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

Context
1M tokens
Completion cap
40,000
Tools
Yes
JSON
No
Released
2025-06-17

Call it from Geek Hub

Same OpenAI SDK. Change the base URL and the model id.

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"}]
  }'
Get an API key

Frequently asked questions

What is 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 runs on the Geek Hub API (OpenAI-compatible). Model id: minimax/minimax-m1.
Is MiniMax M1 free?
No. Input is $0.5995 per 1M tokens and output is $2.3981 per 1M tokens on Geek Hub (markup included).
What is the context length of MiniMax M1?
MiniMax M1 has a 1M tokens context window. It supports up to 40,000 completion tokens.
Does MiniMax M1 support tool calling and structured outputs?
MiniMax M1 accepts tools and tool_choice for function calling. Structured outputs are not listed for this endpoint.
When was MiniMax M1 released?
MiniMax M1 was released on 2025-06-17.

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