Coding benchmark # AlgoTune leaderboard > AlgoTune results for 18 AI models, led by GPT-5.2 at 2.05. What the benchmark measures, who runs it, and a source for every score. - Canonical page: https://noometry.com/benchmarks/algotune - Last updated: 2026-10-10 - Title: AlgoTune Leaderboard (October 2026): Scores by Model As of October 2026, GPT-5.2 has the highest published AlgoTune score on Noometry at 2.05, out of 18 models with results. Last verified October 10, 2026 ## About AlgoTune Speeding up numerical and algorithmic Python functions, scored as average speed-up over reference code. - **Category:** [Coding](https://noometry.com/best/coding) - **Introduced:** 2025 - **Format:** Code optimization - **Unit:** Raw score - **Official site:** [algotune.io](https://algotune.io) ## Top 15 models Top models on AlgoTune 1. GPT-5.2 2.05 2. Gemini 3.1 Pro Preview 2.02 3. GPT-5.4 1.85 4. Gemini 3 Pro 1.83 5. Claude Opus 4.5 1.77 6. o4-mini 1.72 7. DeepSeek-R1 1.7 8. GPT-5 1.67 9. Claude Sonnet 4.5 1.52 10. GLM-4.5 1.52 11. Gemini 2.5 Pro 1.51 12. Claude Opus 4.6 1.47 13. Qwen3-Coder 480B-A35B Instruct 1.44 14. gpt-oss-120b 1.41 15. GPT-5 Mini 1.38 16. 1.21.41.61.82.02.2 Sponsored placements are available on pages like this one. [Advertise on Noometry](https://noometry.com/advertise) ## All results AlgoTune results by model | # | Model | Provider | Rating | Setting | Source | Date | | --- | --- | --- | --- | --- | --- | --- | | 1 | [GPT-5.2](https://noometry.com/models/gpt-5-2) | [OpenAI](https://noometry.com/providers/openai) | 2.05 | medium | [Epoch AI](https://epoch.ai/benchmarks) | | | 2 | [Gemini 3.1 Pro Preview](https://noometry.com/models/gemini-3-1-pro-preview) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 2.02 | | [Epoch AI](https://epoch.ai/benchmarks) | | | 3 | [GPT-5.4](https://noometry.com/models/gpt-5-4) | [OpenAI](https://noometry.com/providers/openai) | 1.85 | high | [Epoch AI](https://epoch.ai/benchmarks) | | | 4 | [Gemini 3 Pro](https://noometry.com/models/gemini-3-pro) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 1.83 | | [Epoch AI](https://epoch.ai/benchmarks) | | | 5 | [Claude Opus 4.5](https://noometry.com/models/claude-opus-4-5) | [Anthropic](https://noometry.com/providers/anthropic) | 1.77 | | [Epoch AI](https://epoch.ai/benchmarks) | | | 6 | [o4-mini](https://noometry.com/models/o4-mini) | [OpenAI](https://noometry.com/providers/openai) | 1.72 | high | [Epoch AI](https://epoch.ai/benchmarks) | | | 7 | [DeepSeek-R1](https://noometry.com/models/deepseek-r1) | [![](/logos/deepseek.svg) DeepSeek](https://noometry.com/providers/deepseek) | 1.7 | | [Epoch AI](https://epoch.ai/benchmarks) | | | 8 | [GPT-5](https://noometry.com/models/gpt-5) | [OpenAI](https://noometry.com/providers/openai) | 1.67 | high | [Epoch AI](https://epoch.ai/benchmarks) | | | 9 | [Claude Sonnet 4.5](https://noometry.com/models/claude-sonnet-4-5) | [Anthropic](https://noometry.com/providers/anthropic) | 1.52 | | [Epoch AI](https://epoch.ai/benchmarks) | | | 10 | [GLM-4.5](https://noometry.com/models/glm-4-5) | [Z.ai (Zhipu)](https://noometry.com/providers/zai) | 1.52 | thinking | [Epoch AI](https://epoch.ai/benchmarks) | | | 11 | [Gemini 2.5 Pro](https://noometry.com/models/gemini-2-5-pro) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 1.51 | | [Epoch AI](https://epoch.ai/benchmarks) | | | 12 | [Claude Opus 4.6](https://noometry.com/models/claude-opus-4-6) | [Anthropic](https://noometry.com/providers/anthropic) | 1.47 | high | [Epoch AI](https://epoch.ai/benchmarks) | | | 13 | [Qwen3-Coder 480B-A35B Instruct](https://noometry.com/models/qwen3-coder-480b-a35b-instruct) | [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 1.44 | | [Epoch AI](https://epoch.ai/benchmarks) | | | 14 | [gpt-oss-120b](https://noometry.com/models/gpt-oss-120b) | [OpenAI](https://noometry.com/providers/openai) | 1.41 | high | [Epoch AI](https://epoch.ai/benchmarks) | | | 15 | [GPT-5 Mini](https://noometry.com/models/gpt-5-mini) | [OpenAI](https://noometry.com/providers/openai) | 1.38 | high | [Epoch AI](https://epoch.ai/benchmarks) | | | 16 | [Claude Opus 4.1](https://noometry.com/models/claude-opus-4-1) | [Anthropic](https://noometry.com/providers/anthropic) | 1.34 | | [Epoch AI](https://epoch.ai/benchmarks) | | | 17 | [Claude Opus 4](https://noometry.com/models/claude-opus-4) | [Anthropic](https://noometry.com/providers/anthropic) | 1.33 | | [Epoch AI](https://epoch.ai/benchmarks) | | | 18 | [GPT-5 Pro](https://noometry.com/models/gpt-5-pro) | [OpenAI](https://noometry.com/providers/openai) | 1.31 | medium | [Epoch AI](https://epoch.ai/benchmarks) | | ## Compare the leaders - [GPT-5.2 vs Gemini 3.1 Pro Preview](https://noometry.com/compare/gemini-3-1-pro-preview-vs-gpt-5-2) - [GPT-5.2 vs GPT-5.4](https://noometry.com/compare/gpt-5-2-vs-gpt-5-4) - [GPT-5.2 vs Gemini 3 Pro](https://noometry.com/compare/gemini-3-pro-vs-gpt-5-2) - [GPT-5.2 vs Claude Opus 4.5](https://noometry.com/compare/claude-opus-4-5-vs-gpt-5-2) - [Gemini 3.1 Pro Preview vs GPT-5.4](https://noometry.com/compare/gemini-3-1-pro-preview-vs-gpt-5-4) - [Gemini 3.1 Pro Preview vs Gemini 3 Pro](https://noometry.com/compare/gemini-3-1-pro-preview-vs-gemini-3-pro) ## 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 AlgoTune measure? Speeding up numerical and algorithmic Python functions, scored as average speed-up over reference code. ### Which model has the highest AlgoTune score? As of October 2026, GPT-5.2 has the highest published AlgoTune score on Noometry at 2.05, out of 18 models with results. ### What is the best open-weight model on AlgoTune? GLM-4.5 has the highest AlgoTune score among open-weight models at 1.52, ranking 10 of 18 overall. ### Cite this page Noometry. (2026). AlgoTune leaderboard. Retrieved October 10, 2026, from https://noometry.com/benchmarks/algotune Quote Noometry with a link back to this page. It is also available in [Markdown](https://noometry.com/md/benchmarks/algotune.md).