中国人工智能公司MiniMax推出全新人工智能模型M1，称其性能可与OpenAI、Anthropic和谷歌DeepMind等实验室的顶级模型相抗衡，而训练成本却仅为后者的一小部分，且运行成本也更低。
此类情形早已屡见不鲜：每隔数月，一家在美国籍籍无名的中国人工智能实验室便会发布一款人工智能模型，颠覆人们对训练与运行前沿人工智能所需成本的传统认知。
今年1月，深度求索（DeepSeek）的R1模型引发全球轰动；3月，一家名为蝴蝶效应科技（Butterfly Effect，注册地在新加坡，但团队大部分成员在中国）的初创公司及其“代理人工智能”模型Manus曾短暂成为焦点；本周，总部位于上海的初创公司MiniMax凭借6月16日推出的M1模型成为人工智能行业热议的焦点——此前，该公司因发布人工智能生成的视频游戏而闻名。
根据MiniMax公布的数据，M1模型在智能和创造力方面可与OpenAI、Anthropic和深度求索的顶尖模型相抗衡，然而其训练和运行成本却低得惊人。
该公司表示，仅花费53.47万美元租用数据中心计算资源用于训练M1，这比ChatGPT-4o的训练成本预估值低近200倍。行业专家称，ChatGPT-4o的训练成本可能超过1亿美元（OpenAI尚未公布其训练成本数据）。
如果这一数据准确无误（MiniMax的说法尚未得到独立验证），那么那些向OpenAI和Anthropic等私有大型语言模型制造商投入数千亿美元的蓝筹股投资者，以及微软和谷歌的股东，很可能会因此感到不安。这是因为人工智能业务目前处于严重亏损状态；据科技媒体《The Information》10月的一份报告称，行业领军企业OpenAI预计将在2026年亏损140亿美元，而且可能要到2028年才能实现收支平衡。该报告的分析基于OpenAI与投资者共享的财务文件。
如果客户能够通过使用MiniMax的开源人工智能模型获得与OpenAI模型相同的性能表现，这可能会削弱市场对OpenAI产品的需求。OpenAI已在大幅降低其最强大模型的定价以稳固市场份额。最近，它将o3推理模型的使用成本削减了80%，而这还是在MiniMax发布M1之前。
MiniMax的报告结果还意味着，企业在运行这些模型时可能无需投入过多计算成本，此情况可能会波及亚马逊AWS、微软Azure和谷歌云平台等云服务提供商的利润。同时，这可能导致对英伟达芯片的需求减少，而英伟达芯片是人工智能数据中心的核心硬件。
MiniMax的M1最终产生的影响可能与今年早些时候深度求索（总部位于杭州）发布其R1大型语言模型时的情况类似。当时，深度求索宣称R1的性能与ChatGPT相当，但训练成本仅为ChatGPT的一小部分，这一声明导致英伟达股价单日下跌17%，市值蒸发约6000亿美元。截至目前，MiniMax的消息尚未引发类似波动。本周英伟达股价跌幅不到0.5%，不过，如果MiniMax的M1能像深度求索的R1模型那样得到广泛应用，情况可能会发生变化。
MiniMax关于M1的声明尚未得到验证
不同之处在于，独立开发者尚未证实MiniMax关于M1的声明。以深度求索的R1为例，开发者迅速确认该模型性能确实如公司所宣称的那般出色；而蝴蝶效应科技的Manus模型在开发者测试中暴露出易出错的缺陷，无法达到公司演示的效果，初期热度迅速消退。未来几天将成为关键节点——开发者是接纳M1，还是反应冷淡，届时自会见分晓。
MiniMax背后有腾讯、阿里巴巴等中国头部科技公司支持。目前尚不清楚该公司员工规模，其首席执行官闫俊杰的公开信息也极为有限。除了MiniMax Chat外，该公司还推出了图像生成工具Hailuo AI和虚拟形象应用Talkie。据MiniMax称，这些产品在200多个国家和地区拥有数千万用户，以及5万家企业客户，其中许多企业被Hailuo AI能够即时生成视频游戏的能力所吸引。
然而，没有什么比免费试用更能吸引客户。目前，想要试用MiniMax M1的用户可通过其运行的API免费试用，开发者还能免费下载整个模型并在自有计算资源上运行（不过在这种情况下，开发者需自行承担计算时间费用）。如果MiniMax的能力如该公司所宣称的那样，无疑会收获一定的关注度。
M1的另一大核心卖点在于其具备100万令牌的“上下文窗口”。令牌是数据单元，大致相当于四分之三单词的文本量，上下文窗口指模型生成单次回应时可使用的数据上限。100万令牌大致相当于七到八本书或一小时的视频内容——这意味着M1能处理的数据量超过部分顶尖模型：例如，OpenAI的o3和Anthropic的Claude Opus 4的上下文窗口仅约20万令牌。不过，Gemini 2.5 Pro同样拥有100万令牌的上下文窗口，而Meta的部分开源Llama模型上下文窗口甚至可达1000万令牌。
一位X用户写道：“MiniMax M1太疯狂了！”他声称自己在毫无编程基础的情况下，仅用60秒就生成了一个网飞（Netflix）克隆版——包括电影预告片、实时网站以及“完美响应式设计”。 （财富中文网）
译者：中慧言-王芳
It’s becoming a familiar pattern: Every few months, an AI lab in China that most people in the U.S. have never heard of releases an AI model that upends conventional wisdom about the cost of training and running cutting-edge AI.
In January, it was DeepSeek’s R1 that took the world by storm. Then in March, it was a startup called Butterfly Effect—technically based in Singapore but with most of its team in China—and its “agentic AI” model, Manus, that briefly captured the spotlight. This week, it’s a Shanghai-based upstart called MiniMax, best known previously for releasing AI-generated video games, that is the talk of the AI industry thanks to the M1 model it debuted on June 16.
According to data published by MiniMax, its M1 is competitive with top models from OpenAI, Anthropic, and DeepSeek when it comes to both intelligence and creativity, but is dirt cheap to train and run.
The company says it spent just $534,700 renting the data center computing resources needed to train M1. This is nearly 200-fold cheaper than estimates of the training cost of ChatGPT-4o, which, industry experts say, likely exceeded $100 million (OpenAI has not released its training cost figures).
If accurate—and MiniMax’s claims have yet to be independently verified—this figure will likely cause some agita among blue-chip investors who’ve sunk hundreds of billions into private LLM makers like OpenAI and Anthropic, as well as Microsoft and Google shareholders. This is because the AI business is deeply unprofitable; industry leader OpenAI is likely on track to lose $14 billion in 2026 and is unlikely to break even until 2028, according to an October report from tech publication The Information, which based its analysis on OpenAI financial documents that had been shared with investors.
If customers can get the same performance as OpenAI’s models by using MiniMax’s open-source AI models, it will likely dent demand for OpenAI’s products. OpenAI has already been aggressively lowering the pricing of its most capable models to retain market share. It recently slashed the cost of using its o3 reasoning model by 80%. And that was before MiniMax’s M1 release.
MiniMax’s reported results also mean that businesses may not need to spend as much on computing costs to run these models, potentially denting profits for cloud providers such as Amazon’s AWS, Microsoft’s Azure, and Google’s Google Cloud Platform. And it may mean less demand for Nvidia’s chips, which are the workhorses of AI data centers.
The impact of MiniMax’s M1 may ultimately be similar to what happened when Hangzhou-based DeepSeek released its R1 LLM model earlier this year. DeepSeek claimed that R1 functioned on par with ChatGPT at a fraction of the training cost. DeepSeek’s statement sank Nvidia’s stock by 17% in a single day—erasing about $600 billion in market value. So far, that hasn’t happened with the MiniMax news. Nvidia’s shares have fallen less than 0.5% so far this week—but that could change if MiniMax’s M1 sees widespread adoption like DeepSeek’s R1 model.
MiniMax’s claims about M1 have not yet been verified
The difference may be that independent developers have yet to confirm MiniMax’s claims about M1. In the case of DeepSeek’s R1, developers quickly determined that the model’s performance was indeed as good as the company said. With Butterfly Effect’s Manus, however, the initial buzz faded fast after developers testing Manus found that the model seemed error-prone and couldn’t match what the company had demonstrated. The coming days will prove critical in determining whether developers embrace M1 or respond more tepidly.
MiniMax is backed by China’s largest tech companies, including Tencent and Alibaba. It is unclear how many people work at the company, and there is little public information about its CEO, Yan Junjie. Aside from MiniMax Chat, the company also offers graphic generator Hailuo AI and avatar app Talkie. Through these products, MiniMax claims tens of millions of users across 200 countries and regions as well as 50,000 enterprise clients, a number of whom were drawn to Hailuo for its ability to generate video games on the fly.
But few things win customers more than free access. Right now, those who want to try MiniMax’s M1 can do so for free through an API MiniMax runs. Developers can also download the entire model for free and run it on their own computing resources (although in that case, the developers have to pay for the compute time). If MiniMax’s capabilities are what the company claims, it will no doubt gain some traction.
The other big selling point for M1 is that it has a “context window” of 1 million tokens. A token is a chunk of data, equivalent to about three-quarters of one word of text, and a context window is the limit of how much data the model can use to generate a single response. One million tokens is equivalent to about seven or eight books or one hour of video content. The 1 million–token context window for M1 means it can take in more data than some of the top-performing models: OpenAI’s o3 and Anthropic’s Claude Opus 4, for example, both have context windows of only about 200,000 tokens. Gemini 2.5 Pro, however, also has a 1 million–token context window, and some of Meta’s open-source Llama models have context windows of up to 10 million tokens.
“MiniMax M1 is INSANE!” writes one X user who claims to have made a Netflix clone—complete with movie trailers, a live website, and “perfect responsive design” in 60 seconds with “zero” coding knowledge.