Coding benchmark # DeepSWE leaderboard > DeepSWE results for 29 AI models, led by GPT-6.1 Sol at 75.2%. What the benchmark measures, who runs it, and a source for every score. - Canonical page: https://noometry.com/benchmarks/deepswe - Last updated: 2026-10-10 - Title: DeepSWE Leaderboard (October 2026): Scores by Model As of October 2026, GPT-6.1 Sol has the highest published DeepSWE score on Noometry at 75.2%, out of 29 models with results. Last verified October 10, 2026 ## About DeepSWE A description with primary sources is being prepared for this benchmark. - **Category:** [Coding](https://noometry.com/best/coding) - **Introduced:** 2026 - **Format:** Repository patch - **Unit:** Percent (random guessing ≈ 0%) - **Official site:** [epoch.ai](https://epoch.ai/benchmarks) ## Top 15 models Top models on DeepSWE 1. GPT-6.1 Sol 75.2% 2. GPT-6 Astra 74.1% 3. Gemini 3.8 Flash 73.8% 4. Claude Opus 5 73.6% 5. GPT-5.6 Sol 72.7% 6. Claude Fable 5 69.9% 7. GPT-5.6 Terra 69.6% 8. GLM-5.3 69% 9. GPT-6 Sol 68.8% 10. Kimi K3 68.5% 11. Grok 4.6 67.5% 12. GPT-5.6 Luna 67.2% 13. GPT-5.5 67% 14. GPT-6 Luna 66.6% 15. Gemini 3.7 Flash 65.5% 16. 6065707580 Sponsored placements are available on pages like this one. [Advertise on Noometry](https://noometry.com/advertise) ## All results DeepSWE results by model | # | Model | Provider | Score | Setting | Source | Date | | --- | --- | --- | --- | --- | --- | --- | | 1 | [GPT-6.1 Sol](https://noometry.com/models/gpt-6-1-sol) | [OpenAI](https://noometry.com/providers/openai) | 75.2% | high | [Model card](https://openai.com/index/introducing-gpt-6-1-sol/) (self-reported) | 2026-09-29 | | 2 | [GPT-6 Astra](https://noometry.com/models/gpt-6-astra) | [OpenAI](https://noometry.com/providers/openai) | 74.1% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | | | 3 | [Gemini 3.8 Flash](https://noometry.com/models/gemini-3-8-flash) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 73.8% | high | [Epoch AI](https://epoch.ai/benchmarks) | | | 4 | [Claude Opus 5](https://noometry.com/models/claude-opus-5) | [Anthropic](https://noometry.com/providers/anthropic) | 73.6% | max | [Epoch AI](https://epoch.ai/benchmarks) | | | 5 | [GPT-5.6 Sol](https://noometry.com/models/gpt-5-6-sol) | [OpenAI](https://noometry.com/providers/openai) | 72.7% | max | [Epoch AI](https://epoch.ai/benchmarks) | | | 6 | [Claude Fable 5](https://noometry.com/models/claude-fable-5) | [Anthropic](https://noometry.com/providers/anthropic) | 69.9% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | | | 7 | [GPT-5.6 Terra](https://noometry.com/models/gpt-5-6-terra) | [OpenAI](https://noometry.com/providers/openai) | 69.6% | max | [Epoch AI](https://epoch.ai/benchmarks) | | | 8 | [GLM-5.3](https://noometry.com/models/glm-5-3) | [Z.ai (Zhipu)](https://noometry.com/providers/zai) | 69% | max | [Epoch AI](https://epoch.ai/benchmarks) | | | 9 | [GPT-6 Sol](https://noometry.com/models/gpt-6-sol) | [OpenAI](https://noometry.com/providers/openai) | 68.8% | max | [Model card](https://openai.com/index/introducing-gpt-6-sol-and-luna/) (self-reported) | 2026-09-22 | | 10 | [Kimi K3](https://noometry.com/models/kimi-k3) | [Moonshot AI](https://noometry.com/providers/moonshot) | 68.5% | max | [Epoch AI](https://epoch.ai/benchmarks) | | | 11 | [Grok 4.6](https://noometry.com/models/grok-4-6) | [xAI](https://noometry.com/providers/xai) | 67.5% | medium | [Epoch AI](https://epoch.ai/benchmarks) | | | 12 | [GPT-5.6 Luna](https://noometry.com/models/gpt-5-6-luna) | [OpenAI](https://noometry.com/providers/openai) | 67.2% | max | [Epoch AI](https://epoch.ai/benchmarks) | | | 13 | [GPT-5.5](https://noometry.com/models/gpt-5-5) | [OpenAI](https://noometry.com/providers/openai) | 67% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | | | 14 | [GPT-6 Luna](https://noometry.com/models/gpt-6-luna) | [OpenAI](https://noometry.com/providers/openai) | 66.6% | max | [Model card](https://openai.com/index/introducing-gpt-6-sol-and-luna/) (self-reported) | 2026-09-22 | | 15 | [Gemini 3.7 Flash](https://noometry.com/models/gemini-3-7-flash) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 65.5% | medium | [Epoch AI](https://epoch.ai/benchmarks) | | | 16 | [GLM-5.3-Flash](https://noometry.com/models/glm-5-3-flash) | [Z.ai (Zhipu)](https://noometry.com/providers/zai) | 63.4% | max | [Epoch AI](https://epoch.ai/benchmarks) | | | 17 | [Claude Opus 4.8](https://noometry.com/models/claude-opus-4-8) | [Anthropic](https://noometry.com/providers/anthropic) | 59% | max | [Epoch AI](https://epoch.ai/benchmarks) | | | 18 | [Qwen3.8 Max](https://noometry.com/models/qwen3-8-max) | [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 57.5% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | | | 19 | [Muse Spark 1.2](https://noometry.com/models/muse-spark-1-2) | [![](/logos/meta.svg) Meta](https://noometry.com/providers/meta) | 54.9% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | | | 20 | [Claude Sonnet 5](https://noometry.com/models/claude-sonnet-5) | [Anthropic](https://noometry.com/providers/anthropic) | 53.8% | max | [Epoch AI](https://epoch.ai/benchmarks) | | | 21 | [Grok 4.5](https://noometry.com/models/grok-4-5) | [xAI](https://noometry.com/providers/xai) | 53.8% | high | [Epoch AI](https://epoch.ai/benchmarks) | | | 22 | [Muse Spark 1.1](https://noometry.com/models/muse-spark-1-1) | [![](/logos/meta.svg) Meta](https://noometry.com/providers/meta) | 53.3% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 23 | [GPT-5.4](https://noometry.com/models/gpt-5-4) | [OpenAI](https://noometry.com/providers/openai) | 51.8% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | | | 24 | [Gemini 3.6 Flash](https://noometry.com/models/gemini-3-6-flash) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 46.7% | high | [Epoch AI](https://epoch.ai/benchmarks) | | | 25 | [GLM-5.2](https://noometry.com/models/glm-5-2) | [Z.ai (Zhipu)](https://noometry.com/providers/zai) | 43.8% | max | [Epoch AI](https://epoch.ai/benchmarks) | | | 26 | [Gemini 3.5 Flash](https://noometry.com/models/gemini-3-5-flash) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 37.4% | medium | [Epoch AI](https://epoch.ai/benchmarks) | | | 27 | [Kimi K2.7 Code](https://noometry.com/models/kimi-k2-7-code) | [Moonshot AI](https://noometry.com/providers/moonshot) | 30.5% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 28 | [Claude Sonnet 4.6](https://noometry.com/models/claude-sonnet-4-6) | [Anthropic](https://noometry.com/providers/anthropic) | 29.9% | high | [Epoch AI](https://epoch.ai/benchmarks) | | | 29 | [Gemini 3.1 Pro Preview](https://noometry.com/models/gemini-3-1-pro-preview) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 11.7% | | [Epoch AI](https://epoch.ai/benchmarks) | | ## Compare the leaders - [GPT-6.1 Sol vs GPT-6 Astra](https://noometry.com/compare/gpt-6-1-sol-vs-gpt-6-astra) - [GPT-6.1 Sol vs Gemini 3.8 Flash](https://noometry.com/compare/gemini-3-8-flash-vs-gpt-6-1-sol) - [GPT-6.1 Sol vs Claude Opus 5](https://noometry.com/compare/claude-opus-5-vs-gpt-6-1-sol) - [GPT-6.1 Sol vs GPT-5.6 Sol](https://noometry.com/compare/gpt-5-6-sol-vs-gpt-6-1-sol) - [GPT-6 Astra vs Gemini 3.8 Flash](https://noometry.com/compare/gemini-3-8-flash-vs-gpt-6-astra) - [GPT-6 Astra vs Claude Opus 5](https://noometry.com/compare/claude-opus-5-vs-gpt-6-astra) ## Other coding benchmarks - [SWE-bench Verified](https://noometry.com/benchmarks/swe-bench-verified) - [FrontierCode](https://noometry.com/benchmarks/frontiercode) - [SWE-bench Verified (bash only)](https://noometry.com/benchmarks/swe-bench-bash-only) - [Aider Polyglot](https://noometry.com/benchmarks/aider-polyglot) - [LMArena WebDev](https://noometry.com/benchmarks/arena-webdev) - [CursorBench](https://noometry.com/benchmarks/cursorbench) - [SWE-bench Multilingual](https://noometry.com/benchmarks/swe-bench-multilingual) - [FrontierSWE](https://noometry.com/benchmarks/frontierswe) - [SciCode](https://noometry.com/benchmarks/scicode) - [GSO](https://noometry.com/benchmarks/gso-bench) - [WeirdML](https://noometry.com/benchmarks/weirdml) - [LMArena Coding](https://noometry.com/benchmarks/arena-coding) ## Frequently asked questions ### Which model has the highest DeepSWE score? As of October 2026, GPT-6.1 Sol has the highest published DeepSWE score on Noometry at 75.2%, out of 29 models with results. ### What is the best open-weight model on DeepSWE? GLM-5.3 has the highest DeepSWE accuracy among open-weight models at 69%, ranking 8 of 29 overall. ### Cite this page Noometry. (2026). DeepSWE leaderboard. Retrieved October 10, 2026, from https://noometry.com/benchmarks/deepswe Quote Noometry with a link back to this page. It is also available in [Markdown](https://noometry.com/md/benchmarks/deepswe.md).