through an adapter that turns their output into scorable patches.
40 upvotes on Hugging Face papers and 112 GitHub stars.
- Made by
- TokenRhythm
- Released
- June 2026
- Kind
- Fixing real repositories
- Level
- Expert
- Scored
- Checked automatically
- Answers
- Agent task
- Tasks
- All public
- Strongest model tested
- GPT-5.5 #20 on the capability index
- Last model added
- September 2026
- Scores refreshed
- November 12th, 2026
In the authors’ words
The software engineering capabilities of general-purpose agent harnesses remain underexplored, and existing benchmarks offer limited support for comparing these harnesses under consistent conditions. To address this gap, we introduce Claw-SWE-Bench, a unified benchmark that enables researchers to systematically assess the capabilities and efficiency of general-purpose harnesses on software engineering tasks. The benchmark contains 350 real-world GitHub issue-resolution instances across eight programming languages and 43 repositories and provides a shared adapter protocol to align task inputs, outputs, and execution environments across harnesses. Experiments show that general-purpose harnesses can effectively resolve real-world software issues and that their success rates and resource consumption vary substantially even when the underlying model is held fixed. To lower evaluation costs and support faster debugging and iteration, we also provide Claw-SWE-Bench Lite, an 80-instance subset designed to preserve the key evaluation properties of the full benchmark. We hope this benchmark will help researchers better evaluate and understand the performance of general-purpose harnesses on software engineering tasks and guide the development of more capable and efficient harnesses. The data is available at https://github.com/opensquilla/claw-swe-bench and https://huggingface.co/datasets/TokenRhythm/Claw-SWE-Bench.
DeepSeek V4.1 Flash leads with 81.7% among models released by September 2026
Best score per entry from the benchmark's own leaderboard, read on November 12th, 2026. Where a model ran with several settings or harnesses, we keep its best. Every run is in the next chart. Names in gray aren't in our model list: fine-tunes, agent systems, products and older models. They don't count toward leaders.
The newest model with a score here came out in September 2026, and nobody has tested a newer one. Among the 20 highest-ranked models on the capability index, these have no score yet:
- Claude Fable 5.2
-
Gemini 4 Carbon
- Claude Opus 5.5
- GPT-6.1 Sol
- and 15 more
- 81.7
DeepSeek V4.1 Flash
- 78.0 GPT-5.5
- 77.1 Claude Opus 4.7
- 74.6 MiMo-V2.6 Flash
- 74.3 GLM-5.2
- 73.1 GLM-5.1
- 73.1
Qwen3.7-Max
- 71.7
DeepSeek-V4-Pro
- 70.3
DeepSeek-V4-Flash
- 69.7
MiniMax-M3
- 69.4 Hunyuan Hy3
- 68.3 Kimi K2.7 Code
- 67.1
LongCat-2.0
- 66.9 Kimi K2.6
- 62.8
Qwen 3.6 Flash
- 61.4
MiniMax-M2.7
- 48.6
Seed2.0 Mini
- 1
DeepSeek V4.1 Flash Full-350; mean of 3 runs 81.7%
- 2 GPT-5.5 Full-350; single run 78%
- 3 Claude Opus 4.7 Full-350; single run 77.1%
- 4 MiMo-V2.6 Flash Full-350; single run 74.6%
- 5 GLM-5.2 Full-350; single run 74.3%
- 6 GLM-5.1 Full-350; mean of 3 runs 73.1%
- 7
Qwen3.7-Max Full-350; single run 73.1%
- 8
DeepSeek-V4-Pro Full-350; single run 71.7%
- 9
DeepSeek-V4-Flash Full-350; single run 70.3%
- 10
MiniMax-M3 Full-350; single run 69.7%
- 11 Hunyuan Hy3 Full-350; single run 69.4%
- 12 Kimi K2.7 Code Full-350; single run 68.3%
- 13
LongCat-2.0 Full-350; single run 67.1%
- 14 Kimi K2.6 Full-350; single run 66.9%
- 15
Qwen 3.6 Flash Full-350; mean of 3 runs 62.8%
- 16
MiniMax-M2.7 Full-350; single run 61.4%
- 17
Seed2.0 Mini Full-350; single run 48.6%
Source: Official Claw-SWE-Bench leaderboard
| # | Entry | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | | 81.7% | Full-350; mean of 3 runs, Hermes Agent | Sep 2026 | Official leaderboard |
| 2 | GPT-5.5 | 78% | Full-350; single run, OpenClaw | Apr 2026 | Official leaderboard |
| 3 | Claude Opus 4.7 | 77.1% | Full-350; single run, OpenClaw | Apr 2026 | Official leaderboard |
| 4 | MiMo-V2.6 Flash | 74.6% | Full-350; single run, OpenClaw | Sep 2026 | Official leaderboard |
| 5 | GLM-5.2 | 74.3% | Full-350; single run, OpenClaw | Jun 2026 | Official leaderboard |
| 6 | GLM-5.1 | 73.1% | Full-350; mean of 3 runs, Hermes Agent | Apr 2026 | Official leaderboard |
| 7 | | 73.1% | Full-350; single run, OpenClaw | May 2026 | Official leaderboard |
| 8 | | 71.7% | Full-350; single run, OpenClaw | Apr 2026 | Official leaderboard |
| 9 | | 70.3% | Full-350; single run, OpenClaw | Apr 2026 | Official leaderboard |
| 10 | | 69.7% | Full-350; single run, OpenClaw | Jun 2026 | Official leaderboard |
| 11 | Hunyuan Hy3 | 69.4% | Full-350; single run, OpenClaw | Official leaderboard | |
| 12 | Kimi K2.7 Code | 68.3% | Full-350; single run, OpenClaw | Jun 2026 | Official leaderboard |
| 13 | | 67.1% | Full-350; single run, OpenClaw | Jul 2026 | Official leaderboard |
| 14 | Kimi K2.6 | 66.9% | Full-350; single run, OpenClaw | Apr 2026 | Official leaderboard |
| 15 | | 62.8% | Full-350; mean of 3 runs, Hermes Agent | Apr 2026 | Official leaderboard |
| 16 | | 61.4% | Full-350; single run, OpenClaw | Mar 2026 | Official leaderboard |
| 17 | | 48.6% | Full-350; single run, OpenClaw | Feb 2026 | Official leaderboard |
Cost–Resolve Pareto
The line joins the runs that no other run beats on both measures. We redrew this chart from the numbers on the official leaderboard.
| Model | Setting | Total Pass@1 | Total API cost for Full-350 |
|---|---|---|---|
| | Full-350; mean of 3 runs, Hermes Agent | 81.7% | $36 |
| | Full-350; mean of 3 runs, OpenClaw | 79.8% | $50 |
| | Full-350; mean of 3 runs, nanobot | 79.6% | $78 |
| GPT-5.5 | Full-350; single run, OpenClaw | 78% | $1,399 |
| Claude Opus 4.7 | Full-350; single run, OpenClaw | 77.1% | $1,082 |
| | Full-350; mean of 3 runs, DeepSeek Harness | 76.5% | $41 |
| | Full-350; mean of 3 runs, ZeroClaw | 76.1% | $99 |
| | Full-350; mean of 3 runs, Meta-Harness | 74.9% | $13 |
| MiMo-V2.6 Flash | Full-350; single run, OpenClaw | 74.6% | $22.3 |
| GLM-5.2 | Full-350; single run, OpenClaw | 74.3% | $304.13 |
| GLM-5.1 | Full-350; mean of 3 runs, Hermes Agent | 73.1% | $389 |
| | Full-350; single run, OpenClaw | 73.1% | $436.4 |
| GLM-5.1 | Full-350; single run, OpenClaw | 72% | $290.6 |
| | Full-350; single run, OpenClaw | 71.7% | $81.3 |
| | Full-350; mean of 3 runs, GenericAgent | 71.5% | $12 |
| GLM-5.1 | Full-350; mean of 3 runs, OpenClaw | 71.2% | $338 |
| | Full-350; single run, OpenClaw | 70.3% | $8.2 |
| | Full-350; single run, OpenClaw | 69.7% | $271.8 |
| GLM-5.1 | Full-350; mean of 3 runs, ZeroClaw | 69.7% | $391 |
| Hunyuan Hy3 | Full-350; single run, OpenClaw | 69.4% | $141.1 |
| GLM-5.1 | Full-350; mean of 3 runs, DeepSeek Harness | 69.1% | $513 |
| GLM-5.1 | Full-350; mean of 3 runs, nanobot | 68.7% | $525 |
| Kimi K2.7 Code | Full-350; single run, OpenClaw | 68.3% | $270.9 |
| | Full-350; single run, OpenClaw | 67.1% | $478 |
| Kimi K2.6 | Full-350; single run, OpenClaw | 66.9% | $633.7 |
| GLM-5.1 | Full-350; mean of 3 runs, Meta-Harness | 65.7% | $190 |
| GLM-5.1 | Full-350; mean of 3 runs, GenericAgent | 62.9% | $86 |
| | Full-350; mean of 3 runs, Hermes Agent | 62.8% | $104 |
| | Full-350; single run, OpenClaw | 61.4% | $196.7 |
| | Full-350; single run, OpenClaw | 60.9% | $77.5 |
| | Full-350; mean of 3 runs, OpenClaw | 60.9% | $87 |
| | Full-350; mean of 3 runs, ZeroClaw | 58.3% | $69 |
| | Full-350; mean of 3 runs, DeepSeek Harness | 56.8% | $91 |
| | Full-350; mean of 3 runs, nanobot | 52.5% | $175 |
| | Full-350; single run, OpenClaw | 48.6% | $19.4 |
| | Full-350; mean of 3 runs, Meta-Harness | 45.1% | $49 |
| | Full-350; mean of 3 runs, GenericAgent | 38.5% | $39 |
The best score, 81.7%, costs $0.10 a task
Runs on the cost frontier score higher than any run that's cheaper. The costs are the leaderboard's own estimates, so compare them only within this benchmark.
Source: Official Claw-SWE-Bench leaderboard
| Model | Effort | Harness | Score | Cost per task | Output tokens |
|---|---|---|---|---|---|
| | Full-350; mean of 3 runs | Hermes Agent | 81.7% | $0.10 | 38,000 |
| | Full-350; mean of 3 runs | OpenClaw | 79.8% | $0.14 | 72,571 |
| | Full-350; mean of 3 runs | nanobot | 79.6% | $0.22 | 38,857 |
| GPT-5.5 | Full-350; single run | OpenClaw | 78% | $4.00 | 44,857 |
| Claude Opus 4.7 | Full-350; single run | OpenClaw | 77.1% | $3.09 | 17,714 |
| | Full-350; mean of 3 runs | DeepSeek Harness | 76.5% | $0.12 | 36,857 |
| | Full-350; mean of 3 runs | ZeroClaw | 76.1% | $0.28 | 177,714 |
| | Full-350; mean of 3 runs | Meta-Harness | 74.9% | $0.04 | 20,571 |
| MiMo-V2.6 Flash | Full-350; single run | OpenClaw | 74.6% | $0.06 | 104,000 |
| GLM-5.2 | Full-350; single run | OpenClaw | 74.3% | $0.87 | |
| GLM-5.1 | Full-350; mean of 3 runs | Hermes Agent | 73.1% | $1.11 | 19,714 |
| | Full-350; single run | OpenClaw | 73.1% | $1.25 | |
| GLM-5.1 | Full-350; single run | OpenClaw | 72% | $0.83 | 20,000 |
| | Full-350; single run | OpenClaw | 71.7% | $0.23 | 31,429 |
| | Full-350; mean of 3 runs | GenericAgent | 71.5% | $0.03 | 19,429 |
| GLM-5.1 | Full-350; mean of 3 runs | OpenClaw | 71.2% | $0.97 | 23,143 |
| | Full-350; single run | OpenClaw | 70.3% | $0.02 | 36,857 |
| GLM-5.1 | Full-350; mean of 3 runs | ZeroClaw | 69.7% | $1.12 | 12,286 |
| | Full-350; single run | OpenClaw | 69.7% | $0.78 | |
| GLM-5.1 | Full-350; mean of 3 runs | DeepSeek Harness | 69.1% | $1.47 | 15,143 |
| GLM-5.1 | Full-350; mean of 3 runs | nanobot | 68.7% | $1.50 | 22,286 |
| Kimi K2.7 Code | Full-350; single run | OpenClaw | 68.3% | $0.77 | |
| | Full-350; single run | OpenClaw | 67.1% | $1.37 | 62,000 |
| Kimi K2.6 | Full-350; single run | OpenClaw | 66.9% | $1.81 | 34,571 |
| GLM-5.1 | Full-350; mean of 3 runs | Meta-Harness | 65.7% | $0.54 | 16,286 |
| GLM-5.1 | Full-350; mean of 3 runs | GenericAgent | 62.9% | $0.25 | 14,571 |
| | Full-350; mean of 3 runs | Hermes Agent | 62.8% | $0.30 | 20,857 |
| | Full-350; single run | OpenClaw | 61.4% | $0.56 | 25,714 |
| | Full-350; single run | OpenClaw | 60.9% | $0.22 | 25,143 |
| | Full-350; mean of 3 runs | OpenClaw | 60.9% | $0.25 | 27,143 |
| | Full-350; mean of 3 runs | ZeroClaw | 58.3% | $0.20 | 17,429 |
| | Full-350; mean of 3 runs | DeepSeek Harness | 56.8% | $0.26 | 27,714 |
| | Full-350; mean of 3 runs | nanobot | 52.5% | $0.50 | 28,286 |
| | Full-350; single run | OpenClaw | 48.6% | $0.06 | 61,714 |
| | Full-350; mean of 3 runs | Meta-Harness | 45.1% | $0.14 | 21,714 |
| | Full-350; mean of 3 runs | GenericAgent | 38.5% | $0.11 | 21,429 |
2 models have held number one on the official leaderboard since its release in June 2026
Lab claims count from the day the card came out, independent runs from the model’s release.
Source: Official Claw-SWE-Bench leaderboard
Scores over time
All the scores we have, one dot each, so a model tested at several settings appears more than once.
Source: Official Claw-SWE-Bench leaderboard
No lab model card reports it yet
We read the headline results of 522 launch posts from 52 labs, and none of them list this benchmark.
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