Experience MiniMax-M1 Online

Meet the world's first open-weight hybrid-attention reasoning model. Harness the power of 1M token context for complex problem-solving and deep analysis. No registration, unlimited free access.

Try MiniMax-M1 Free Open Source Code
Minimax M1

Try MiniMax-M1 Online - 100% Free

Unleash the power of next-generation AI reasoning without any cost or registration.

Online Demo

Interact with MiniMax-M1 directly in your browser with our full-featured demo.

  • Full 1M Token Context
  • No Registration Needed
  • Advanced Reasoning Capabilities
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Local Installation

Run MiniMax-M1 on your own hardware with our open-weight model.

  • Free for Commercial Use
  • Full Model Weight Access
  • Community Driven Support
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Why MiniMax-M1 is a Game-Changer

Unprecedented Reasoning

Built with a hybrid MoE architecture and trained with advanced RL, M1 excels at complex logic, coding, and multi-step problem-solving.

Massive 1M Token Context

Analyze entire books, extensive research papers, or full code repositories in a single pass, enabling a deeper level of understanding and analysis.

Revolutionary Efficiency

The "Lightning Attention" mechanism dramatically reduces computational costs, making large-scale AI more accessible and sustainable.

Download MiniMax-M1 Models

Model Parameters (Total/Active) Max Context Action
MiniMax-M1-40K 456B / 45.9B 1M Tokens Go to Repo
MiniMax-M1-80K 456B / 45.9B 1M Tokens Go to Repo

Frequently Asked Questions about MiniMax-M1

MiniMax-M1 is a state-of-the-art, open-weight large language model designed for complex reasoning. Its key innovations are:

  • A hybrid Mixture-of-Experts (MoE) architecture for power and efficiency.
  • A massive 1 million token context window, perfect for analyzing entire codebases or research papers.
  • A "Lightning Attention" mechanism that drastically reduces computation on long sequences.

Yes, MiniMax-M1 is released under a permissive open-weight license, making it completely free for both academic research and commercial applications without requiring royalties or fees.

MiniMax-M1 sets a new standard, particularly in specific areas. Compared to other strong open-weight models, M1 shows superior performance in complex software engineering, tool use, and long-context tasks. Its efficiency is also a major advantage; it consumes only 25% of the computational resources (FLOPs) of DeepSeek R1 at a 100K token generation length.

The "thinking budget" refers to the extent of reinforcement learning (RL) the model has undergone. We provide two versions:

  • M1-40K: A highly capable model trained with a 40,000 step budget.
  • M1-80K: An even more advanced version with an 80,000 step budget for enhanced reasoning capabilities.

While our online demo requires only a web browser, running M1 locally is demanding due to its size. We recommend:

  • A high-end GPU setup with substantial VRAM to hold the model weights.
  • Python 3.8 or newer.
  • Familiarity with frameworks like PyTorch and the Hugging Face ecosystem.