Benchmarks de Modelos IA
Estándar: 36 · HF: 22
Sobre los benchmarks
Los benchmarks son pruebas estandarizadas que puntúan el rendimiento de los modelos de IA en razonamiento, conocimiento, matemáticas y programación. Úsalos para comparar modelos de forma objetiva y elegir el adecuado para tu tarea.
📊 Estándar. Estándar - los cuatro benchmarks públicos más citados (MMLU, GPQA, HumanEval, SWE-Bench), extraídos de la página de anuncio de cada modelo; nuestra Puntuación los combina en un solo número.
🤗 HF Open LLM Leaderboard. HF Open LLM Leaderboard - seis tareas (IFEval, BBH, MATH, GPQA, MuSR, MMLU-Pro) medidas de forma uniforme para modelos open source; ordenados por la Media.
📚MMLU
57 materias académicas
🔬GPQA Diamond
Preguntas científicas de nivel PhD
💻HumanEval
Generación de código Python
🔧SWE-Bench
Tareas reales de GitHub
| # | Modelo↕ | Proveedor↕ | MMLU↕ | GPQA↕ | HumanEval↕ | SWE-Bench↕ | Puntuación↑ |
|---|---|---|---|---|---|---|---|
| 1 | Meta | 63.4 | 24.7 | 58.3 | 9.5 | 37.0 | |
| 2 | Mistral AI | 68.0 | 32.0 | 73.4 | 15.0 | 45.3 | |
| 3 | Cohere | 80.4 | 30.1 | 74.2 | 16.8 | 47.9 | |
| 4 | 75.2 | 38.4 | 74.0 | 14.7 | 48.7 | ||
| 5 | 78.9 | 37.0 | 78.9 | 16.2 | 50.7 | ||
| 6 | OpenAI | 82.0 | 40.1 | 87.1 | 22.8 | 55.9 | |
| 7 | Meta | 83.6 | 46.7 | 80.5 | 21.8 | 56.3 | |
| 8 | 83.0 | 45.0 | 85.0 | 22.0 | 56.9 | ||
| 9 | 85.9 | 46.2 | 84.1 | 26.9 | 58.8 | ||
| 10 | Alibaba/Qwen | 80.0 | 42.0 | 92.3 | 30.0 | 59.2 | |
| 11 | Alibaba/Qwen | 86.1 | 49.0 | 86.5 | 23.7 | 59.5 | |
| 12 | Anthropic | 82.9 | 43.0 | 88.3 | 33.2 | 59.9 | |
| 13 | Mistral AI | 84.0 | 47.2 | 92.1 | 32.6 | 62.1 | |
| 14 | Sber | 68.0 | - | 60.0 | - | 63.6 | |
| 15 | Meta | 88.6 | 50.7 | 89.0 | 34.1 | 63.7 | |
| 16 | Yandex | 69.0 | - | 62.0 | - | 65.1 | |
| 17 | HyperCLOVA X | Naver | 79.0 | - | 55.0 | - | 65.7 |
| 18 | OpenAI | 88.7 | 53.6 | 90.2 | 38.3 | 65.9 | |
| 19 | Alibaba | 89.0 | 59.0 | 87.0 | 37.0 | 66.5 | |
| 20 | Yandex | 72.0 | - | 65.0 | - | 68.1 | |
| 21 | DeepSeek | 88.5 | 59.1 | 89.4 | 42.0 | 68.3 | |
| 22 | Sber | 74.0 | - | 68.0 | - | 70.7 | |
| 23 | Baidu | 88.0 | 55.0 | 82.0 | - | 72.8 | |
| 24 | OpenAI | 86.9 | 67.4 | 91.7 | 49.3 | 72.9 | |
| 25 | MiniMax Text-01 | MiniMax | 88.0 | 56.0 | 84.0 | - | 73.9 |
| 26 | Anthropic | 88.3 | 65.0 | 92.0 | 57.0 | 74.4 | |
| 27 | Yandex | 78.0 | - | 72.0 | - | 74.7 | |
| 28 | DeepSeek | 90.8 | 71.5 | 92.6 | 49.2 | 75.1 | |
| 29 | OpenAI | 92.3 | 77.3 | 92.4 | 48.9 | 77.0 | |
| 30 | Tencent | 81.0 | - | 74.0 | - | 77.1 | |
| 31 | ByteDance | 82.0 | - | 75.0 | - | 78.1 | |
| 32 | Baidu | 82.0 | - | 76.0 | - | 78.7 | |
| 33 | Zhipu AI | 83.0 | - | 76.0 | - | 79.1 | |
| 34 | Moonshot AI | 83.0 | - | 77.0 | - | 79.7 | |
| 35 | Anthropic | 90.1 | 74.3 | 92.1 | 72.5 | 81.5 | |
| 36 | Anthropic | - | - | - | 74.5 | - |
Score = MMLU×20% + GPQA×30% + HumanEval×25% + SWE-Bench×25%Todas las puntuaciones en % · mayor es mejor→ Tabla completa de modelos con precios y velocidad

