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FrontierMath Tiers 1–3: resultados de modelos de IA
FrontierMath Tiers 1–3 es un benchmark de Matemáticas ejecutado por Epoch AI. En nuestros datos hay resultados publicados de 86 modelos.
Forma parte del índice SI-IQ (carga α = 1,50, nivel de azar 0 %).
- Evaluador
- Epoch AI
- Página principal del benchmark
- epoch.ai/frontiermath
Resultados
| 1 | GPT-6 Astra OpenAI | 93,7 % | 150146–153 | 9 |
| 2 | GPT-6.1 Sol OpenAI | 93,7 % | 148144–152 | 9 |
| 3 | Claude Opus 5.5 Anthropic | 91,2 % | 144141–148 | 9 |
| 4 | Claude Fable 5.1 Anthropic | 90,2 % | 140136–143 | 8 |
| 5 | GPT-6 Sol OpenAI | 89,8 % | 140137–144 | 9 |
| 6 | GPT-5.6 Sol OpenAI | 89,1 % | 140137–143 | 9 |
| 7 | Claude Sonnet 5.5 Anthropic | 88,8 % | 140137–143 | 9 |
| 8 | GPT-5.5 Pro OpenAI | 87,7 % | 139135–142 | 5 datos insuficientes |
| 9 | Claude Fable 5 Anthropic | 87,0 % | 138134–141 | 9 |
| 10 | GPT-5.6 Terra OpenAI | 86,0 % | 134131–137 | 9 |
| 11 | Gemini 4 Argon Google DeepMind | 86,0 % | 140137–143 | 9 |
| 12 | Claude Opus 5 Anthropic | 85,6 % | 136133–140 | 9 |
| 13 | GPT-5.5 OpenAI | 85,3 % | 138135–141 | 10 |
| 14 | GPT-5.4 Pro OpenAI | 82,5 % | 135131–138 | 5 datos insuficientes |
| 15 | GPT-5.6 Luna OpenAI | 82,1 % | 130127–133 | 9 |
| 16 | Muse Wonder Meta AI | 82,1 % | 136133–139 | 4 datos insuficientes |
| 17 | Claude Opus 4.8 Anthropic | 80,0 % | 132129–135 | 9 |
| 18 | GPT-6 Luna OpenAI | 78,9 % | 130127–133 | 9 |
| 19 | GPT-5.4 OpenAI | 78,6 % | 131128–133 | 10 |
| 20 | Claude Haiku 5.5 Anthropic | 75,1 % | 129126–132 | 9 |
| 21 | Qwen 3.8 Max Alibaba | 74,7 % | 130127–133 | 7 datos insuficientes |
| 22 | Muse Spark 1.3 Meta AI | 74,4 % | 128125–131 | 5 datos insuficientes |
| 23 | GPT-5.2 Pro OpenAI | 74,0 % | 132128–135 | 3 datos insuficientes |
| 24 | Kimi K3abierto Moonshot | 72,2 % | 127124–130 | 9 |
| 25 | Gemini 3.7 Flash Google DeepMind | 71,6 % | 131128–134 | 9 |
| 26 | Claude Opus 4.7 Anthropic | 70,2 % | 127124–130 | 10 |
| 27 | Muse Spark Openabierto Meta AI | 69,8 % | 127124–130 | 7 |
| 28 | GLM-5.3abierto Z.ai (Zhipu AI) | 68,8 % | 125122–128 | 9 |
| 29 | Gemini 3.8 Flash Google DeepMind | 68,4 % | 130127–133 | 9 |
| 30 | GPT-5.2 OpenAI | 67,4 % | 125122–128 | 11 |
| 31 | DeepSeek V4.1 Flashabierto DeepSeek | 67,4 % | 128125–131 | 9 |
| 32 | Claude Opus 4.6 Anthropic | 66,0 % | 123120–126 | 10 |
| 33 | Grok 4.6 xAI | 66,0 % | 126123–129 | 9 |
| 34 | Claude Sonnet 5 Anthropic | 65,6 % | 126123–129 | 9 |
| 35 | Qwen3.8 Max (0902) Alibaba | 65,6 % | 127123–130 | 6 datos insuficientes |
| 36 | DeepSeek V4 Pro 0813abierto DeepSeek | 64,6 % | 128125–131 | 9 |
| 37 | Qwen3.7-Max Alibaba | 64,6 % | 125122–128 | 10 |
| 38 | Gemini 3.5 Flash Google DeepMind | 62,8 % | 126123–129 | 10 |
| 39 | Gemini 3.1 Pro Google DeepMind | 59,6 % | 127124–130 | 10 |
| 40 | GLM-5.2abierto Z.ai (Zhipu AI) | 59,2 % | 121118–124 | 10 |
| 41 | Gemini 3.6 Flash Google DeepMind | 58,9 % | 125122–128 | 9 |
| 42 | Mistral Large 4 Mistral AI | 58,6 % | 122119–125 | 7 datos insuficientes |
| 43 | DeepSeek V4 Flash 0731abierto DeepSeek | 57,5 % | 124121–127 | 9 |
| 44 | Grok 4.5 xAI | 57,2 % | 124121–127 | 6 datos insuficientes |
| 45 | Kimi K2.6abierto Moonshot | 57,2 % | 121118–124 | 10 |
| 46 | GLM-5.3-Flashabierto Z.ai (Zhipu AI) | 55,8 % | 120117–124 | 8 |
| 47 | GPT-5 Pro OpenAI | 55,8 % | 124120–128 | 3 datos insuficientes |
| 48 | GPT-5 OpenAI | 55,4 % | 121118–124 | 12 |
| 49 | Kimi K2.7 Codeabierto Moonshot | 54,0 % | 118115–122 | 8 |
| 50 | Grok 4.7 xAI | 53,0 % | 123120–127 | 7 datos insuficientes |
| 51 | GPT-5.4 Mini OpenAI | 51,2 % | 117113–120 | 9 |
| 52 | Gemini 3 Flash Google DeepMind | 51,2 % | 122119–125 | 10 |
| 53 | GPT-5 mini OpenAI | 46,7 % | 115112–118 | 12 |
| 54 | Inkling-Smallabierto Thinking Machines | 46,3 % | 118115–122 | 8 |
| 55 | DeepSeek-V4-Proabierto DeepSeek | 45,3 % | 117113–120 | 10 |
| 56 | GPT-5.4 Nano OpenAI | 44,9 % | 118115–121 | 9 |
| 57 | Grok 4.3 Beta xAI | 42,8 % | 117114–121 | 6 datos insuficientes |
| 58 | Qwen 3.6 Plus Alibaba | 38,2 % | 113109–117 | 7 datos insuficientes |
| 59 | GLM-5.1abierto Z.ai (Zhipu AI) | 36,8 % | 116112–120 | 8 |
| 60 | o4-mini OpenAI | 36,1 % | 112109–115 | 10 |
| 61 | Qwen3.6 27Babierto Alibaba | 35,1 % | 115110–119 | 6 |
| 62 | Claude Opus 4.5 Anthropic | 34,4 % | 115111–118 | 11 |
| 63 | Qwen3.7-Plus Alibaba | 34,4 % | 115110–119 | 6 |
| 64 | Inklingabierto Thinking Machines | 33,3 % | 114110–117 | 8 |
| 65 | o3 OpenAI | 33,3 % | 116113–119 | 10 |
| 66 | Qwen3.5 397B-A17Babierto Alibaba | 31,2 % | 114109–118 | 6 |
| 67 | Gemini 3.1 Flash-Lite Google | 27,7 % | 109105–114 | 3 datos insuficientes |
| 68 | GPT-5.5 Instant OpenAI | 26,3 % | 107103–111 | 4 datos insuficientes |
| 69 | Gemini 3.5 Flash-Lite Google DeepMind | 26,0 % | 109106–113 | 8 |
| 70 | Gemini 2.5 Pro (Jun 2025) Google DeepMind | 24,6 % | 108104–112 | 8 |
| 71 | Claude Sonnet 4.5 Anthropic | 23,9 % | 110107–113 | 12 |
| 72 | Qwen 3.6 Flash Alibaba | 22,5 % | 107103–111 | 6 datos insuficientes |
| 73 | Qwen 3.6 35B-A3Babierto Alibaba | 20,4 % | 111107–115 | 6 |
| 74 | GPT-5 nano OpenAI | 20,0 % | 105102–109 | 11 |
| 75 | Qwen3.7 Flash Alibaba | 19,3 % | 107103–112 | 4 datos insuficientes |
| 76 | Qwen3-Max Alibaba | 18,9 % | 106103–110 | 6 datos insuficientes |
| 77 | o3-mini OpenAI | 18,6 % | 105101–108 | 9 |
| 78 | Qwen 3.5 Flash (hosted 35B-A3B) Alibaba | 18,2 % | 107103–111 | 6 datos insuficientes |
| 79 | o1 OpenAI | 14,7 % | 10399–106 | 6 datos insuficientes |
| 80 | Claude Opus 4.1 Anthropic | 12,6 % | 105102–109 | 6 datos insuficientes |
| 81 | GPT-4.1 mini OpenAI | 6,7 % | 9188–95 | 11 |
| 82 | GPT-4.1 OpenAI | 6,0 % | 9187–94 | 11 |
| 83 | GPT-4 Turbo (Apr 2024) OpenAI | 0,7 % | 6864–73 | 4 datos insuficientes |
| 84 | GPT-4o mini OpenAI | 0,7 % | 6864–73 | 10 |
| 85 | GPT-4o (Aug 2024) OpenAI | 0,4 % | 7368–77 | 9 |
| 86 | GPT-3.5 Turbo (Jan 2024) OpenAI | 0,0 % | 4742–53 | 5 datos insuficientes |
Fuentes de datos: Aider polyglot leaderboard data (Apache-2.0); Arena leaderboard dataset on Hugging Face (CC BY 4.0); Berkeley Function Calling Leaderboard, Gorilla project (Apache-2.0); Epoch AI, ‘Capabilities & benchmarking’ (CC BY 4.0); LiteLLM, model_prices_and_context_window.json (MIT); Source: OpenRouter (openrouter.ai), licensed under CC BY 4.0: reuse and republish with attribution to OpenRouter (licencias y cambios).