Reasoning benchmark # ARC-AGI-1 leaderboard > ARC-AGI-1 results for 83 AI models, led by Claude Fable 5 at 98.5%. What the benchmark measures, who runs it, and a source for every score. - Canonical page: https://noometry.com/benchmarks/arc-agi-1 - Last updated: 2026-10-10 - Title: ARC-AGI-1 Leaderboard (October 2026): Scores by Model As of October 2026, Claude Fable 5 has the highest published ARC-AGI-1 score on Noometry at 98.5%, out of 83 models with results. Last verified October 10, 2026 ## About ARC-AGI-1 Abstract grid puzzles: infer a transformation rule from a few examples and apply it to a new grid. - **Category:** [Reasoning](https://noometry.com/best/reasoning) - **Introduced:** 2019 - **Format:** Grid puzzles - **Unit:** Percent (random guessing ≈ 0%) - **Official site:** [arcprize.org](https://arcprize.org) ## Top 15 models Top models on ARC-AGI-1 1. Claude Fable 5 98.5% 2. Claude Opus 5.5 98.5% 3. Gemini 3.8 Flash 98.5% 4. GPT-6.1 Sol 98.5% 5. GPT-6 Astra 98.5% 6. Gemini 3.1 Pro Preview 98% 7. Claude Fable 5.1 97.5% 8. Claude Opus 5 97.5% 9. GPT-5.6 Sol 97.5% 10. GPT-5.5 Pro 96.5% 11. GPT-5.6 Terra 96.5% 12. Gemini 3 Deep Think 96% 13. Gemini 3.7 Flash 95.5% 14. GPT-6 Sol 95.5% 15. GPT-5.5 95% 16. 949596979899 Sponsored placements are available on pages like this one. [Advertise on Noometry](https://noometry.com/advertise) ## All results ARC-AGI-1 results by model | # | Model | Provider | Score | Setting | Source | Date | | --- | --- | --- | --- | --- | --- | --- | | 1 | [Claude Fable 5](https://noometry.com/models/claude-fable-5) | [Anthropic](https://noometry.com/providers/anthropic) | 98.5% | max | [Epoch AI](https://epoch.ai/benchmarks) | | | 2 | [Claude Opus 5.5](https://noometry.com/models/claude-opus-5-5) | [Anthropic](https://noometry.com/providers/anthropic) | 98.5% | high | [Epoch AI](https://epoch.ai/benchmarks) | | | 3 | [Gemini 3.8 Flash](https://noometry.com/models/gemini-3-8-flash) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 98.5% | high | [Epoch AI](https://epoch.ai/benchmarks) | | | 4 | [GPT-6.1 Sol](https://noometry.com/models/gpt-6-1-sol) | [OpenAI](https://noometry.com/providers/openai) | 98.5% | high | [Epoch AI](https://epoch.ai/benchmarks) | | | 5 | [GPT-6 Astra](https://noometry.com/models/gpt-6-astra) | [OpenAI](https://noometry.com/providers/openai) | 98.5% | high | [Epoch AI](https://epoch.ai/benchmarks) | | | 6 | [Gemini 3.1 Pro Preview](https://noometry.com/models/gemini-3-1-pro-preview) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 98% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 7 | [Claude Fable 5.1](https://noometry.com/models/claude-fable-5-1) | [Anthropic](https://noometry.com/providers/anthropic) | 97.5% | max | [Epoch AI](https://epoch.ai/benchmarks) | | | 8 | [Claude Opus 5](https://noometry.com/models/claude-opus-5) | [Anthropic](https://noometry.com/providers/anthropic) | 97.5% | high | [Epoch AI](https://epoch.ai/benchmarks) | | | 9 | [GPT-5.6 Sol](https://noometry.com/models/gpt-5-6-sol) | [OpenAI](https://noometry.com/providers/openai) | 97.5% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | | | 10 | [GPT-5.5 Pro](https://noometry.com/models/gpt-5-5-pro) | [OpenAI](https://noometry.com/providers/openai) | 96.5% | high | [Epoch AI](https://epoch.ai/benchmarks) | | | 11 | [GPT-5.6 Terra](https://noometry.com/models/gpt-5-6-terra) | [OpenAI](https://noometry.com/providers/openai) | 96.5% | max | [Epoch AI](https://epoch.ai/benchmarks) | | | 12 | [Gemini 3 Deep Think](https://noometry.com/models/gemini-3-deep-think) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 96% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 13 | [Gemini 3.7 Flash](https://noometry.com/models/gemini-3-7-flash) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 95.5% | high | [Epoch AI](https://epoch.ai/benchmarks) | | | 14 | [GPT-6 Sol](https://noometry.com/models/gpt-6-sol) | [OpenAI](https://noometry.com/providers/openai) | 95.5% | max | [Epoch AI](https://epoch.ai/benchmarks) | | | 15 | [GPT-5.5](https://noometry.com/models/gpt-5-5) | [OpenAI](https://noometry.com/providers/openai) | 95% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | | | 16 | [GPT-5.4 Pro](https://noometry.com/models/gpt-5-4-pro) | [OpenAI](https://noometry.com/providers/openai) | 94.5% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | | | 17 | [Kimi K3](https://noometry.com/models/kimi-k3) | [Moonshot AI](https://noometry.com/providers/moonshot) | 94.5% | max | [Epoch AI](https://epoch.ai/benchmarks) | | | 18 | [Claude Opus 4.6](https://noometry.com/models/claude-opus-4-6) | [Anthropic](https://noometry.com/providers/anthropic) | 94% | 120K | [Epoch AI](https://epoch.ai/benchmarks) | | | 19 | [GPT-5.4](https://noometry.com/models/gpt-5-4) | [OpenAI](https://noometry.com/providers/openai) | 93.7% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | | | 20 | [Claude Opus 4.7](https://noometry.com/models/claude-opus-4-7) | [Anthropic](https://noometry.com/providers/anthropic) | 93.5% | high | [Epoch AI](https://epoch.ai/benchmarks) | | | 21 | [Claude Opus 4.8](https://noometry.com/models/claude-opus-4-8) | [Anthropic](https://noometry.com/providers/anthropic) | 92.5% | max | [Epoch AI](https://epoch.ai/benchmarks) | | | 22 | [Gemini 3.5 Flash](https://noometry.com/models/gemini-3-5-flash) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 92.5% | high | [Epoch AI](https://epoch.ai/benchmarks) | | | 23 | [Gemini 3.6 Flash](https://noometry.com/models/gemini-3-6-flash) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 91.2% | high | [Epoch AI](https://epoch.ai/benchmarks) | | | 24 | [GLM-5.3-Flash](https://noometry.com/models/glm-5-3-flash) | [Z.ai (Zhipu)](https://noometry.com/providers/zai) | 91% | max | [Epoch AI](https://epoch.ai/benchmarks) | | | 25 | [DeepSeek V4 Pro](https://noometry.com/models/deepseek-v4-pro) | [![](/logos/deepseek.svg) DeepSeek](https://noometry.com/providers/deepseek) | 90.5% | low | [Epoch AI](https://epoch.ai/benchmarks) | | | 26 | [GPT-5.2 Pro](https://noometry.com/models/gpt-5-2-pro) | [OpenAI](https://noometry.com/providers/openai) | 90.5% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | | | 27 | [Grok 4.20 (Non-Reasoning)](https://noometry.com/models/grok-4-20) | [xAI](https://noometry.com/providers/xai) | 89.5% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 28 | [DeepSeek V4 Flash](https://noometry.com/models/deepseek-v4-flash) | [![](/logos/deepseek.svg) DeepSeek](https://noometry.com/providers/deepseek) | 89% | max | [Epoch AI](https://epoch.ai/benchmarks) | | | 29 | [GPT-5.6 Luna](https://noometry.com/models/gpt-5-6-luna) | [OpenAI](https://noometry.com/providers/openai) | 88% | max | [Epoch AI](https://epoch.ai/benchmarks) | | | 30 | [Grok 4.6](https://noometry.com/models/grok-4-6) | [xAI](https://noometry.com/providers/xai) | 87.5% | medium | [Epoch AI](https://epoch.ai/benchmarks) | | | 31 | [Qwen3.8 27B](https://noometry.com/models/qwen3-8-27b) | [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 87.5% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | | | 32 | [Grok 4.5](https://noometry.com/models/grok-4-5) | [xAI](https://noometry.com/providers/xai) | 87.2% | medium | [Epoch AI](https://epoch.ai/benchmarks) | | | 33 | [GPT-6 Luna](https://noometry.com/models/gpt-6-luna) | [OpenAI](https://noometry.com/providers/openai) | 86.7% | max | [Epoch AI](https://epoch.ai/benchmarks) | | | 34 | [Claude Sonnet 4.6](https://noometry.com/models/claude-sonnet-4-6) | [Anthropic](https://noometry.com/providers/anthropic) | 86.5% | high | [Epoch AI](https://epoch.ai/benchmarks) | | | 35 | [GPT-5.2](https://noometry.com/models/gpt-5-2) | [OpenAI](https://noometry.com/providers/openai) | 86.2% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | | | 36 | [Gemini 3 Flash Preview](https://noometry.com/models/gemini-3-flash-preview) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 84.7% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 37 | [Inkling-Small](https://noometry.com/models/inkling-small) | [T Thinking Machines Lab](https://noometry.com/providers/thinking-machines) | 84% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | | | 38 | [Claude Opus 4.5](https://noometry.com/models/claude-opus-4-5) | [Anthropic](https://noometry.com/providers/anthropic) | 80% | 64K | [Epoch AI](https://epoch.ai/benchmarks) | | | 39 | [Inkling](https://noometry.com/models/inkling) | [T Thinking Machines Lab](https://noometry.com/providers/thinking-machines) | 79.5% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 40 | [GLM-5.2](https://noometry.com/models/glm-5-2) | [Z.ai (Zhipu)](https://noometry.com/providers/zai) | 77% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 41 | [Gemini 3 Pro](https://noometry.com/models/gemini-3-pro) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 75% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 42 | [GPT-5.1](https://noometry.com/models/gpt-5-1) | [OpenAI](https://noometry.com/providers/openai) | 72.8% | high | [Epoch AI](https://epoch.ai/benchmarks) | | | 43 | [GPT-5 Pro](https://noometry.com/models/gpt-5-pro) | [OpenAI](https://noometry.com/providers/openai) | 70.2% | high | [Epoch AI](https://epoch.ai/benchmarks) | | | 44 | [Grok 4](https://noometry.com/models/grok-4) | [xAI](https://noometry.com/providers/xai) | 66.7% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 45 | [GPT-5](https://noometry.com/models/gpt-5) | [OpenAI](https://noometry.com/providers/openai) | 65.7% | high | [Epoch AI](https://epoch.ai/benchmarks) | | | 46 | [Kimi K2.5](https://noometry.com/models/kimi-k2-5) | [Moonshot AI](https://noometry.com/providers/moonshot) | 65.3% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 47 | [Claude Sonnet 4.5](https://noometry.com/models/claude-sonnet-4-5) | [Anthropic](https://noometry.com/providers/anthropic) | 63.7% | 32K | [Epoch AI](https://epoch.ai/benchmarks) | | | 48 | [GPT-5.4 mini](https://noometry.com/models/gpt-5-4-mini) | [OpenAI](https://noometry.com/providers/openai) | 63.7% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | | | 49 | [MiniMax-M2.5](https://noometry.com/models/minimax-m2-5) | [![](/logos/minimax.svg) MiniMax](https://noometry.com/providers/minimax) | 63.7% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 50 | [o3](https://noometry.com/models/o3) | [OpenAI](https://noometry.com/providers/openai) | 60.8% | high | [Epoch AI](https://epoch.ai/benchmarks) | | | 51 | [o3-pro](https://noometry.com/models/o3-pro) | [OpenAI](https://noometry.com/providers/openai) | 59.3% | high | [Epoch AI](https://epoch.ai/benchmarks) | | | 52 | [o4-mini](https://noometry.com/models/o4-mini) | [OpenAI](https://noometry.com/providers/openai) | 58.7% | high | [Epoch AI](https://epoch.ai/benchmarks) | | | 53 | [DeepSeek-V3.2-Exp](https://noometry.com/models/deepseek-v3-2-exp) | [![](/logos/deepseek.svg) DeepSeek](https://noometry.com/providers/deepseek) | 57% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 54 | [GPT-5 Mini](https://noometry.com/models/gpt-5-mini) | [OpenAI](https://noometry.com/providers/openai) | 54.3% | high | [Epoch AI](https://epoch.ai/benchmarks) | | | 55 | [Gemini 3.5 Flash Lite](https://noometry.com/models/gemini-3-5-flash-lite) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 53.5% | high | [Epoch AI](https://epoch.ai/benchmarks) | | | 56 | [GPT-5.4 nano](https://noometry.com/models/gpt-5-4-nano) | [OpenAI](https://noometry.com/providers/openai) | 51.5% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | | | 57 | [Grok 4 Fast](https://noometry.com/models/grok-4-fast) | [xAI](https://noometry.com/providers/xai) | 48.5% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 58 | [Claude Haiku 4.5](https://noometry.com/models/claude-haiku-4-5) | [Anthropic](https://noometry.com/providers/anthropic) | 47.7% | 32K | [Epoch AI](https://epoch.ai/benchmarks) | | | 59 | [GLM-5](https://noometry.com/models/glm-5) | [Z.ai (Zhipu)](https://noometry.com/providers/zai) | 44.7% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 60 | [Gemini 2.5 Pro](https://noometry.com/models/gemini-2-5-pro) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 41% | 16K | [Epoch AI](https://epoch.ai/benchmarks) | | | 61 | [Claude Sonnet 4](https://noometry.com/models/claude-sonnet-4) | [Anthropic](https://noometry.com/providers/anthropic) | 40% | 16K | [Epoch AI](https://epoch.ai/benchmarks) | | | 62 | [Claude Opus 4](https://noometry.com/models/claude-opus-4) | [Anthropic](https://noometry.com/providers/anthropic) | 35.7% | 16K | [Epoch AI](https://epoch.ai/benchmarks) | | | 63 | [o3-mini](https://noometry.com/models/o3-mini) | [OpenAI](https://noometry.com/providers/openai) | 34.5% | high | [Epoch AI](https://epoch.ai/benchmarks) | | | 64 | [Gemini 2.5 Flash](https://noometry.com/models/gemini-2-5-flash) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 33.3% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 65 | [o1](https://noometry.com/models/o1) | [OpenAI](https://noometry.com/providers/openai) | 30.7% | medium | [Epoch AI](https://epoch.ai/benchmarks) | | | 66 | [Claude 3.7 Sonnet](https://noometry.com/models/claude-3-7-sonnet) | [Anthropic](https://noometry.com/providers/anthropic) | 28.6% | 16K | [Epoch AI](https://epoch.ai/benchmarks) | | | 67 | [o1-pro](https://noometry.com/models/o1-pro) | [OpenAI](https://noometry.com/providers/openai) | 23.3% | low | [Epoch AI](https://epoch.ai/benchmarks) | | | 68 | [DeepSeek-R1](https://noometry.com/models/deepseek-r1) | [![](/logos/deepseek.svg) DeepSeek](https://noometry.com/providers/deepseek) | 21.2% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 69 | [GPT-5 Nano](https://noometry.com/models/gpt-5-nano) | [OpenAI](https://noometry.com/providers/openai) | 20.7% | medium | [Epoch AI](https://epoch.ai/benchmarks) | | | 70 | [Grok-3 mini](https://noometry.com/models/grok-3-mini) | [xAI](https://noometry.com/providers/xai) | 16.5% | low | [Epoch AI](https://epoch.ai/benchmarks) | | | 71 | [Grok-3 mini](https://noometry.com/models/grok-3-mini) | [xAI](https://noometry.com/providers/xai) | 16.5% | low | [Epoch AI](https://epoch.ai/benchmarks) | | | 72 | [o1-mini](https://noometry.com/models/o1-mini) | [OpenAI](https://noometry.com/providers/openai) | 14% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 73 | [Qwen3 235B-A22B](https://noometry.com/models/qwen3-235b-a22b) | [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 11% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 74 | [GPT-4.5](https://noometry.com/models/gpt-4-5) | [OpenAI](https://noometry.com/providers/openai) | 10.3% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 75 | [Magistral Medium](https://noometry.com/models/magistral-medium) | [![](/logos/mistral.svg) Mistral AI](https://noometry.com/providers/mistral) | 6.1% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 76 | [GPT-4.1](https://noometry.com/models/gpt-4-1) | [OpenAI](https://noometry.com/providers/openai) | 5.5% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 77 | [Grok 3](https://noometry.com/models/grok-3) | [xAI](https://noometry.com/providers/xai) | 5.5% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 78 | [Magistral Small](https://noometry.com/models/magistral-small) | [![](/logos/mistral.svg) Mistral AI](https://noometry.com/providers/mistral) | 5% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 79 | [GPT-4o](https://noometry.com/models/gpt-4o) | [OpenAI](https://noometry.com/providers/openai) | 4.5% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 80 | [Llama 4 Maverick](https://noometry.com/models/llama-4-maverick) | [![](/logos/meta.svg) Meta](https://noometry.com/providers/meta) | 4.4% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 81 | [GPT-4.1 mini](https://noometry.com/models/gpt-4-1-mini) | [OpenAI](https://noometry.com/providers/openai) | 3.5% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 82 | [Llama 4 Scout](https://noometry.com/models/llama-4-scout) | [![](/logos/meta.svg) Meta](https://noometry.com/providers/meta) | 0.5% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 83 | [GPT-4.1 nano](https://noometry.com/models/gpt-4-1-nano) | [OpenAI](https://noometry.com/providers/openai) | 0% | | [Epoch AI](https://epoch.ai/benchmarks) | | ## Compare the leaders - [Claude Fable 5 vs Claude Opus 5.5](https://noometry.com/compare/claude-fable-5-vs-claude-opus-5-5) - [Claude Fable 5 vs Gemini 3.8 Flash](https://noometry.com/compare/claude-fable-5-vs-gemini-3-8-flash) - [Claude Fable 5 vs GPT-6.1 Sol](https://noometry.com/compare/claude-fable-5-vs-gpt-6-1-sol) - [Claude Fable 5 vs GPT-6 Astra](https://noometry.com/compare/claude-fable-5-vs-gpt-6-astra) - [Claude Opus 5.5 vs Gemini 3.8 Flash](https://noometry.com/compare/claude-opus-5-5-vs-gemini-3-8-flash) - [Claude Opus 5.5 vs GPT-6.1 Sol](https://noometry.com/compare/claude-opus-5-5-vs-gpt-6-1-sol) ## Other reasoning benchmarks - [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) - [SimpleBench](https://noometry.com/benchmarks/simplebench) - [Kagi LLM Benchmark](https://noometry.com/benchmarks/kagi-reasoning) - [NYT Connections (extended)](https://noometry.com/benchmarks/nyt-connections) - [CritPt](https://noometry.com/benchmarks/critpt) - [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) - [EnigmaEval](https://noometry.com/benchmarks/enigmaeval) - [Thematic Generalization](https://noometry.com/benchmarks/thematic-generalization) - [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) - [EBR-Bench](https://noometry.com/benchmarks/ebr-bench) - [LiveBench Reasoning](https://noometry.com/benchmarks/livebench-reasoning) - [Mystery Game Puzzles](https://noometry.com/benchmarks/mystery-game-puzzles) ## Frequently asked questions ### What does ARC-AGI-1 measure? Abstract grid puzzles: infer a transformation rule from a few examples and apply it to a new grid. ### Which model has the highest ARC-AGI-1 score? As of October 2026, Claude Fable 5 has the highest published ARC-AGI-1 score on Noometry at 98.5%, out of 83 models with results. ### What is the best open-weight model on ARC-AGI-1? Kimi K3 has the highest ARC-AGI-1 accuracy among open-weight models at 94.5%, ranking 17 of 83 overall. ### Cite this page Noometry. (2026). ARC-AGI-1 leaderboard. Retrieved October 10, 2026, from https://noometry.com/benchmarks/arc-agi-1 Quote Noometry with a link back to this page. It is also available in [Markdown](https://noometry.com/md/benchmarks/arc-agi-1.md).