2025年末的全球资本市场，AGI（通用人工智能）赛道正上演"冰火两重天"：一边是行业洗牌加速、中 小玩家因算力与资金压力陆续出清，另一边是头部企业估值狂飙、资本争抢核心标的。12月21日晚间， 国产AI独角兽MiniMax（上海稀宇极智科技有限公司）在港交所披露易正式刊登聆讯后招股书 （PHIP）。此前的12月17日，公司已顺利通过港交所上市聆讯并获得中国证监会备案，若进程顺利， 将于2026年1月正式挂牌。这家成立仅四年的企业不仅即将创下全球AI公司从成立到IPO的最快纪录， 更有望成为全球资本市场首个纯AGI主业上市公司，为中国AI力量敲开国际资本大门。几乎同一时段， 行业龙头OpenAI传出新一轮千亿融资计划，投后估值或将飙升至8300亿美元，创下全球科技初创企业 估值峰值。8300亿估值引爆赛道！AGI成资本最确定性押注方向OpenAI的估值变化，堪称全球AGI赛道 热度的"晴雨表"。近日，在短短48小时内，其估值从5000亿美元跃升至8300亿美元，远超传统SaaS公司 的估值水平。这一夸张估值背后，实际上是投资者对其在AGI赛道推动商业化落地的强烈信心。然而， 当前资本的狂热并非盲目跟风。UNC ...

Core Viewpoint - MiniMax has launched its self-developed MiniMax M1 model, which competes directly with DeepSeek R1 and Google's Gemini 2.5 Pro in terms of key technical specifications, architecture design, context processing capabilities, and training costs [1][2]. Group 1: Model Specifications - MiniMax M1 supports a context length of 1 million tokens, which is 8 times larger than DeepSeek R1's 128,000 tokens and only slightly behind Google's Gemini 2.5 Pro [1]. - The total parameter count for MiniMax M1 is 456 billion, with 45.9 billion parameters activated per token, while DeepSeek R1 has a total of 671 billion parameters but activates only 37 billion per token [1]. Group 2: Cost Efficiency - MiniMax M1 consumes only 25% of the floating-point operations compared to DeepSeek R1 when generating 100,000 tokens, and requires less than half the computational power for inference tasks of 64,000 tokens [2]. - The training cost for MiniMax M1 was only $535,000, significantly lower than the initial expectations and much less than the $5-6 million GPU cost for training DeepSeek R1 [2]. Group 3: Pricing Strategy - MiniMax M1 has a tiered pricing model for its API services based on the number of input or output tokens, with the first tier charging 0.8 yuan per million input tokens and 8 yuan per million output tokens, which is lower than DeepSeek R1's pricing [3]. - The pricing for the first two tiers of MiniMax M1 is lower than that of DeepSeek R1, and the third tier for long text is currently not covered by DeepSeek [3]. Group 4: Technology Innovations - MiniMax M1's capabilities are supported by two core technologies: the linear attention mechanism (Lightning Attention) and the reinforcement learning algorithm CISPO, which enhances efficiency and stability in training [2].