MiniMax M1

MiniMax-M1 is a large-scale, open-weight reasoning model designed for extended context and high-efficiency inference. It leverages a hybrid Mixture-of-Experts (MoE) architecture paired with a custom "lightning attention" mechanism, allowing it to process long sequences—up to 1 million tokens—while maintaining competitive FLOP efficiency. With 456 billion total parameters and 45.9B active per token, this variant is optimized for complex, multi-step reasoning tasks. Trained via a custom reinforcement learning pipeline (CISPO), M1 excels in long-context understanding, software engineering, agentic tool use, and mathematical reasoning. Benchmarks show strong performance across FullStackBench, SWE-bench, MATH, GPQA, and TAU-Bench, often outperforming other open models like DeepSeek R1 and Qwen3-235B.

ChatMiniMax1M tokens$0.5995 / $2.3981 · 1M

minimax/minimax-m1

Contexto
1M tokens
Máx. completion
40,000
Tools
Sí
JSON
No
Lanzamiento
2025-06-17

Llámalo desde Geek Hub

El mismo SDK de OpenAI. Cambia el base URL y el id del modelo.

curl https://api.geekhub.mx/v1/chat/completions \
  -H "Authorization: Bearer $GEEKHUB_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "minimax/minimax-m1",
    "messages": [{"role": "user", "content": "Hola"}]
  }'
Consigue tu API key

Preguntas frecuentes

¿Qué es MiniMax M1?
MiniMax-M1 is a large-scale, open-weight reasoning model designed for extended context and high-efficiency inference. It leverages a hybrid Mixture-of-Experts (MoE) architecture paired with a custom "lightning attention" mechanism, allowing it to process long sequences—up to 1 million tokens—while maintaining competitive FLOP efficiency. With 456 billion total parameters and 45.9B active per token, this variant is optimized for complex, multi-step reasoning tasks. Trained via a custom reinforcement learning pipeline (CISPO), M1 excels in long-context understanding, software engineering, agentic tool use, and mathematical reasoning. Benchmarks show strong performance across FullStackBench, SWE-bench, MATH, GPQA, and TAU-Bench, often outperforming other open models like DeepSeek R1 and Qwen3-235B. MiniMax M1 corre en el API de Geek Hub (compatible con OpenAI). Id: minimax/minimax-m1.
¿MiniMax M1 es gratis?
No. El input cuesta $0.5995 / 1M tokens y el output $2.3981 / 1M tokens en Geek Hub (markup incluido).
¿Cuál es el contexto de MiniMax M1?
MiniMax M1 tiene una ventana de 1M tokens. Soporta hasta 40,000 tokens de completion.
¿MiniMax M1 soporta tool calling y structured outputs?
MiniMax M1 acepta tools y tool_choice para function calling. No lista structured outputs en este endpoint.
¿Cuándo se lanzó MiniMax M1?
MiniMax M1 se lanzó el 17 de junio de 2025.

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