刚刚，上海大模型独角兽MiniMax，正式通过港交所聆讯，吹响了IPO冲刺号角。 克雷西 杰西卡 发自 凹非寺 量子位 | 公众号 QbitAI 这这这……这招股书一披露，不是里里外外都在讲OpenAI和美国大模型巨兽们的泡沫之大吗？ 历数MiniMax的过往经历，这家大模型独角兽在市场当中一骑绝尘，也绝非偶然—— 更早之前，这家公司在资本市场的融资能力同样一流，成立四年间，已吸引米哈游、阿里巴巴、腾讯、小红书、小米、金山、PCG和正大集团等知名机构 投资站台，累计融资已超15亿美元。 但直到招股书披露，更重要的资本吸引力原因才完全明确—— 不仅因为全模态能力全球领先，更关键的是，累计花费只用了5亿美元，不到OpenAI的1%。 所以，MiniMax到底都做了些什么？ MiniMax是谁？ 如果用MiniMax给自己的定位来说，这是一家全球化的通用人工智能（AGI）科技公司。 全球化不难理解，MiniMax的服务已经覆盖了全球200多个国家和地区，国际化业务收入占比达70%，公司人才也有30%拥有海外背景。 AGI，则是MiniMax的终极探索目标。 具体实现上，MiniMax将可扩展性（Scalabilit ...

在持续高强度研发投入、快速迭代全模态模型的同时，MiniMax也不断增强商业化和组织效率。招股书 数据显示，公司经调整净亏损在2025年与上年同期相比近乎持平，实现了在高速增长下的亏损有效收 窄，这得益于多元化的收入模型与高效的费用投入。2025年前九个月，MiniMax在收入同比增长超 170%的同时，研发开支同比增长30%，销售及营销开支同比下降26%。据统计，MiniMax自成立至今年 9月累计花费5亿美元（约35亿元人民币），低于国际同行。 通用人工智能（AGI）公司MiniMax（稀宇科技）12月21日首次刊发其聆讯后资料集（PHIP）版本的招 股书资料，有望刷新纪录，成为从成立到IPO历时最短的AI公司。 企查查显示，MiniMax 2022年成立以来共经历5次融资，获米哈游、阿里、腾讯、小红书、高瓴、 IDG、红杉、经纬、明势、云启等多家顶尖机构的投资和支持。作为通用人工智能公司，MiniMax基于 自研大模型，打造了覆盖C端与B端的AI原生产品矩阵，包括海螺AI、Talkie和星野等。 招股书显示，语音领域，MiniMax于2023年推出国内首个基于Transformer架构的语音大模型Spe ...

Core Viewpoint - MiniMax, a leading AI model unicorn, has successfully passed the Hong Kong Stock Exchange hearing, signaling its IPO ambitions amidst discussions about the bubble in large AI models like OpenAI [1][3]. Group 1: Company Overview - MiniMax has raised over $1.5 billion in funding within four years, attracting investments from notable firms such as MiHoYo, Alibaba, Tencent, and others [3][62]. - The company has a global presence, serving over 200 countries, with 70% of its revenue coming from international markets [6][42]. - MiniMax aims to achieve Artificial General Intelligence (AGI) and views scalability as a core driver towards this goal [8][7]. Group 2: Technological Advancements - MiniMax is one of the few companies that invested in multimodal model development from its inception [10]. - The company has released several models, including the M1 and M2 text models, with M2 achieving top rankings in performance and cost efficiency [16][17]. - MiniMax has also developed leading models in voice, music, and video, with its video model Hailuo ranking in the top tier of international tests [20][25][26]. Group 3: Financial Performance - MiniMax's revenue surged from $346,000 in 2023 to $30.52 million in 2024, marking a 782.2% increase [39]. - By the first nine months of 2025, revenue reached $53.44 million, significantly surpassing the previous year's total [40]. - The company has achieved a gross margin improvement from -24.7% in 2023 to 23.3% in the first nine months of 2025 [45][46]. Group 4: Operational Efficiency - MiniMax's R&D expenses have increased significantly, but the efficiency of these investments has improved, with training-related cloud computing costs as a percentage of revenue decreasing from over 1365% in 2023 to 266.5% in 2025 [52][54]. - The company has a cash reserve of $1.102 billion, sufficient to sustain operations for over 53 months without additional fundraising [58][59]. - MiniMax's team is young, with an average age of 29, and a high proportion of R&D personnel, which contributes to its innovative and efficient operational model [70][71].

Core Insights - MiniMax, a leading AI technology company, is set to list on the Hong Kong Stock Exchange in January 2026, potentially marking the fastest IPO for an AI company globally since its establishment [4] - The company has demonstrated remarkable growth with a revenue increase of 782.2% and 70% of its income coming from overseas markets, challenging the notion that AI development requires massive capital infusion [6][12] - MiniMax's business model emphasizes a complete cycle of technology self-research, global deployment, and commercial monetization, showcasing its capability to redefine industry standards [6][21] Financial Performance - MiniMax's revenue surged from $3.5 million in 2022 to $30.5 million in 2024, reflecting a growth rate of 782.2% [7] - The company achieved a gross profit margin improvement from -24.7% in 2023 to 23.3% in the first nine months of 2025 [7] - As of September 30, 2025, MiniMax had over $1.1 billion in cash reserves, sufficient to sustain operations for over 53 months without additional fundraising [10] Revenue Structure - MiniMax has diversified its revenue streams through subscription services, in-app purchases, and enterprise APIs, with a significant portion of its income derived from consumer-facing products [9] - The company has seen a 181% year-on-year increase in consumer revenue, with over 71% of its income coming from the consumer segment [9] - MiniMax serves over 2.12 billion individual users and more than 100,000 enterprise clients globally, indicating strong international market penetration [9][12] Technological Advancements - MiniMax has positioned itself as one of the few companies in the top tier of multimodal AI, achieving significant breakthroughs in voice, video, and text models with minimal investment [14][19] - The company has developed advanced models such as Speech 2.6 and Hailuo 2.3, which have received recognition for their performance in global evaluations [16][17] - MiniMax's innovative approach to technology development, characterized by a lean organizational structure and a youthful workforce, has contributed to its rapid advancements and cost efficiency [19] Market Impact - MiniMax's upcoming IPO is expected to shift the focus of the AI industry from speculative investments to value creation and efficiency [21] - The company's success provides a replicable model for other Chinese AI firms, potentially leading to a maturation of the entire AI industry chain [21] - MiniMax's achievements highlight China's growing competitiveness in the global AI landscape, suggesting a significant shift in industry dynamics [20]

12月21日，全球通用人工智能（AGI）公司MiniMax（稀宇科技）首次刊发其聆讯后资料集 （PHIP）版本的招股书资料，有望刷新记录，成为从成立到IPO 历时最短的AI公司，标志着 中国力量在国际资本市场迈出关键一步。 MiniMax成立于2022年初，是一家"生而全球化"的AI公司，致力于研发具备国际竞争力的通用 模型。基于自研大模型，MiniMax 成功打造了覆盖C端与B端的 AI 原生产品矩阵，包括海螺 AI、Talkie和星野等，并为企业用户和开发者提供开放平台服务。 截至2025年9月30日，MiniMax已有超过200个国家及地区的逾2.12亿名个人用户以及超过100 个国家的13万企业客户。值得关注的是，其2025年前九个月营收同比增长超过170%，海外市 场收入贡献占比超70%，展现出卓越的全球市场开拓能力与收入结构的健康多元。 MiniMax专注全模态模型自研，技术迭代密集，模型进展每年上一个台阶，实现持续突破， 是"全球唯四全模态进入第一梯队"的大模型公司。 语音领域，于2023年推出国内首个基于Transformer架构的语音大模型Speech 01，随后在2024 年将综合性能提升 ...

每经上海12月21日电（记者 陈婷）12月21日，通用人工智能（AGI）公司MiniMax（稀宇科技）首次刊 发其聆讯后资料集（PHIP）版本的招股书。招股书显示，截至2025年9月30日止的九个月，MiniMax收 入为5343.7万美元。 截至2025年9月底，MiniMax员工385人，平均年龄29岁，研发人员占比近74%，董事会平均年龄32岁。 招股书提及，随着扩大运营规模，MiniMax的毛利率由2023年的-24.7%升至2024年的12.2%，并进一步 升至截至2025年9月30日止九个月的23.3%。此外，其AI（人工智能）原生产品的付费用户数从2023年 的约11.97万名增至2024年的约65.03万名，并于截至2025年9月30日止九个月进一步增至约177.16万名。 截至2025年9月30日，其AI原生产品累计为来自超过200个国家及地区的逾2亿名个人用户，以及来自超 过100个国家及地区的10万余名企业和开发者提供服务。 ...

在AGI(通用人工智能)全球竞赛中，中国大模型"独角兽"企业的资本化进程终于迈出实质性一步。 12月19日晚间，北京智谱华章科技股份有限公司(以下简称智谱)披露招股书(申请版本，下同)，宣布赴 港冲刺"全球大模型第一股"。 《每日经济新闻》记者注意到，成立于2019年的智谱，由清华大学技术成果转化而来。招股书显示，智 谱过去3年营收实现年均复合增长率超过130%。不过，在营收高歌猛进的同时，作为一家典型技术驱动 型企业，智谱也面临巨额研发投入带来的亏损挑战。 大模型热潮翻涌3年，这份招股书不仅是智谱自身的"体检表"，更是观察大模型在"百模大战"后进入商 业化落地深水区的一个重要样本。 从行业地位看，招股书援引咨询公司弗若斯特沙利文的数据称，按2024年收入计，智谱在中国独立通用 大模型厂商中排名第一，但市场份额仅为6.6%。这意味着，即便其处于相对靠前的位置，行业整体的 市场份额分布仍高度分散。 与收入快速放大形成对照的是公司尚未明确看到规模化盈利拐点。2022~2024年各年度，智谱分别录得 净亏损1.44亿元、7.88亿元和29.58亿元，2025年上半年净亏损为23.58亿元，亏损额同比扩大91%。从亏 ...

炒股就看金麒麟分析师研报，权威，专业，及时，全面，助您挖掘潜力主题机会！ 新浪科技讯 12月21日晚间消息，通用人工智能（AGI）公司MiniMax（稀宇科技）刊发其聆讯后资料集 （PHIP）版本的招股书资料。据悉，截至2025年9月30日，MiniMax已有超过200个国家及地区的逾2.12 亿名个人用户以及超过100个国家的13万企业客户。 值得关注的是，MiniMax 2025年前九个月营收同比增长超过170%，海外市场收入贡献占比超70%，展 现出出色的全球市场开拓能力与收入结构多元性。 值得注意的是，MiniMax自成立到25年9月累计花费5亿美金（约35亿RMB），对比OpenAI的400亿至 550亿美元累计花销。 MiniMax 成立于2022年初，基于自研大模型，MiniMax 打造了覆盖C端与B端的 AI 原生产品矩阵，包括 海螺AI、Talkie和星野等，并为企业用户和开发者提供开放平台服务。 责任编辑：常福强 根据招股书数据显示，经调整净亏损在2025年与上年同期相比近乎持平，实现了在高速增长下的亏损有 效收窄。这得益于多元化的收入模型与高效的费用投入——2025年前九个月，在收入同比 ...

文 | 虎啸商业评论 在硅谷那条被咖啡和代码浸润的街道上，数字往往不只是货币，更是意志的延伸。 当山姆·奥特曼（Sam Altman）在2025年12月这个寒冷的冬季，为OpenAI抛出那份高达1000亿美元的融 资计划书，8300亿美元的投后估值预期，让这家尚未真正"盈利"的公司直逼万亿美元俱乐部。 这笔钱的规模，甚至超过了许多主权国家的年度财政预算。 对于普通人来说，这只是一个天文数字；但对于深谙行业逻辑的资深人士而言，这1000亿美元是 OpenAI在AGI（通用人工智能）前夜，为自己修筑的最后一道、也是最高的一道护城河。 01 算力重工业化，Scaling Law的"昂贵税收" 我们必须清醒地认识到，AI产业正在经历一场从"轻资产软件业"向"重资产重工业"的剧烈转型。 如果说早期的ChatGPT是实验室里的灵光一现，那么现在的GPT-5以及后续的"Stargate（星门）"项目， 则是需要消耗数个中等城市电力、填满数万个机柜的工业级怪兽。 在这个节点，如果不能通过一次性的大规模注资来锁定未来的算力供给，那么OpenAI所谓的"技术领 先"将在Google、Meta和Anthropic的资本围剿下迅速缩 ...

Core Viewpoint - The article discusses the limitations of achieving Artificial General Intelligence (AGI) due to physical and resource constraints, emphasizing that scaling alone is not sufficient for significant advancements in AI [3][20][32]. Group 1: Limitations of AGI - Tim Dettmers argues that AGI will not happen because computation is fundamentally physical, and there are inherent limitations in hardware improvements and scaling laws [8][10][12]. - The article highlights that as transistor sizes shrink, while computation becomes cheaper, memory access becomes increasingly expensive, leading to inefficiencies in processing power [11][17]. - The concept of "superintelligence" is critiqued as a flawed notion, suggesting that improvements in intelligence require substantial resources, and thus, any advancements will be gradual rather than explosive [28][29][30]. Group 2: Hardware and Scaling Challenges - The article points out that GPU advancements have plateaued, with significant improvements in performance per cost ceasing around 2018, leading to diminishing returns on hardware investments [16][17]. - Scaling AI models has become increasingly costly, with the need for linear improvements requiring exponential resource investments, indicating a nearing physical limit to scaling benefits [20][22]. - The efficiency of current AI infrastructure is heavily reliant on large user bases to justify the costs of deployment, which poses risks for smaller players in the market [21][22]. Group 3: Divergent Approaches in AI Development - The article contrasts the U.S. approach of "winner-takes-all" in AI development with China's focus on practical applications and productivity enhancements, suggesting that the latter may be more sustainable in the long run [23][24]. - It emphasizes that the core value of AI lies in its utility and productivity enhancement rather than merely achieving higher model capabilities [24][25]. Group 4: Future Directions and Opportunities - Despite the challenges, the article suggests that there are still significant opportunities for improvement in AI systems through better hardware utilization and innovative model designs [39][45][67]. - It highlights the potential for advancements in training efficiency and inference optimization, indicating that current models are not yet fully optimized for existing hardware capabilities [41][43][46]. - The article concludes that the path to more capable AI systems is not singular, and multiple avenues exist for achieving substantial improvements in performance and utility [66][69].