内容来源：https://aiweekly.co/issues/what-ai-models-are-actually-coming-in-the-next-six-months

内容总结：

AI行业前瞻：未来半年三大赛道竞速，多款重磅模型发布在即

随着2025年进入尾声，全球AI行业正迎来一轮密集的发布窗口期。综合各方信息，OpenAI、谷歌、Meta、Anthropic以及多家中国实验室和世界模型初创公司均被曝出正在筹备或即将发布新模型。从泄露信息、测试报告到投资人评论，市场对发布时间的猜测已提前透支。本文梳理了未来六个月内最可能落地的产品、可能跳票的项目，以及真正值得用户调整规划的发布。

闭源模型之争：安全与延迟成主要瓶颈

西方四大实验室在发布日期的确定上各有难处。OpenAI的下一个模型正在等待安全架构的最终确认，而非训练完成。内部代号Astra的模型在智能体编码和网络安全方面展现出巨大能力提升，以至于OpenAI无法排除其具备关键能力的可能性，部分内部工作已暂停，直到更严格的控制措施到位。这使得Astra成为当前队列中最重要但最难以排期的模型。

Meta则将12月31日定为检验其AI重建成果的节点。下一代Muse Spark模型（代号Watermelon）仍在训练中，算力投入大幅增加，若延迟发布将不仅意味着时间后移，更会重新引发外界对Meta巨额投入是否产出前沿模型的质疑。

谷歌方面，Gemini 3.5 Pro已在测试中，但据报道进度落后数月，而Gemini 4尚在预训练阶段。外界预计谷歌更可能先推出延迟的3.5 Pro，而非传闻中突然发布的Gemini 4。Anthropic的传闻则较为复杂，既有小版本更新，也有内部更强大但未计划发布的模型，这些信息更多是信号而非路线图。

开源模型异军突起：中国力量或成价格颠覆者

最可信的意外可能来自中国。据路透社报道，中国初创公司MiniMax正在训练可能是全球最大的开源权重模型，参数量高达2.7万亿，最早可能在第三季度发布。报道称该时间窗口是知情消息而非确切承诺，但若落地，其推理成本与可部署性将是市场关注焦点，而非单纯参数比拼。

另一家中国公司智谱AI（Z.ai）创始人唐杰已公开表示，计划在2027年前发布一款对标Anthropic模型的开源模型，并可能于年底前面世。其现有GLM-5.2在部分智能体和网络安全测试中已接近美国领先模型，而成本仅为后者约一半。下一代发布有望进一步重置前沿能力的价格门槛。

物理AI赛道：落地日期最明确，考验真实场景

具身智能领域的发布计划最为清晰，因为其成果最终需在工厂等现实环境中接受检验。英伟达的Cosmos 3旨在统一合成世界生成、物理推理与行动模拟，而GR00T N2则将这套技术应用于机器人控制，计划于年底发布。Genesis公司也承诺在年底前将模型部署到客户环境中。这一赛道的核心考验不是视频生成质量，而是机器人能否在陌生环境中成功执行任务。

未来半年发布预测与关键结论

综合公开承诺、测试报告和训练进度，以下是未来六个月的主要发布概率预测：

• 9月前可能的发布：Anthropic的Fable 5.1小版本更新（概率65%）、MiniMax的2.7万亿参数开源模型（概率75%）、SSI的首个模型（概率25%，可能以论文或小范围演示形式）。
• 四季度核心窗口：Meta的Watermelon（概率80%）、智谱的开源对标模型（概率70%）、英伟达GR00T N2（85%）、Cosmos 3（65%）及Genesis的Eno部署（70%）。谷歌Gemini 3.5 Pro（60%）和OpenAI Astra（45%）届时可能已具备发布能力，但未必会全面开放。
• 明年1月至2月：预计将承接四季度未能落地的项目，以及一些无明确时间表的实验室工作。Gemini 4、Grok 5、Mistral下一代大模型等在此窗口发布的概率均低于35%。

三大赛道，三种赢家

未来六个月将不再是一场简单的排行榜竞赛，而是三条并行赛道的比拼：闭源模型的瓶颈在于安全部署（如Astra因能力过强而难以常规发布），开源模型的竞争核心在于经济性（能否以更低成本提供接近前沿性能），物理模型则考验可靠性。这意味着，OpenAI或Meta可能拥有最强的智能体，中国实验室可能开发者最用得起、可控的模型，而英伟达则可能掌握连接模型与机器的关键层。市场最终将根据实际部署价值，而非单纯参数，来选择真正的赢家。

关键要点：

• 团队应做好1月前至少三次模型迁移的准备，保持评估和路由的灵活性。
• 中国开源模型可能引发最大价格冲击，迫使闭源厂商降价或开放更多权限。
• Astra的延迟本身即能力信号，下一阶段前沿瓶颈可能是安全部署而非训练算力。
• 世界模型终于有了可验证的截止日期，GR00T N2和Eno必须在陌生物理环境中成功运行，排行榜无法掩盖失败。

中文翻译：

如果你在工作中使用AI，你所依赖的工具在二月之前可能再次发生变化。OpenAI、谷歌、Meta、Anthropic、几家中国实验室以及一批世界模型初创公司都在准备或据传正在准备新品发布。有些已公布了日期，有些只出现在测试报告、泄露信息或投资者评论中。本期将把这些信号整理成一份实用清单：哪些可能如期发布，哪些可能会跳票，以及哪些发布真正值得你调整计划。

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野外观察
受众已经抢在发布日历之前行动了。在“野外观察”中查看最新的完整动态。

• 一段Fable 5.1泄露视频正以每小时1，678次的观看量增长。传闻汇总还预告了Gemini更新和一个“无审查”版Qwen模型，这生动体现了当前模型猜测的包装方式。观看视频。
• Canva进入摄影与视频类第3名。无论哪个模型赢得竞赛，它都将越来越多地以人们已在使用产品的形式呈现，而非作为一个聊天机器人标签页。查看应用。
• AI视频创作上升了三位。AI视频生成器+创作者工具升至图形与设计类第9名，又一个信号表明更好的视频模型将立即获得需求。查看应用。
• Gauth在教育类上升了两位。以摄像头为先的讲解方式正成为学生的默认模型界面。查看应用。
• Alta以AI衣橱进入生活方式类。获胜的消费级模型可能是用户永远无需指名道姓的那一个。查看应用。

快讯速览
实验室角斗士时代
西方四大实验室各自有四个不同理由，无法公布一个干净的日期。

• OpenAI的下一个模型在等待安全架构的完成，而不是另一次训练运行。Astra是真实的，OpenAI表示其最新评估显示代理编码和网络安全方面取得了巨大进展，以至于无法排除关键能力风险。部分内部工作已暂停，直到更强的管控措施到位。这是队列中最具影响力的模型，也是最难排期的。
• Meta已将12月31日变成了对其AI重建的全民公投。Watermelon，即下一代Muse Spark系列，仍在以远超此前的算力进行训练，预计今年内发布。延期将不仅仅是日历上的滑移；它将重新开启一个问题：Meta的巨额投入和人才挖角是否真的产出了前沿模型。
• 谷歌有两个旗舰模型在管线中，其中一个已经延期。Gemini 3.5 Pro正在测试中，但据报道落后计划数月，而谷歌表示Gemini 4正在进行预训练。可能的顺序是先发布延期的3.5 Pro，之后才是真正的代际跃升，而不是传闻中所期待的Gemini 4惊喜发布。阅读状态。
• Anthropic的传闻堆里包含一个补丁版本、一个登月项目和一个你拿不到的模型。据报道，Fable 5.1已出现在部分账户中，而SemiAnalysis创始人Dylan Patel推测Mythos 2已被用于训练Mythos 3。另外，Anthropic自己的风险报告描述了一个更强的内部Model 2，但该公司不计划发布。这些目击和推测是信号，而非路线图。

DeepSeek的悄然接管
最可信的惊喜可能并非来自美国实验室。

• MiniMax可能在十月之前将2.7万亿参数的开源权重投入市场。路透社报道称，这家中国初创公司正在训练可能是全球最大的开源权重模型，发布时间可能在第三季度。MiniMax拒绝置评，因此请将该时间窗口视为基于知情人士的报道而非承诺。如果它如期发布，最直接的焦点将是推理成本与可部署性，而非参数规模炫耀。阅读报道。
• Z.ai表示，一个Fable级别的开源模型将在年底前发布。创始人唐杰公开表示，他的公司可能会在2027年之前发布一个能与Anthropic的Fable匹敌的开源模型。其当前的GLM-5.2在部分代理和网络安全测试中已接近美国领先模型，而成本大约只有后者的一半。下一次发布可能重新定义前沿能力的价格。

万物自动模式
物理AI实验室拥有最清晰的日期，因为他们的主张最终必须触及工厂车间。

• 英伟达有两个物理AI发布正从两个方向逼近。Cosmos 3旨在统一合成世界生成、物理推理和动作模拟；GR00T N2则将该技术栈转向机器人控制。英伟达表示Cosmos 3即将推出，GR00T N2定于年底发布。重要的基准不是视频质量，而是在陌生房间中的成功动作执行。
• Genesis承诺在年底前将其模型部署到客户环境中。GENE是Eno内部的推理与控制系统，Eno是一款专为长周期工业任务设计的通用机器人。生产和定向客户部署计划在年底前完成。这不是一次新的检查点版本发布，但它可能是检验世界动作模型能否从演示视频走向实战的最干净测试。

六个月发布预测板
以下是AI周刊基于公开承诺、已报道的测试情况、训练状态以及未解决关卡数量给出的编辑概率。这些是预测，而非公司官方指引。

从现在到九月：泄露窗口期
Fable 5.1，65%。账户目击使小版本发布变得可信，而且Anthropic有充分动力改进其公开模型，同时将更危险的能力留在Mythos访问控制之后。预期是一个更好的代理和编程模型，而非新范式。
MiniMax的2.7T开源模型，75%。路透社的时间窗口很具体，该模型据报道正在开发中，而且中国实验室的发布节奏一直被西方观察者低估。风险在于第三季度的API预览被误认为可下载权重。
SSI模型，25%。一位有联系的投资者说是八月。SSI不置一词。论文、有限研究预览或经挑选的合作伙伴演示比公开API更有可能。

十月到十二月：真正的发布窗口
Watermelon，80%。这是最干净的前沿承诺。Meta已将年底日期及其AI重启的公信力与该发布绑定。预期宣传重点将是编程、代理以及Meta用户数据的优势，而非仅仅一个基准测试桂冠。
Z.ai的Fable级别开源模型，70%。唐杰已表示年底前发布，而中国压缩能力差距的速度已超出美国实验室预期。如果该模型以宽松权重和更低推理成本发布，它对开发者的意义可能超过任何登顶排行榜的闭源模型。
GR00T N2，85%；Cosmos 3，65%；Eno部署，70%。物理AI是第四季度最可信的集群。英伟达已为GR00T给出了日期，Cosmos给出了“即将”，Genesis已指明了客户窗口。跳票将表现为访问范围更窄、机器人实体更少或精心选择的环境，而非取消发布。
Gemini 3.5 Pro，60%；Astra，45%。谷歌需要解决性能延期问题。OpenAI需要通过安全与安保关卡。两个模型在十二月之前可能都已具备发布能力，但这不意味着任何一家会广泛开放使用。

一月到二月：延期堆积区
这里是错过的第四季度承诺所去之处，以及那些有产能但没有公开排期的实验室。Thinking Machines明年初开始动用一吉瓦的Vera Rubin算力，但那指向未来的训练，而非二月份的前沿发布。Reflection已在SpaceXAI算力上进行训练，同样没有日期。Gemini 4、Grok 5、Mistral的下一代Large模型、World Labs的下一代Marble系列，以及Runway或谷歌的新旗舰视频模型，在这个六个月窗口期都属于35%以下。不是因为工作没在做，而是因为没有足够强的发布证据能把活动转化为日历日期。

这是三场比赛，而非一个排行榜
通常的叙事问的是哪家实验室将在圣诞节前拥有最聪明的模型。这忽略了即将到来的格局。闭源模型竞赛正变成一个部署问题：Astra可能网络能力过强而无法常规发布，Anthropic正在分离公开系统与受限系统，谷歌必须决定是晚点发货还是等待更干净的跃升。开源模型竞赛正变成一个经济学问题：MiniMax和Z.ai不需要赢得每一个基准测试，如果他们能让接近前沿的能力可下载且便宜得多。物理模型竞赛正变成一个可靠性问题：Cosmos、GR00T和GENE必须将内部世界模型保持足够长的时间，以在受控演示之外完成工作。

这将产生三个不同的赢家。OpenAI或Meta可以拥有最强能力的代理。一家中国实验室可以拥有开发者真正负担得起且能掌控的模型。英伟达可以拥有连接模型与机器的层级。接下来的六个月不会产生一个“最佳模型”。它们将揭示市场到底重视并愿意部署哪种智能。

核心要点

• 为一月之前的至少三次模型迁移做好计划。Fable 5.1、一个中国开源模型和Watermelon的可信度足够高，团队应保持评估和路由的可移植性。
• 最大的价格冲击可能来自中国。一个Fable级别的开源模型不需要赢得每个基准测试，就能迫使闭源模型供应商降价或放宽访问权限。
• Astra的延期本身就是能力信号。下一个前沿瓶颈可能是安全部署，而非训练算力。
• 世界模型终于有了可证伪的截止日期。GR00T N2和Eno必须在陌生物理环境中工作，而排行榜在那里无法掩盖失败。

首发发现
我们的独家新闻猎手在媒体到达之前挖掘到的原始研究。完整解析见链接页面。

• 下一次模型升级可能实际上是一个更好的“鞍具”。在8，135次试验中，程序性代理技能产生了65.7%的提升，而显性知识仅增加了4.5%。检索精度从五个候选技能时的29.6%骤降至一百个候选技能时的3.3%。这篇论文是对以下观念的一个警告：不要把每一次能力跃升都当作新权重——程序、检索和控制流程可能决定了哪个“模型”在实践中感觉最聪明。

值得一读

• 前沿AI预测存在测量问题：对62个系统的审计发现，模型预测经常混合基准测试、算力、发布和专家证据，却没有保持事件记录的准确性。（arXiv）
• 《世界模型功能分类法》：World Labs区分了渲染器、模拟器和规划器，然后解释了为什么真正的奖赏是一个能在三者之间闭合回路的模型。（World Labs）
• Yann LeCun的AMI Labs为世界模型融资10.3亿美元：以学习世界运作方式的模型取代以语言为先的系统的创业论据。（TechCrunch）

等等，什么？

• 十一个词把世界上最神秘的AI实验室变成了八月发布活动。在一场关于瓦数、晶圆和持续学习的播客讨论中，投资者Gavin Baker表示SSI告诉他将在八月发布一个模型。整集节目没有给出模型名称、模态、基准测试、访问计划或具体日期。一个全球传闻周期建立在一个不在那里工作的人的一句话之上。

值得观看
AI从业者目前正在传阅的视频——由AI电视策划。

本周投票
哪次发布最会改变你的2027年计划？

上周，289位读者参与了投票：
五家实验室，五个不同答案。你实际信任哪一个？

哪次发布最会改变你的2027年计划？

本周末见。
Alexis

英文来源：

If you use AI at work, the tools you rely on could change again before February. OpenAI, Google, Meta, Anthropic, several Chinese labs, and a group of world-model startups are all preparing or rumored to be preparing new releases. Some have announced dates. Others have only appeared in testing reports, leaks, or investor comments. This issue sorts those signals into a practical list: what is likely to ship, what will probably slip, and which releases might actually be worth changing your plans for.
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In the Wild
The audience has already front-run the release calendar. See the latest full movement in In the Wild.

• A Fable 5.1 leak video is pulling 1,678 views an hour. The rumor roundup also promises a Gemini update and an "uncensored" Qwen model, which is a neat snapshot of how model speculation now gets packaged. Watch it.
• Canva entered Photo & Video at No. 3. Whatever wins the model race will increasingly arrive inside a product people already use, not as a chatbot tab. View the app.
• AI video creation climbed three places. AI Video Generator + Creator moved to No. 9 in Graphics & Design, another signal that better video models will find demand immediately. View the app.
• Gauth climbed two places in Education. Camera-first explanations are becoming a default model interface for students. View the app.
• Alta entered Lifestyle with an AI closet. The winning consumer model may be the one users never have to name. View the app.
  Quick Hits
  The Lab Gladiator Era
  The four largest Western labs have four different reasons they cannot publish a clean date.
• OpenAI's next model is waiting on a security architecture, not another training run. Astra is real, and OpenAI says its latest evaluations show such large gains in agentic coding and cybersecurity that it cannot rule out critical capability. Some internal work is paused until stronger controls are in place. This is the most consequential model in the queue and the least schedulable.
• Meta has turned December 31 into a referendum on its AI rebuild. Watermelon, the next Muse Spark generation, is still training with vastly more compute and is supposed to arrive this year. A delay would be more than calendar slip; it would reopen the question of whether Meta's spending and talent raid produced a frontier model.
• Google has two flagships in the pipe and one of them is already late. Gemini 3.5 Pro is in testing but reportedly months behind schedule, while Google says Gemini 4 is in pretraining. The likely sequence is a delayed 3.5 Pro release before any true generational jump, not the surprise Gemini 4 launch the rumor accounts want. Read the status.
• Anthropic's rumor stack contains a patch, a moonshot, and a model you cannot have. Fable 5.1 has reportedly appeared in some accounts, while SemiAnalysis founder Dylan Patel theorizes that Mythos 2 has been used to train Mythos 3. Separately, Anthropic's own risk report describes a stronger internal Model 2 that it does not plan to release. The sightings and theory are signals, not a roadmap.
  DeepSeek's Quiet Takeover
  The most credible surprise may not come from a US lab.
• MiniMax could put 2.7 trillion open-weight parameters into the market before October. Reuters reports that the Chinese startup is training what may be the world's largest open-weight model, with a release possible in the third quarter. MiniMax declined to comment, so treat the window as informed reporting rather than a promise. If it lands, the immediate story will be inference cost and deployability, not parameter bragging rights. Read the report.
• Z.ai says a Fable-class open model will arrive before year-end. Founder Jie Tang has publicly said his company will likely ship an open model that rivals Anthropic's Fable before 2027. Its current GLM-5.2 already approaches leading US models on some agentic and cybersecurity tests at roughly half the cost. The next release could reset the price of frontier capability.
  Auto Mode Everything
  The physical-AI labs have the clearest dates because their claims eventually have to touch a factory floor.
• Nvidia has two physical-AI releases approaching from opposite directions. Cosmos 3 is meant to unify synthetic world generation, physical reasoning, and action simulation; GR00T N2 turns that stack toward robot control. Nvidia says Cosmos 3 is coming soon and GR00T N2 is slated for year-end. The important benchmark will not be video quality. It will be successful action in an unfamiliar room.
• Genesis has promised to put its model into customer environments before the year closes. GENE is the reasoning and control system inside Eno, a general-purpose robot designed for long-horizon industrial work. Production and targeted customer deployments are planned by year-end. This is not a fresh checkpoint release, but it may be the cleanest test of whether a world-action model can graduate from a demo reel.
  The Six-Month Release Board
  These are AI Weekly's editorial odds, based on public commitments, reported testing, training status, and the number of unresolved gates. They are forecasts, not company guidance.
  Now through September: the leak window
  Fable 5.1, 65%. The account sightings make a point release believable, and Anthropic has every incentive to improve its public model while keeping more dangerous capability behind Mythos access controls. Expect a better agent and coding model, not a new paradigm.
  MiniMax's 2.7T open model, 75%. The Reuters window is specific, the model is reportedly in development, and Chinese labs are shipping at a cadence Western observers keep underestimating. The risk is that a Q3 API preview gets mistaken for downloadable weights.
  An SSI model, 25%. A connected investor says August. SSI says nothing. A paper, limited research preview, or hand-picked partner demo is more plausible than a public API.
  October through December: the real launch window
  Watermelon, 80%. This is the cleanest frontier promise. Meta has attached both a year-end date and the credibility of its AI reboot to the release. Expect the pitch to focus on coding, agents, and the advantage of Meta's user data, not simply a benchmark crown.
  Z.ai's Fable-class open model, 70%. Tang has said before year-end, and China has already compressed the capability gap faster than US labs expected. If this ships with permissive weights and lower inference cost, it could matter more to developers than whichever closed model tops the leaderboard.
  GR00T N2, 85%; Cosmos 3, 65%; Eno deployments, 70%. Physical AI is the most believable Q4 cluster. Nvidia has given GR00T a date, Cosmos a "soon," and Genesis has named the customer window. Slippage will show up as narrower access, fewer robot bodies, or carefully chosen environments rather than a cancelled launch.
  Gemini 3.5 Pro, 60%; Astra, 45%. Google needs to clear a performance delay. OpenAI needs to clear a safety and security gate. Both models probably exist in release-capable form before December. That does not mean either company will make them broadly available.
  January through February: the rollover pile
  This is where missed Q4 promises go, along with the labs that have capacity but no public schedule. Thinking Machines begins drawing on a gigawatt of Vera Rubin compute early next year, but that points to future training, not a February frontier launch. Reflection is already training on SpaceXAI capacity, also without a date. Gemini 4, Grok 5, Mistral's next Large model, the next Marble generation from World Labs, and new flagship video models from Runway or Google all belong below 35% for this six-month window. Not because the work is not happening. Because there is no release evidence strong enough to turn activity into a calendar.
  This Is Three Races, Not One Leaderboard
  The usual framing asks which lab will have the smartest model by Christmas. That misses the shape of what is coming. The closed-model race is becoming a deployment problem: Astra may be too cyber-capable for ordinary release, Anthropic is separating public and restricted systems, and Google has to decide whether to ship late or wait for a cleaner jump. The open-model race is becoming an economics problem: MiniMax and Z.ai do not need to beat every benchmark if they make near-frontier capability downloadable and much cheaper. The physical-model race is becoming a reliability problem: Cosmos, GR00T, and GENE have to preserve an internal model of the world long enough to complete work outside a controlled demo.
  That creates three different winners. OpenAI or Meta can own the most capable agent. A Chinese lab can own the model developers can actually afford and control. Nvidia can own the layer that connects models to machines. The next six months will not produce one "best model." They will reveal which kind of intelligence the market values enough to deploy.
  Key Takeaways
• Plan for at least three model migrations before January. Fable 5.1, a Chinese open model, and Watermelon are credible enough that teams should keep evaluations and routing portable.
• The biggest price shock may come from China. A Fable-class open model does not need to win every benchmark to force closed-model vendors to cut prices or loosen access.
• Astra's delay is itself a capability signal. The next frontier bottleneck may be secure deployment, not training compute.
• World models finally have falsifiable deadlines. GR00T N2 and Eno have to work in unfamiliar physical environments, where a leaderboard cannot hide failure.
  Found First
  Primary research our scoop hunter surfaced before the press got there. Full write-up on the linked page.
• The next model upgrade may actually be a better harness. Across 8,135 trials, procedural agent skills produced a 65.7% lift while explicit knowledge added only 4.5%. Retrieval precision collapsed from 29.6% with five candidate skills to 3.3% with 100. The paper is a warning against treating every capability jump as new weights: procedure, retrieval, and control flow may decide which "model" feels smartest in practice.
  Worth Reading
• Frontier AI forecasting has a measurement problem: an audit of 62 systems finds that model forecasts often mix benchmark, compute, release, and expert evidence without keeping the event record straight. (arXiv)
• A Functional Taxonomy of World Models: World Labs separates renderers, simulators, and planners, then explains why the real prize is a model that closes the loop between all three. (World Labs)
• Yann LeCun's AMI Labs raises $1.03 billion for world models: the startup case for replacing language-first systems with models that learn how the world works. (TechCrunch)
  Wait, What?
• Eleven words turned the world's most secretive AI lab into an August launch event. In a podcast discussion about watts, wafers, and continual learning, investor Gavin Baker said SSI told him it would release a model in August. The full episode offers no model name, modality, benchmark, access plan, or day. A global rumor cycle has been built on one sentence from someone who does not work there.
  Worth Watching
  The videos AI practitioners are passing around right now — curated on AI TV.
  This week's poll
  Which release would most change your 2027 plans?
  Last week, 289 of you voted:
  Five labs, five different answers. Which one do you actually trust?
  Which release would most change your 2027 plans?
  Back this weekend.
  Alexis