200亿AI独角兽反击，MiniMax首款推理模型对标DeepSeeK，算力成本仅53万美元

Core Insights - MiniMax, a Chinese AI startup valued at 20 billion RMB, has launched its first inference model, M1, which challenges leading models like DeepSeek and others with significantly lower training costs and superior efficiency [1][6]. Performance and Efficiency - M1 outperforms domestic closed-source models and approaches the performance of the best overseas models, surpassing DeepSeek, Alibaba, ByteDance, OpenAI, Google, and Anthropic in certain tasks [1]. - In terms of efficiency, M1 consumes less than 50% of the computational power of DeepSeek R1 when generating 64K tokens, and only 25% for 100K tokens [7]. - The model has a total of 456 billion parameters and supports context inputs of up to 1 million tokens, which is eight times that of DeepSeek R1 [3]. Cost Efficiency - The entire training process for M1 utilized 512 NVIDIA H800 GPUs over three weeks, with a rental cost of approximately 537,400 USD (around 3.8 million RMB), which is an order of magnitude lower than initially expected [6]. - MiniMax has developed a new reinforcement learning algorithm named CISPO, which achieved double the speed of ByteDance's recent DAPO algorithm, requiring only 50% of the training steps to reach similar performance [6]. Market Positioning - MiniMax has adopted a tiered pricing strategy for its API, making M1 more cost-effective compared to DeepSeek R1, especially in the input length ranges of 0-32K and 32K-128K tokens [8]. - M1 is positioned as a "price killer" in the market, receiving positive feedback from developers for its cost-performance ratio [8]. Future Developments - M1 is just the first product in a series of releases planned by MiniMax, which aims to introduce intelligent agent applications and further updates in video and music model capabilities [9]. - The company believes that M1's efficient architecture will provide unique advantages in future intelligent agent applications that require extensive reasoning and integration of long-context information [9].