Shypd.aiThe largest open index of AI toolsMiniMax-M1 is an open-weight, large-scale hybrid-attention reasoning model that supports a context length of 1 million tokens. It is designed for complex tasks requiring long input processing and extensive reasoning.
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MiniMax-M1 is the world's first open-weight, large-scale hybrid-attention reasoning model, powered by a hybrid Mixture-of-Experts (MoE) architecture combined with a lightning attention mechanism. Developed based on the MiniMax-Text-01 model, it features 456 billion parameters with 45.9 billion parameters activated per token. A key differentiator is its native support for a context length of 1 million tokens, significantly larger than competitors. The lightning attention mechanism ensures efficient scaling of test-time compute, consuming 25% of the FLOPs compared to DeepSeek R1 at a generation length of 100K tokens. MiniMax-M1 is trained using large-scale reinforcement learning (RL) on diverse problems, including mathematical reasoning and software engineering. It offers two versions with 40K and 80K thinking budgets, outperforming other strong open-weight models on complex software engineering, tool-using, and long-context tasks. It also supports function calling capabilities and provides a chatbot and API for general use and evaluation.
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