Coding benchmark

# LiveBench Coding leaderboard

> LiveBench Coding results for 39 AI models, led by Gemini 2.5 Pro at 85.9%. What the benchmark measures, who runs it, and a source for every score.
- Canonical page: https://noometry.com/benchmarks/livebench-coding
- Last updated: 2026-10-10
- Title: LiveBench Coding Leaderboard (October 2026): Scores by Model

As of October 2026, Gemini 2.5 Pro has the highest published LiveBench Coding score on Noometry at 85.9%, out of 39 models with results.

Last verified October 10, 2026

## About LiveBench Coding

LiveBench's coding tasks, refreshed regularly to limit training-data contamination.

- **Category:** [Coding](https://noometry.com/best/coding)
- **Introduced:** 2024
- **Format:** Code generation
- **Unit:** Percent (random guessing ≈ 0%)
- **Official site:** [livebench.ai](https://livebench.ai)

## Top 15 models

Top models on LiveBench Coding

1.  Gemini 2.5 Pro 85.9%
2.  o3-mini 82.7%
3.  GPT-4.5 75.2%
4.  Claude 3.7 Sonnet 74.5%
5.  GPT-5.1 72.5%
6.  QwQ-32B 72.2%
7.  DeepSeek-V3 70.9%
8.  o1 69.7%
9.  Claude 3.5 Sonnet 67.1%
10.  DeepSeek-R1 66.7%
11.  Qwen2.5-Max 64.4%
12.  Gemini 2.0 Pro 63.5%
13.  Gemini 2.0 Flash (Feb 2025) 63.4%
14.  Qwen2.5-Coder-32B 56.9%
15.  DeepSeek-R1-Distill-Llama-70B 51.6%
16.  405060708090

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

## All results

LiveBench Coding results by model
| # | Model | Provider | Score | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- | --- |
| 1 | [Gemini 2.5 Pro](https://noometry.com/models/gemini-2-5-pro) |  [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 85.9% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 2 | [o3-mini](https://noometry.com/models/o3-mini) | [OpenAI](https://noometry.com/providers/openai) | 82.7% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 3 | [GPT-4.5](https://noometry.com/models/gpt-4-5) | [OpenAI](https://noometry.com/providers/openai) | 75.2% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 4 | [Claude 3.7 Sonnet](https://noometry.com/models/claude-3-7-sonnet) | [Anthropic](https://noometry.com/providers/anthropic) | 74.5% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 5 | [GPT-5.1](https://noometry.com/models/gpt-5-1) | [OpenAI](https://noometry.com/providers/openai) | 72.5% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 6 | [QwQ-32B](https://noometry.com/models/qwq-32b) |  [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 72.2% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 7 | [DeepSeek-V3](https://noometry.com/models/deepseek-v3) |  [![](/logos/deepseek.svg) DeepSeek](https://noometry.com/providers/deepseek) | 70.9% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 8 | [o1](https://noometry.com/models/o1) | [OpenAI](https://noometry.com/providers/openai) | 69.7% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 9 | [Claude 3.5 Sonnet](https://noometry.com/models/claude-3-5-sonnet) | [Anthropic](https://noometry.com/providers/anthropic) | 67.1% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 10 | [DeepSeek-R1](https://noometry.com/models/deepseek-r1) |  [![](/logos/deepseek.svg) DeepSeek](https://noometry.com/providers/deepseek) | 66.7% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 11 | [Qwen2.5-Max](https://noometry.com/models/qwen2-5-max) |  [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 64.4% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 12 | [Gemini 2.0 Pro](https://noometry.com/models/gemini-2-0-pro) |  [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 63.5% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 13 | [Gemini 2.0 Flash (Feb 2025)](https://noometry.com/models/gemini-2-0-flash) |  [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 63.4% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 14 | [Qwen2.5-Coder-32B](https://noometry.com/models/qwen2-5-coder-32b) |  [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 56.9% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 15 | [DeepSeek-R1-Distill-Llama-70B](https://noometry.com/models/deepseek-r1-distill-llama-70b) |  [![](/logos/deepseek.svg) DeepSeek](https://noometry.com/providers/deepseek) | 51.6% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 16 | [GPT-4o](https://noometry.com/models/gpt-4o) | [OpenAI](https://noometry.com/providers/openai) | 51.4% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 17 | [Claude 3.5 Haiku](https://noometry.com/models/claude-3-5-haiku) | [Anthropic](https://noometry.com/providers/anthropic) | 51.4% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 18 | [o1-mini](https://noometry.com/models/o1-mini) | [OpenAI](https://noometry.com/providers/openai) | 48% | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 19 | [Gemini 2.0 Flash-Lite](https://noometry.com/models/gemini-2-0-flash-lite) |  [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 47.1% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 20 | [Mistral Large](https://noometry.com/models/mistral-large) |  [![](/logos/mistral.svg) Mistral AI](https://noometry.com/providers/mistral) | 47.1% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 21 | [Grok-2 (Dec 2024)](https://noometry.com/models/grok-2) | [xAI](https://noometry.com/providers/xai) | 46.4% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 22 | [GPT-4o mini](https://noometry.com/models/gpt-4o-mini) | [OpenAI](https://noometry.com/providers/openai) | 43.1% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 23 | [Gemma 3 27B](https://noometry.com/models/gemma-3-27b) |  [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 39.9% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 24 | [Claude 3 Opus](https://noometry.com/models/claude-3-opus) | [Anthropic](https://noometry.com/providers/anthropic) | 38.6% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 25 | [Amazon Nova Pro](https://noometry.com/models/amazon-nova-pro) | [Amazon](https://noometry.com/providers/amazon) | 38.1% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 26 | [Llama-3.3-70B-Instruct](https://noometry.com/models/llama-3-3-70b-instruct) |  [![](/logos/meta.svg) Meta](https://noometry.com/providers/meta) | 36.6% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 27 | [Mistral Small](https://noometry.com/models/mistral-small) |  [![](/logos/mistral.svg) Mistral AI](https://noometry.com/providers/mistral) | 36.2% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 28 | [Gemma 2 27B](https://noometry.com/models/gemma-2-27b) |  [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 36% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 29 | [Sonar](https://noometry.com/models/sonar) |  [![](/logos/perplexity.svg) Perplexity](https://noometry.com/providers/perplexity) | 35.1% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 30 | [DeepSeek-R1-Distill-Qwen-32B](https://noometry.com/models/deepseek-r1-distill-qwen-32b) |  [![](/logos/deepseek.svg) DeepSeek](https://noometry.com/providers/deepseek) | 33.7% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 31 | [Phi-4](https://noometry.com/models/phi-4) |  [![](/logos/microsoft.svg) Microsoft](https://noometry.com/providers/microsoft) | 30.7% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 32 | [Amazon Nova Lite](https://noometry.com/models/amazon-nova-lite) | [Amazon](https://noometry.com/providers/amazon) | 27.5% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 33 | [Gemma 2 9B](https://noometry.com/models/gemma-2-9b) |  [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 22.5% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 34 | [Phi 3 Small 8k Instruct](https://noometry.com/models/phi-3-small-8k-instruct) |  [![](/logos/microsoft.svg) Microsoft](https://noometry.com/providers/microsoft) | 20.3% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 35 | [Amazon Nova Micro](https://noometry.com/models/amazon-nova-micro) | [Amazon](https://noometry.com/providers/amazon) | 20.2% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 36 | [Command R+](https://noometry.com/models/command-r-plus) |  [![](/logos/cohere.svg) Cohere](https://noometry.com/providers/cohere) | 19.1% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 37 | [Command R](https://noometry.com/models/command-r) |  [![](/logos/cohere.svg) Cohere](https://noometry.com/providers/cohere) | 17.9% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 38 | [Phi 3 Mini 4k Instruct](https://noometry.com/models/phi-3-mini-4k-instruct) |  [![](/logos/microsoft.svg) Microsoft](https://noometry.com/providers/microsoft) | 15.5% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 39 | [OLMo 2 Furious 13B](https://noometry.com/models/olmo-2-furious-13b) |  [![](/logos/ai2.svg) Allen Institute for AI (Ai2)](https://noometry.com/providers/ai2) | 10.4% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

## Compare the leaders

-   [Gemini 2.5 Pro vs o3-mini](https://noometry.com/compare/gemini-2-5-pro-vs-o3-mini)
-   [Gemini 2.5 Pro vs GPT-4.5](https://noometry.com/compare/gemini-2-5-pro-vs-gpt-4-5)
-   [Gemini 2.5 Pro vs Claude 3.7 Sonnet](https://noometry.com/compare/claude-3-7-sonnet-vs-gemini-2-5-pro)
-   [Gemini 2.5 Pro vs GPT-5.1](https://noometry.com/compare/gemini-2-5-pro-vs-gpt-5-1)
-   [o3-mini vs GPT-4.5](https://noometry.com/compare/gpt-4-5-vs-o3-mini)
-   [o3-mini vs Claude 3.7 Sonnet](https://noometry.com/compare/claude-3-7-sonnet-vs-o3-mini)

## 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)
-   [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)

## Frequently asked questions

### What does LiveBench Coding measure?

LiveBench's coding tasks, refreshed regularly to limit training-data contamination.

### Which model has the highest LiveBench Coding score?

As of October 2026, Gemini 2.5 Pro has the highest published LiveBench Coding score on Noometry at 85.9%, out of 39 models with results.

### What is the best open-weight model on LiveBench Coding?

QwQ-32B has the highest LiveBench Coding accuracy among open-weight models at 72.2%, ranking 6 of 39 overall.

### Cite this page

Noometry. (2026). LiveBench Coding leaderboard. Retrieved October 10, 2026, from https://noometry.com/benchmarks/livebench-coding

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