MiniMax M1 1. Model Overview We introduce MiniMax M1, the world's first open weight, large scale hybrid attention reasoning model. MiniMax M1 is powered by a hybrid Mixture of Experts (MoE) architecture combined with a lightning attention mechanism. The model is developed based on our previous MiniMax Text 01 model, which contains a total of 456 billion parameters with 45.9 billion parameters activated per token. Consistent with MiniMax Text 01, the M1 model natively supports a context length of 1 million tokens, 8x the context size of DeepSeek R1. Furthermore, the lightning attention mechanism in MiniMax M1 enables efficient scaling of test time compute – For example, compared to DeepSeek R1, M1 consumes 25% of the FLOPs at a generation length of 100K tokens. These properties make M1 particularly suitable for complex tasks that require processing long inputs and thinking extensively. MiniMax M1 is trained using large scale reinforcement learning (RL) on diverse problems ranging from traditional mathematical reasoning to sandbox based, real world software engineering environments. We develop an efficient RL scaling framework for M1 highlighting two perspectives: (1) We propose CISP…
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