MiniMax, a Shanghai-based AI company, has open-sourced MiniMax-M1, the world’s longest context window reasoning AI model, featuring a context length of 1 million tokens—eight times larger than DeepSeek R1’s 128k tokens. MiniMax-M1 is a large-scale hybrid-attention model built on a Mixture-of-Experts (MoE) architecture combined with a lightning attention mechanism, enabling highly efficient processing and scaling of long inputs with significantly reduced computational cost. The model contains 456 billion parameters with 45.9 billion active per token and supports complex reasoning tasks including mathematics, software engineering, and agentic tool use.
MiniMax-M1 was trained using a novel reinforcement learning algorithm called CISPO, which clips importance sampling weights instead of token updates, enhancing training efficiency. The model outperforms other leading open-weight models like DeepSeek-R1 and Qwen3-235B on benchmarks involving extended thinking, coding, and long-context understanding. It is also highly cost-effective, with MiniMax reporting training expenses around $535,000—approximately 200 times cheaper than OpenAI’s GPT-4 estimated training costs.
The model’s lightning attention mechanism reduces the required compute during inference to about 25-30% of what competing models need for similar tasks, making it well-suited for real-world applications demanding extensive reasoning and long-context processing. MiniMax-M1 is available on GitHub and HuggingFace, positioning it as a strong open-source foundation for next-generation AI agents capable of tackling complex, large-scale problems efficiently.
MiniMax-M1 represents a breakthrough in open-source AI by combining an unprecedented 1 million token context window, hybrid MoE architecture, efficient reinforcement learning, and cost-effective training, challenging leading commercial and open-weight models worldwide.