根据全球公开发售文件，公司计划发行 3741.95万股H股，招股将于2026年1月5日结束，并 预计于1月8日正式挂牌交易。 此次发行定价为每股 116.20港元，以此计算，智谱华章本次募资规模预计将达43亿港元， IPO市值预计超过511亿港元。 11家基石投资者累计认购29.8亿港元，智谱华章此次IPO基石占比近七成。这背后是一场技术 与资本的双重竞速——作为中国独立通用大模型开发商的收入 前列者 ，智谱华章 2024年收 入达到3.12亿元。 它用四年时间，将年营收从 5740万元提升至超过3亿元。而支撑这场高速增长的，是每年翻 倍的研发投入和一套已跑通的MaaS（模型即服务）商业模式。 抢滩占位MaaS市场 在亮眼的市值背后，是智谱华章高速增长的财务表现。公司的营收从 2022年的5740万元， 增长至2024年的3.12亿元，年复合增长率高达130%。 2025年上半年，公司收入为1.91亿元，同比增长325.39%。 与此同时，公司的毛利率表现稳健， 2022年至2024年分别为54.6%、64.6%和56.3%， 2025年上半年为50.0%。 "全球大模型第一股"的争夺在 2025年末迎来终局 ...

今天起，智谱正式向"全球大模型第一股"发起冲刺，招股启动了。消息一出，圈内炸了，朋友圈刷屏了，但热闹背后，真的全是机会吗。 AI圈的资本盛宴，迎来了关键一战。 12月30日，智谱正式启动招股，向"国产大模型第一股"的宝座发起冲刺。这不仅是智谱自身的里程碑，更被视作整个国内大模型行业从"烧钱研发"迈 向"资本市场检验"的标志性事件，毕竟在此之前，所有大模型玩家都还困在"高投入、低产出"的循环里，智谱的IPO，相当于为全行业探路。 市场的热情肉眼可见：作为从清华KEG实验室走出来的"学霸型企业"，智谱背后站着阿里、腾讯、美团等大厂，红杉、高瓴等顶级资本，还有多地国资加 持，IPO前估值已达243.8亿元。招股书里更是亮出了"三年营收复合增长率130%""服务1.2万家企业客户"的亮眼数据。 但在欢呼声中，我们更需要冷静下来，顶着清华光环、累计融资超83亿元的智谱，真的准备好了吗？ 智谱的IPO，标志着国内大模型行业正式进入"淘汰赛"阶段。2026年，随着更多玩家涌入资本市场，这场竞争只会更加残酷。 大模型风口吹了这么久，终于有选手跑到上市门口了。智谱这一步，看似风光无限，其实背后争议不小。有人说这是技术的胜利，有 ...

12月19日，北京智谱华章科技股份有限公司（下称"智谱"）正式披露聆讯后的招股书。 智谱抢在了MiniMax母公司上海稀宇科技有限公司（下称"MiniMax"）之前公开招股书，亦被不少市场 人士视为更有希望冲击"全球大模型第一股"。 其中，MiniMax被报道通过了港交所的聆讯，但迄今尚未公布招股书。 随着此次招股书的披露，外界对于智谱的经营状况也有了更深层次的了解。 2022年至2024年，智谱的收入分别为5740万、1.25亿元、3.12亿元。 具体来看，智谱主打MaaS（模型即服务），收入分成本地和云端部署两大部分。 其中，为客户提供私欲专属的AI模型是主要收入来源，2024年创收2.64亿元，占比在8成以上。 但本地化部署更像是"一次性项目"。 根据收入确认方式，智谱以套餐形式提供本地化部署的解决方案，定价根据模型类型与规模、纳入的计 算资源量及实施成本确定，套餐价格可按一次性计费或按年计费。 一般来说，本地部署的单个项目定价较高，这从智谱的客户结构中亦可得到印证，2024年前五大客户贡 献了1.42亿元的收入，占比达到45.5%。 相比之下，云端部署创收方式更为"细水长流"。 智谱的云端部署类型业务 ...

Core Insights - The article discusses the rapid growth and significance of Model as a Service (MaaS) in the AI landscape, emphasizing its role in lowering barriers to AI technology adoption and enhancing application efficiency [1][2][8] - By 2024, the Chinese MaaS market is projected to reach 710 million yuan, representing a year-on-year increase of 215.7% from 2023 [1][8] - China leads globally in the number of large models, with 1,509 out of 3,755 models published worldwide as of July 2025 [1][4] MaaS Industry Overview - MaaS encapsulates AI algorithms and capabilities to provide services that simplify AI technology usage, reduce application development costs, and enhance operational efficiency [2][4] - The service model supports various industries, including finance, government, and telecommunications, facilitating the large-scale application of AI [1][10] Market Size and Growth - The Chinese MaaS market is expected to experience explosive growth, reaching 710 million yuan in 2024, a significant increase from the previous year [1][8] - The AI large model application market in China is projected to reach 4.79 billion yuan in 2024, indicating substantial growth from 2023 [6] Competitive Landscape - The top five MaaS providers in China, including Volcano Engine, Alibaba, Baidu, Tencent, and China Mobile, collectively hold over 80% of the market share, with Volcano Engine leading at 37.5% [1][11] - The competitive landscape consists of cloud service providers, AI companies, and telecommunications operators, each leveraging their unique strengths to offer MaaS solutions [11] Development Trends - Future trends in MaaS include the collaboration of large and small models, unification of service capabilities, the emergence of new application ecosystems, and enhanced security measures [12][13]

Market Overview - The market size of AI large model solutions in China is projected to reach 3.49 billion yuan in 2024, representing a year-on-year growth of 126.4%, while the MaaS (Model as a Service) market is expected to see explosive growth of 215.7% [2][6] - The MaaS market is anticipated to grow at a compound annual growth rate (CAGR) of 66.1% from 2024 to 2029, reaching 9 billion yuan by 2029 [6][10] Cost Challenges - High infrastructure costs are a significant barrier to the scaling of enterprise-level AI applications, with Nvidia predicting global AI infrastructure spending to reach 3-4 trillion USD by 2030, with a CAGR of 38%-46% from 2025 to 2030 [3] - The cost of training a single enterprise large model can exceed 1 million yuan, with ongoing costs during the inference phase creating a rigid expenditure burden [3][4] - Small and medium-sized enterprises (SMEs) average AI investment is only 3.2 million yuan, less than one-tenth of that of large enterprises, limiting their applications to basic scenarios like intelligent customer service [4] Technical Challenges - The complexity of enterprise-level AI applications is significantly higher than personal scenarios, particularly in terms of computing power management and model adaptation [4][5] - The coexistence of multiple chip brands, including domestic and Nvidia chips, creates a "computing island" phenomenon due to differences in instruction sets and optimization logic [5] MaaS Advantages - MaaS is seen as the optimal service model for the implementation of enterprise-level AI, providing an integrated solution that includes model repositories, inference engines, and operational tools [6][10] - The cost advantages of MaaS are evident, as it significantly reduces the overall costs of AI applications through technological optimization and model innovation [6][7] - Companies using the MaaS model report a 2-3 times higher return on AI investment compared to traditional models, with the financial sector seeing returns as high as 4 times [7] Future Trends - The MaaS market is evolving towards "intelligent integration, localization, and ecosystem development," with public cloud services becoming more prevalent among SMEs and private deployments focusing on security and customization for large enterprises [10][11] - The integration of AI agents and multimodal technologies is expected to transform MaaS services from auxiliary tools to core infrastructure for digital transformation, enabling AI to become a productivity tool accessible to all enterprises [12]