大模型公司的烧钱账

Core Insights - The article discusses the financial challenges and operational strategies of two AI companies, Zhipu and MiniMax, highlighting their significant cash burn and reliance on high computational costs to train competitive language models [15][32][34] Financial Overview - Over the past three years, Zhipu and MiniMax have collectively burned 11 billion yuan, with half of this amount spent on renting computational power for model training [15][32] - Zhipu has reported a gross profit margin of approximately 60% from its enterprise market, with 70% of its revenue coming from localized deployment of large model systems [33] - MiniMax, targeting individual users, generates 70% of its revenue from products like Xingye/Talkie and Hailuo AI, with a monthly active user count reaching 27.6 million by September 2025 [33] Cost Structure - Zhipu's operational costs include 4.4 billion yuan in research and development personnel expenses and 3 billion yuan in computational costs for inference [20][21] - MiniMax's operational costs are similarly high, with 3.1 billion yuan in marketing personnel expenses and 2.3 billion yuan in computational costs for training [23][24] Revenue Models - Zhipu's revenue structure includes 0.44 billion yuan (35%) from enterprise custom services and API income, while MiniMax earns 0.81 billion yuan (85%) from localized deployment [25] - The gross margins for different business models show that Zhipu's localized deployment has a margin of 50%, while MiniMax's original AI products have a negative margin [25] Cash Reserves and Financing - Zhipu has 8.9 billion yuan in available funds, with over 70% being bank loan credits, while MiniMax has 7.35 billion yuan in cash, with 60% allocated to financial investments [33][34] - Both companies are looking to expand their financing channels through public listings, but they will continue to face the challenge of high operational costs [34]