Coding benchmark

# SWE-bench Verified (bash only) leaderboard

> SWE-bench Verified (bash only) results for 39 AI models, led by Claude Opus 4.5 at 76.8%. What the benchmark measures, who runs it, and a source for every score.
- Canonical page: https://noometry.com/benchmarks/swe-bench-bash-only
- Last updated: 2026-10-10
- Title: SWE-bench Verified (bash only) Leaderboard (October 2026): Scores by Model

As of October 2026, Claude Opus 4.5 has the highest published SWE-bench Verified (bash only) score on Noometry at 76.8%, out of 39 models with results.

Last verified October 10, 2026

## About SWE-bench Verified (bash only)

The 500 human-validated SWE-bench Verified GitHub issues, solved by every model inside the same minimal bash-only agent, so the score reflects the model rather than the scaffold.

- **Category:** [Coding](https://noometry.com/best/coding)
- **Introduced:** 2025
- **Size:** 500 tasks
- **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 (bash only)

1.  Claude Opus 4.5 76.8%
2.  Gemini 3 Flash Preview 75.8%
3.  MiniMax-M2.5 75.8%
4.  Claude Opus 4.6 75.6%
5.  Gemini 3 Pro 74.2%
6.  GLM-5 72.8%
7.  GPT-5.2 72.8%
8.  GPT-5.2 Codex 72.8%
9.  Claude Sonnet 4.5 71.4%
10.  Kimi K2.5 70.8%
11.  DeepSeek-V3.2-Exp 70%
12.  Claude Opus 4 67.6%
13.  Claude Haiku 4.5 66.6%
14.  GPT-5.1 66%
15.  GPT-5.1-Codex 66%
16.  6065707580

Sponsored placements are available on pages like this one. [Advertise on Noometry](https://noometry.com/advertise)

## All results

SWE-bench Verified (bash only) results by model
| # | Model | Provider | Score | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- | --- |
| 1 | [Claude Opus 4.5](https://noometry.com/models/claude-opus-4-5) | [Anthropic](https://noometry.com/providers/anthropic) | 76.8% | high | [SWE-bench](https://www.swebench.com/) | 2026-02-17 |
| 2 | [Gemini 3 Flash Preview](https://noometry.com/models/gemini-3-flash-preview) |  [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 75.8% | high | [SWE-bench](https://www.swebench.com/) | 2026-02-17 |
| 3 | [MiniMax-M2.5](https://noometry.com/models/minimax-m2-5) |  [![](/logos/minimax.svg) MiniMax](https://noometry.com/providers/minimax) | 75.8% | high | [SWE-bench](https://www.swebench.com/) | 2026-02-17 |
| 4 | [Claude Opus 4.6](https://noometry.com/models/claude-opus-4-6) | [Anthropic](https://noometry.com/providers/anthropic) | 75.6% |  | [SWE-bench](https://www.swebench.com/) | 2026-02-17 |
| 5 | [Gemini 3 Pro](https://noometry.com/models/gemini-3-pro) |  [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 74.2% |  | [SWE-bench](https://www.swebench.com/) | 2025-11-18 |
| 6 | [GLM-5](https://noometry.com/models/glm-5) | [Z.ai (Zhipu)](https://noometry.com/providers/zai) | 72.8% | high | [SWE-bench](https://www.swebench.com/) | 2026-02-17 |
| 7 | [GPT-5.2](https://noometry.com/models/gpt-5-2) | [OpenAI](https://noometry.com/providers/openai) | 72.8% | high | [SWE-bench](https://www.swebench.com/) | 2026-02-17 |
| 8 | [GPT-5.2 Codex](https://noometry.com/models/gpt-5-2-codex) | [OpenAI](https://noometry.com/providers/openai) | 72.8% |  | [SWE-bench](https://www.swebench.com/) | 2026-02-19 |
| 9 | [Claude Sonnet 4.5](https://noometry.com/models/claude-sonnet-4-5) | [Anthropic](https://noometry.com/providers/anthropic) | 71.4% | high | [SWE-bench](https://www.swebench.com/) | 2026-02-17 |
| 10 | [Kimi K2.5](https://noometry.com/models/kimi-k2-5) | [Moonshot AI](https://noometry.com/providers/moonshot) | 70.8% | high | [SWE-bench](https://www.swebench.com/) | 2026-02-17 |
| 11 | [DeepSeek-V3.2-Exp](https://noometry.com/models/deepseek-v3-2-exp) |  [![](/logos/deepseek.svg) DeepSeek](https://noometry.com/providers/deepseek) | 70% | high | [SWE-bench](https://www.swebench.com/) | 2026-02-17 |
| 12 | [Claude Opus 4](https://noometry.com/models/claude-opus-4) | [Anthropic](https://noometry.com/providers/anthropic) | 67.6% |  | [SWE-bench](https://www.swebench.com/) | 2025-08-02 |
| 13 | [Claude Haiku 4.5](https://noometry.com/models/claude-haiku-4-5) | [Anthropic](https://noometry.com/providers/anthropic) | 66.6% | high | [SWE-bench](https://www.swebench.com/) | 2026-02-17 |
| 14 | [GPT-5.1](https://noometry.com/models/gpt-5-1) | [OpenAI](https://noometry.com/providers/openai) | 66% | medium | [SWE-bench](https://www.swebench.com/) | 2025-11-20 |
| 15 | [GPT-5.1-Codex](https://noometry.com/models/gpt-5-1-codex) | [OpenAI](https://noometry.com/providers/openai) | 66% | medium | [SWE-bench](https://www.swebench.com/) | 2025-11-24 |
| 16 | [GPT-5](https://noometry.com/models/gpt-5) | [OpenAI](https://noometry.com/providers/openai) | 65% | medium | [SWE-bench](https://www.swebench.com/) | 2025-08-07 |
| 17 | [Claude Sonnet 4](https://noometry.com/models/claude-sonnet-4) | [Anthropic](https://noometry.com/providers/anthropic) | 64.9% |  | [SWE-bench](https://www.swebench.com/) | 2025-07-26 |
| 18 | [Kimi K2 (Jul 2025)](https://noometry.com/models/kimi-k2) | [Moonshot AI](https://noometry.com/providers/moonshot) | 63.4% |  | [SWE-bench](https://www.swebench.com/) | 2025-12-10 |
| 19 | [MiniMax-M2](https://noometry.com/models/minimax-m2) |  [![](/logos/minimax.svg) MiniMax](https://noometry.com/providers/minimax) | 61% |  | [SWE-bench](https://www.swebench.com/) | 2025-11-24 |
| 20 | [GPT-5 Mini](https://noometry.com/models/gpt-5-mini) | [OpenAI](https://noometry.com/providers/openai) | 59.8% | medium | [SWE-bench](https://www.swebench.com/) | 2025-08-07 |
| 21 | [o3](https://noometry.com/models/o3) | [OpenAI](https://noometry.com/providers/openai) | 58.4% |  | [SWE-bench](https://www.swebench.com/) | 2025-07-26 |
| 22 | [Devstral Small 2505](https://noometry.com/models/devstral-small) |  [![](/logos/mistral.svg) Mistral AI](https://noometry.com/providers/mistral) | 56.4% |  | [SWE-bench](https://www.swebench.com/) | 2025-12-09 |
| 23 | [GLM-4.6](https://noometry.com/models/glm-4-6) | [Z.ai (Zhipu)](https://noometry.com/providers/zai) | 55.4% |  | [SWE-bench](https://www.swebench.com/) | 2025-12-01 |
| 24 | [Qwen3-Coder 480B-A35B Instruct](https://noometry.com/models/qwen3-coder-480b-a35b-instruct) |  [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 55.4% |  | [SWE-bench](https://www.swebench.com/) | 2025-08-02 |
| 25 | [GLM-4.5](https://noometry.com/models/glm-4-5) | [Z.ai (Zhipu)](https://noometry.com/providers/zai) | 54.2% |  | [SWE-bench](https://www.swebench.com/) | 2025-08-22 |
| 26 | [Devstral 2](https://noometry.com/models/devstral-2) |  [![](/logos/mistral.svg) Mistral AI](https://noometry.com/providers/mistral) | 53.8% |  | [SWE-bench](https://www.swebench.com/) | 2025-12-09 |
| 27 | [Gemini 2.5 Pro](https://noometry.com/models/gemini-2-5-pro) |  [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 53.6% |  | [SWE-bench](https://www.swebench.com/) | 2025-07-26 |
| 28 | [Claude 3.7 Sonnet](https://noometry.com/models/claude-3-7-sonnet) | [Anthropic](https://noometry.com/providers/anthropic) | 52.8% |  | [SWE-bench](https://www.swebench.com/) | 2025-07-20 |
| 29 | [o4-mini](https://noometry.com/models/o4-mini) | [OpenAI](https://noometry.com/providers/openai) | 45% |  | [SWE-bench](https://www.swebench.com/) | 2025-07-26 |
| 30 | [GPT-4.1](https://noometry.com/models/gpt-4-1) | [OpenAI](https://noometry.com/providers/openai) | 39.6% |  | [SWE-bench](https://www.swebench.com/) | 2025-07-26 |
| 31 | [GPT-5 Nano](https://noometry.com/models/gpt-5-nano) | [OpenAI](https://noometry.com/providers/openai) | 34.8% | medium | [SWE-bench](https://www.swebench.com/) | 2025-08-07 |
| 32 | [Gemini 2.5 Flash](https://noometry.com/models/gemini-2-5-flash) |  [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 28.7% |  | [SWE-bench](https://www.swebench.com/) | 2025-07-26 |
| 33 | [gpt-oss-120b](https://noometry.com/models/gpt-oss-120b) | [OpenAI](https://noometry.com/providers/openai) | 26% |  | [SWE-bench](https://www.swebench.com/) | 2025-08-07 |
| 34 | [GPT-4.1 mini](https://noometry.com/models/gpt-4-1-mini) | [OpenAI](https://noometry.com/providers/openai) | 23.9% |  | [SWE-bench](https://www.swebench.com/) | 2025-07-20 |
| 35 | [GPT-4o](https://noometry.com/models/gpt-4o) | [OpenAI](https://noometry.com/providers/openai) | 21.6% |  | [SWE-bench](https://www.swebench.com/) | 2025-07-20 |
| 36 | [Llama 4 Maverick](https://noometry.com/models/llama-4-maverick) |  [![](/logos/meta.svg) Meta](https://noometry.com/providers/meta) | 21% |  | [SWE-bench](https://www.swebench.com/) | 2025-07-20 |
| 37 | [Gemini 2.0 Flash (Feb 2025)](https://noometry.com/models/gemini-2-0-flash) |  [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 13.5% |  | [SWE-bench](https://www.swebench.com/) | 2025-07-26 |
| 38 | [Llama 4 Scout](https://noometry.com/models/llama-4-scout) |  [![](/logos/meta.svg) Meta](https://noometry.com/providers/meta) | 9.1% |  | [SWE-bench](https://www.swebench.com/) | 2025-07-20 |
| 39 | [Qwen2.5-Coder-32B](https://noometry.com/models/qwen2-5-coder-32b) |  [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 9% |  | [SWE-bench](https://www.swebench.com/) | 2025-08-03 |

## Compare the leaders

-   [Claude Opus 4.5 vs Gemini 3 Flash Preview](https://noometry.com/compare/claude-opus-4-5-vs-gemini-3-flash-preview)
-   [Claude Opus 4.5 vs MiniMax-M2.5](https://noometry.com/compare/claude-opus-4-5-vs-minimax-m2-5)
-   [Claude Opus 4.5 vs Claude Opus 4.6](https://noometry.com/compare/claude-opus-4-5-vs-claude-opus-4-6)
-   [Claude Opus 4.5 vs Gemini 3 Pro](https://noometry.com/compare/claude-opus-4-5-vs-gemini-3-pro)
-   [Gemini 3 Flash Preview vs MiniMax-M2.5](https://noometry.com/compare/gemini-3-flash-preview-vs-minimax-m2-5)
-   [Gemini 3 Flash Preview vs Claude Opus 4.6](https://noometry.com/compare/claude-opus-4-6-vs-gemini-3-flash-preview)

## Other coding benchmarks

-   [SWE-bench Verified](https://noometry.com/benchmarks/swe-bench-verified)
-   [DeepSWE](https://noometry.com/benchmarks/deepswe)
-   [FrontierCode](https://noometry.com/benchmarks/frontiercode)
-   [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 (bash only) measure?

The 500 human-validated SWE-bench Verified GitHub issues, solved by every model inside the same minimal bash-only agent, so the score reflects the model rather than the scaffold.

### Which model has the highest SWE-bench Verified (bash only) score?

As of October 2026, Claude Opus 4.5 has the highest published SWE-bench Verified (bash only) score on Noometry at 76.8%, out of 39 models with results.

### What is the best open-weight model on SWE-bench Verified (bash only)?

MiniMax-M2.5 has the highest SWE-bench Verified (bash only) accuracy among open-weight models at 75.8%, ranking 3 of 39 overall.

### Cite this page

Noometry. (2026). SWE-bench Verified (bash only) leaderboard. Retrieved October 10, 2026, from https://noometry.com/benchmarks/swe-bench-bash-only

Quote Noometry with a link back to this page. It is also available in [Markdown](https://noometry.com/md/benchmarks/swe-bench-bash-only.md).