Coding benchmark # Aider Polyglot leaderboard > Aider Polyglot results for 44 AI models, led by GPT-5 at 88%. What the benchmark measures, who runs it, and a source for every score. - Canonical page: https://noometry.com/benchmarks/aider-polyglot - Last updated: 2026-10-10 - Title: Aider Polyglot Leaderboard (October 2026): Scores by Model As of October 2026, GPT-5 has the highest published Aider Polyglot score on Noometry at 88%, out of 44 models with results. Last verified October 10, 2026 ## About Aider Polyglot 225 hard Exercism exercises in C++, Go, Java, JavaScript, Python and Rust, solved through the Aider editing workflow. - **Category:** [Coding](https://noometry.com/best/coding) - **Introduced:** 2024 - **Size:** 225 exercises - **Format:** Code edit - **Unit:** Percent (random guessing ≈ 0%) - **Official site:** [aider.chat](https://aider.chat/docs/leaderboards/) ## Top 15 models Top models on Aider Polyglot 1. GPT-5 88% 2. o3-pro 84.9% 3. Gemini 2.5 Pro 83.1% 4. o3 81.3% 5. Grok 4 79.6% 6. DeepSeek-V3.2-Exp 74.2% 7. Claude Opus 4 72% 8. o4-mini 72% 9. DeepSeek-R1 71.4% 10. Claude 3.7 Sonnet 64.9% 11. o1 61.7% 12. Claude Sonnet 4 61.3% 13. o3-mini 60.4% 14. Qwen3 235B-A22B 59.6% 15. Qwen3 235B-A22B 59.6% 16. 5060708090 Sponsored placements are available on pages like this one. [Advertise on Noometry](https://noometry.com/advertise) ## All results Aider Polyglot results by model | # | Model | Provider | Score | Setting | Source | Date | | --- | --- | --- | --- | --- | --- | --- | | 1 | [GPT-5](https://noometry.com/models/gpt-5) | [OpenAI](https://noometry.com/providers/openai) | 88% | high | [Epoch AI](https://epoch.ai/benchmarks) | | | 2 | [o3-pro](https://noometry.com/models/o3-pro) | [OpenAI](https://noometry.com/providers/openai) | 84.9% | high | [Epoch AI](https://epoch.ai/benchmarks) | | | 3 | [Gemini 2.5 Pro](https://noometry.com/models/gemini-2-5-pro) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 83.1% | 32K | [Epoch AI](https://epoch.ai/benchmarks) | | | 4 | [o3](https://noometry.com/models/o3) | [OpenAI](https://noometry.com/providers/openai) | 81.3% | high | [Epoch AI](https://epoch.ai/benchmarks) | | | 5 | [Grok 4](https://noometry.com/models/grok-4) | [xAI](https://noometry.com/providers/xai) | 79.6% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 6 | [DeepSeek-V3.2-Exp](https://noometry.com/models/deepseek-v3-2-exp) | [![](/logos/deepseek.svg) DeepSeek](https://noometry.com/providers/deepseek) | 74.2% | thinking | [Epoch AI](https://epoch.ai/benchmarks) | | | 7 | [Claude Opus 4](https://noometry.com/models/claude-opus-4) | [Anthropic](https://noometry.com/providers/anthropic) | 72% | 32K | [Epoch AI](https://epoch.ai/benchmarks) | | | 8 | [o4-mini](https://noometry.com/models/o4-mini) | [OpenAI](https://noometry.com/providers/openai) | 72% | high | [Epoch AI](https://epoch.ai/benchmarks) | | | 9 | [DeepSeek-R1](https://noometry.com/models/deepseek-r1) | [![](/logos/deepseek.svg) DeepSeek](https://noometry.com/providers/deepseek) | 71.4% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 10 | [Claude 3.7 Sonnet](https://noometry.com/models/claude-3-7-sonnet) | [Anthropic](https://noometry.com/providers/anthropic) | 64.9% | 32K | [Epoch AI](https://epoch.ai/benchmarks) | | | 11 | [o1](https://noometry.com/models/o1) | [OpenAI](https://noometry.com/providers/openai) | 61.7% | high | [Epoch AI](https://epoch.ai/benchmarks) | | | 12 | [Claude Sonnet 4](https://noometry.com/models/claude-sonnet-4) | [Anthropic](https://noometry.com/providers/anthropic) | 61.3% | 32K | [Epoch AI](https://epoch.ai/benchmarks) | | | 13 | [o3-mini](https://noometry.com/models/o3-mini) | [OpenAI](https://noometry.com/providers/openai) | 60.4% | high | [Epoch AI](https://epoch.ai/benchmarks) | | | 14 | [Qwen3 235B-A22B](https://noometry.com/models/qwen3-235b-a22b) | [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 59.6% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 15 | [Qwen3 235B-A22B](https://noometry.com/models/qwen3-235b-a22b) | [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 59.6% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 16 | [Kimi K2 (Jul 2025)](https://noometry.com/models/kimi-k2) | [Moonshot AI](https://noometry.com/providers/moonshot) | 59.1% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 17 | [Kimi K2 (Jul 2025)](https://noometry.com/models/kimi-k2) | [Moonshot AI](https://noometry.com/providers/moonshot) | 59.1% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 18 | [DeepSeek-V3](https://noometry.com/models/deepseek-v3) | [![](/logos/deepseek.svg) DeepSeek](https://noometry.com/providers/deepseek) | 55.1% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 19 | [Gemini 2.5 Flash](https://noometry.com/models/gemini-2-5-flash) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 55.1% | 23K | [Epoch AI](https://epoch.ai/benchmarks) | | | 20 | [Grok 3](https://noometry.com/models/grok-3) | [xAI](https://noometry.com/providers/xai) | 53.3% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 21 | [GPT-4.1](https://noometry.com/models/gpt-4-1) | [OpenAI](https://noometry.com/providers/openai) | 52.4% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 22 | [Claude 3.5 Sonnet](https://noometry.com/models/claude-3-5-sonnet) | [Anthropic](https://noometry.com/providers/anthropic) | 51.6% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 23 | [Grok-3 mini](https://noometry.com/models/grok-3-mini) | [xAI](https://noometry.com/providers/xai) | 49.3% | high | [Epoch AI](https://epoch.ai/benchmarks) | | | 24 | [GPT-4o](https://noometry.com/models/gpt-4o) | [OpenAI](https://noometry.com/providers/openai) | 45.3% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 25 | [GPT-4.5](https://noometry.com/models/gpt-4-5) | [OpenAI](https://noometry.com/providers/openai) | 44.9% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 26 | [gpt-oss-120b](https://noometry.com/models/gpt-oss-120b) | [OpenAI](https://noometry.com/providers/openai) | 41.8% | high | [Epoch AI](https://epoch.ai/benchmarks) | | | 27 | [gpt-oss-120b](https://noometry.com/models/gpt-oss-120b) | [OpenAI](https://noometry.com/providers/openai) | 41.8% | high | [Epoch AI](https://epoch.ai/benchmarks) | | | 28 | [Qwen3 32B](https://noometry.com/models/qwen3-32b) | [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 40% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 29 | [Gemini 2.0 Flash (Feb 2025)](https://noometry.com/models/gemini-2-0-flash) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 38.2% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 30 | [Gemini 2.0 Pro](https://noometry.com/models/gemini-2-0-pro) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 35.6% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 31 | [o1-mini](https://noometry.com/models/o1-mini) | [OpenAI](https://noometry.com/providers/openai) | 32.9% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 32 | [GPT-4.1 mini](https://noometry.com/models/gpt-4-1-mini) | [OpenAI](https://noometry.com/providers/openai) | 32.4% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 33 | [Claude 3.5 Haiku](https://noometry.com/models/claude-3-5-haiku) | [Anthropic](https://noometry.com/providers/anthropic) | 28% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 34 | [Qwen Max](https://noometry.com/models/qwen-max) | [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 21.8% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 35 | [QwQ-32B](https://noometry.com/models/qwq-32b) | [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 20.9% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 36 | [DeepSeek-V2.5 (Sep 2024)](https://noometry.com/models/deepseek-v2-5) | [![](/logos/deepseek.svg) DeepSeek](https://noometry.com/providers/deepseek) | 17.8% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 37 | [Qwen2.5-Coder-32B](https://noometry.com/models/qwen2-5-coder-32b) | [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 16.4% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 38 | [Llama 4 Maverick](https://noometry.com/models/llama-4-maverick) | [![](/logos/meta.svg) Meta](https://noometry.com/providers/meta) | 15.6% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 39 | [Yi-Lightning](https://noometry.com/models/yi-lightning) | [01.AI](https://noometry.com/providers/01-ai) | 12.9% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 40 | [Command A](https://noometry.com/models/command-a) | [![](/logos/cohere.svg) Cohere](https://noometry.com/providers/cohere) | 12% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 41 | [Codestral](https://noometry.com/models/codestral) | [![](/logos/mistral.svg) Mistral AI](https://noometry.com/providers/mistral) | 11.1% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 42 | [GPT-4.1 nano](https://noometry.com/models/gpt-4-1-nano) | [OpenAI](https://noometry.com/providers/openai) | 8.9% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 43 | [Gemma 3 27B](https://noometry.com/models/gemma-3-27b) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 4.9% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 44 | [GPT-4o mini](https://noometry.com/models/gpt-4o-mini) | [OpenAI](https://noometry.com/providers/openai) | 3.6% | | [Epoch AI](https://epoch.ai/benchmarks) | | ## Compare the leaders - [GPT-5 vs o3-pro](https://noometry.com/compare/gpt-5-vs-o3-pro) - [GPT-5 vs Gemini 2.5 Pro](https://noometry.com/compare/gemini-2-5-pro-vs-gpt-5) - [GPT-5 vs o3](https://noometry.com/compare/gpt-5-vs-o3) - [GPT-5 vs Grok 4](https://noometry.com/compare/gpt-5-vs-grok-4) - [o3-pro vs Gemini 2.5 Pro](https://noometry.com/compare/gemini-2-5-pro-vs-o3-pro) - [o3-pro vs o3](https://noometry.com/compare/o3-vs-o3-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) - [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 Aider Polyglot measure? 225 hard Exercism exercises in C++, Go, Java, JavaScript, Python and Rust, solved through the Aider editing workflow. ### Which model has the highest Aider Polyglot score? As of October 2026, GPT-5 has the highest published Aider Polyglot score on Noometry at 88%, out of 44 models with results. ### What is the best open-weight model on Aider Polyglot? DeepSeek-V3.2-Exp has the highest Aider Polyglot accuracy among open-weight models at 74.2%, ranking 6 of 44 overall. ### Cite this page Noometry. (2026). Aider Polyglot leaderboard. Retrieved October 10, 2026, from https://noometry.com/benchmarks/aider-polyglot Quote Noometry with a link back to this page. It is also available in [Markdown](https://noometry.com/md/benchmarks/aider-polyglot.md).