Coding benchmark # SWE-bench Verified leaderboard > SWE-bench Verified results for 32 AI models, led by Claude Opus 4.7 at 83.5%. What the benchmark measures, who runs it, and a source for every score. - Canonical page: https://noometry.com/benchmarks/swe-bench-verified - Last updated: 2026-10-10 - Title: SWE-bench Verified Leaderboard (October 2026): Scores by Model As of October 2026, Claude Opus 4.7 has the highest published SWE-bench Verified score on Noometry at 83.5%, out of 32 models with results. Last verified October 10, 2026 ## About SWE-bench Verified 500 real GitHub issues from Python repositories, human-verified as solvable. The model must produce a patch that makes the hidden tests pass. - **Category:** [Coding](https://noometry.com/best/coding) - **Introduced:** 2024 - **Size:** 500 issues - **Format:** Repository patch - **Unit:** Percent (random guessing ≈ 0%) - **Official site:** [www.swebench.com](https://www.swebench.com) ## Top 15 models Top models on SWE-bench Verified 1. Claude Opus 4.7 83.5% 2. GPT-5.5 80.6% 3. Gemini 3.5 Flash 79.3% 4. Claude Opus 4.6 78.7% 5. GLM-5.2 78.7% 6. DeepSeek V4 Pro 77.6% 7. Qwen3.7 Max 77.3% 8. GPT-5.4 76.9% 9. Claude Opus 4.5 76.7% 10. Kimi K2.6 76.7% 11. Qwen3.6 Max Preview 76.7% 12. Gemini 3.1 Pro Preview 75.6% 13. Gemini 3 Flash Preview 75.4% 14. Claude Sonnet 4.6 75.2% 15. GPT-5.3 Codex 74.8% 16. 70758085 Sponsored placements are available on pages like this one. [Advertise on Noometry](https://noometry.com/advertise) ## All results SWE-bench Verified results by model | # | Model | Provider | Score | Setting | Source | Date | | --- | --- | --- | --- | --- | --- | --- | | 1 | [Claude Opus 4.7](https://noometry.com/models/claude-opus-4-7) | [Anthropic](https://noometry.com/providers/anthropic) | 83.5% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-04-20 | | 2 | [GPT-5.5](https://noometry.com/models/gpt-5-5) | [OpenAI](https://noometry.com/providers/openai) | 80.6% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | 2026-04-24 | | 3 | [Gemini 3.5 Flash](https://noometry.com/models/gemini-3-5-flash) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 79.3% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-06-01 | | 4 | [Claude Opus 4.6](https://noometry.com/models/claude-opus-4-6) | [Anthropic](https://noometry.com/providers/anthropic) | 78.7% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-02-18 | | 5 | [GLM-5.2](https://noometry.com/models/glm-5-2) | [Z.ai (Zhipu)](https://noometry.com/providers/zai) | 78.7% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-06-25 | | 6 | [DeepSeek V4 Pro](https://noometry.com/models/deepseek-v4-pro) | [![](/logos/deepseek.svg) DeepSeek](https://noometry.com/providers/deepseek) | 77.6% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-06-18 | | 7 | [Qwen3.7 Max](https://noometry.com/models/qwen3-7-max) | [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 77.3% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-06-18 | | 8 | [GPT-5.4](https://noometry.com/models/gpt-5-4) | [OpenAI](https://noometry.com/providers/openai) | 76.9% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-03-06 | | 9 | [Claude Opus 4.5](https://noometry.com/models/claude-opus-4-5) | [Anthropic](https://noometry.com/providers/anthropic) | 76.7% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-02-05 | | 10 | [Kimi K2.6](https://noometry.com/models/kimi-k2-6) | [Moonshot AI](https://noometry.com/providers/moonshot) | 76.7% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-05-08 | | 11 | [Qwen3.6 Max Preview](https://noometry.com/models/qwen3-6-max-preview) | [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 76.7% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-05-28 | | 12 | [Gemini 3.1 Pro Preview](https://noometry.com/models/gemini-3-1-pro-preview) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 75.6% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-02-24 | | 13 | [Gemini 3 Flash Preview](https://noometry.com/models/gemini-3-flash-preview) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 75.4% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-02-18 | | 14 | [Claude Sonnet 4.6](https://noometry.com/models/claude-sonnet-4-6) | [Anthropic](https://noometry.com/providers/anthropic) | 75.2% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-02-21 | | 15 | [GPT-5.3 Codex](https://noometry.com/models/gpt-5-3-codex) | [OpenAI](https://noometry.com/providers/openai) | 74.8% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-02-25 | | 16 | [GLM-5.1](https://noometry.com/models/glm-5-1) | [Z.ai (Zhipu)](https://noometry.com/providers/zai) | 74.2% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-05-15 | | 17 | [GPT-5.2](https://noometry.com/models/gpt-5-2) | [OpenAI](https://noometry.com/providers/openai) | 73.8% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-02-12 | | 18 | [Kimi K2.5](https://noometry.com/models/kimi-k2-5) | [Moonshot AI](https://noometry.com/providers/moonshot) | 73.8% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-02-17 | | 19 | [GPT-5](https://noometry.com/models/gpt-5) | [OpenAI](https://noometry.com/providers/openai) | 73.6% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-02-06 | | 20 | [Claude Opus 4.1](https://noometry.com/models/claude-opus-4-1) | [Anthropic](https://noometry.com/providers/anthropic) | 73.3% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-02-11 | | 21 | [Gemini 3 Pro](https://noometry.com/models/gemini-3-pro) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 72.9% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-02-13 | | 22 | [GLM-5](https://noometry.com/models/glm-5) | [Z.ai (Zhipu)](https://noometry.com/providers/zai) | 72.1% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-02-15 | | 23 | [Claude Sonnet 4.5](https://noometry.com/models/claude-sonnet-4-5) | [Anthropic](https://noometry.com/providers/anthropic) | 71.3% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-02-05 | | 24 | [Claude Opus 4](https://noometry.com/models/claude-opus-4) | [Anthropic](https://noometry.com/providers/anthropic) | 70.7% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-02-06 | | 25 | [GPT-5.1](https://noometry.com/models/gpt-5-1) | [OpenAI](https://noometry.com/providers/openai) | 68% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-02-18 | | 26 | [GPT-5 Mini](https://noometry.com/models/gpt-5-mini) | [OpenAI](https://noometry.com/providers/openai) | 64.7% | medium | [Epoch AI](https://epoch.ai/benchmarks) | 2026-02-01 | | 27 | [o3](https://noometry.com/models/o3) | [OpenAI](https://noometry.com/providers/openai) | 62.3% | medium | [Epoch AI](https://epoch.ai/benchmarks) | 2026-02-12 | | 28 | [Claude 3.7 Sonnet](https://noometry.com/models/claude-3-7-sonnet) | [Anthropic](https://noometry.com/providers/anthropic) | 61% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-02-04 | | 29 | [Qwen3.6 Plus](https://noometry.com/models/qwen3-6-plus) | [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 57.9% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-05-14 | | 30 | [Gemini 2.5 Pro](https://noometry.com/models/gemini-2-5-pro) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 57.6% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-02-13 | | 31 | [GPT-4.1](https://noometry.com/models/gpt-4-1) | [OpenAI](https://noometry.com/providers/openai) | 48.5% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-02-08 | | 32 | [GPT-4o](https://noometry.com/models/gpt-4o) | [OpenAI](https://noometry.com/providers/openai) | 31% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-02-11 | ## Compare the leaders - [Claude Opus 4.7 vs GPT-5.5](https://noometry.com/compare/claude-opus-4-7-vs-gpt-5-5) - [Claude Opus 4.7 vs Gemini 3.5 Flash](https://noometry.com/compare/claude-opus-4-7-vs-gemini-3-5-flash) - [Claude Opus 4.7 vs Claude Opus 4.6](https://noometry.com/compare/claude-opus-4-6-vs-claude-opus-4-7) - [Claude Opus 4.7 vs GLM-5.2](https://noometry.com/compare/claude-opus-4-7-vs-glm-5-2) - [GPT-5.5 vs Gemini 3.5 Flash](https://noometry.com/compare/gemini-3-5-flash-vs-gpt-5-5) - [GPT-5.5 vs Claude Opus 4.6](https://noometry.com/compare/claude-opus-4-6-vs-gpt-5-5) ## Other coding benchmarks - [DeepSWE](https://noometry.com/benchmarks/deepswe) - [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 ### What does SWE-bench Verified measure? 500 real GitHub issues from Python repositories, human-verified as solvable. The model must produce a patch that makes the hidden tests pass. ### Which model has the highest SWE-bench Verified score? As of October 2026, Claude Opus 4.7 has the highest published SWE-bench Verified score on Noometry at 83.5%, out of 32 models with results. ### What is the best open-weight model on SWE-bench Verified? GLM-5.2 has the highest SWE-bench Verified accuracy among open-weight models at 78.7%, ranking 5 of 32 overall. ### Cite this page Noometry. (2026). SWE-bench Verified leaderboard. Retrieved October 10, 2026, from https://noometry.com/benchmarks/swe-bench-verified Quote Noometry with a link back to this page. It is also available in [Markdown](https://noometry.com/md/benchmarks/swe-bench-verified.md).