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MiniMax-M1 is a new open source model with 1 MILLION TOKEN context and new, hyper efficient reinforcement learning
MiniMax-M1 presents a flexible option for organizations looking to experiment with or scale up advanced AI capabilities while managing costs.
For engineering leads responsible for the full lifecycle of LLMs — such as optimizing model performance and deploying under tight timelines — MiniMax-M1 offers a lower operational cost profile while supporting advanced reasoning tasks. The hybrid-attention architecture may help simplify scaling strategies, and the model’s competitive performance on multi-step reasoning and software engineering benchmarks offers a high-capability base for internal copilots or agent-based systems. By combining open access with advanced architecture and compute efficiency, MiniMax-M1 may serve as a foundational model for developers building next-generation applications that require both reasoning depth and long-range input understanding.
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