Microsoft使用2026年10月9日引入了拒绝写出段落的模型。 Microsoft-决定1不聊天，它在一次前传中得分一个固定的答案选项列表，双手回放每个选项的概率，然后让开。 该公司将其建在Alibaba的Qwen3.5-9B上方，通过Microsoft铸币局立即提供，并在不久后通过OpenRouter提供，并且已经指向从事件反应到科学发现等内部用途。
它是一个狭义的发布设计，而其他行业则竞相将更大的，聊天级的助手运送出船，而Microsoft则将10月的公告花在了一种为做一件事而构建的模型上：从列表中选择一个答案，快速，并告诉你它有多自信。 对于建造agent管线、路由层和自动核查系统的工程团队来说，这可以说比另一个通用聊天器更有用。
不要错过Google的新科技故事
在 Google app 中添加一次被擦伤，而我们的故事出现在您的新闻建议中。
什么Microsoft -决定1 实际上
今年的AI头条大多是关于产生更多信号的模型：更多符号，更多背景，更多创意输出。 Microsoft-决定一向相反方向移动。 根据该公司自己的公告，该模型阅读了提供的内容，同时阅读了一套预先定义的答案选项，并返回每个选项的校准概率分数，而不是产生开放式文本。 这与GPT-6 Sol, Gemini 4 Argon，或 Claude Opus 5.5 所构建的工作不同
Microsoft在发布为模型命名了4个核心用例：路由，分级，优先级，以及验证，外加它所谓的工作流程控制。 想想需要向合适的团队传递的支持票，需要“是/否”调用用户内容的片段，这些决定量大，意义小，并且正是90亿参数的专家模型能比全尺寸的推理模型更便宜的任务。
区别对于今天拥有大型语言模型的任何人来说都很重要。 一个聊天机器人生成一个响应，你必须分析它，猜测它的信心，并希望它遵循您的格式指令。 一个决定分数模型跳过这个完全解析步骤：输出格式已经是您定义的选项的概率分布。 这是一个较小的，更机械的问题，一个Microsoft在打赌大量生产系统的数量。
模型内部：为什么Qwen3.5-9B作为基座。
引起评论最多的细节不是用法，Microsoft-Decision-1由Qwen3.5-9B进行后期训练，是Alibaba出产的90亿参数的开放重量模型。 登记册直接标出这一选择，将发布设定为Microsoft，依靠中国AI实验室的开放量释放来快速将产品运出大门，同时注意到Microsoft自己声明有意随时间推移模型的基础。
Microsoft一直明确表示这是一个起点，而不是目的地。 在发布的帖子上，称公司：“为了建设Microsoft-Decision-1，我们把Qwen3.5-9B作为快速、单通的决定分数，并很快将重新以其他模型为基础，包括Microsoft AI(MAI)和OpenAI。” 这对一家通过Microsoft创始平台及其OpenAI伙伴关系将数十亿投资到自己模型开发中的公司来说是引人注目的承认。 这表明Qwen基础被选为以狭义，定义明确的任务来营销的速度，而不是作为长期的建筑承诺。
在2026年，使用别人的开放式模型做脚手架并不罕见。 大量较小的、针对特定任务的工具是用这种方式建造的，同样的逻辑驱动了像JetBrains的Mellum2.1编码模型这样的项目，以便在原始规模上选择一个紧凑的参数计数。 值得一提的是，Microsoft是一家拥有自己前沿模型野心的公司，它公开选择竞争对手实验室的重量作为旗舰名产品，然后在前面告诉客户，基础发动机将在日后改变。
基准测试声称：36个测试，近15万个问题。
Microsoft硬靠数字为产品辩护。 称决定1公司通过36个基准测试和约15万个被搁置的问题的比较，实现了最高的准确度，这个尺度意在表示宽度，而不是一个单一的樱桃选取测试。 Microsoft报告在评价中准确度为83.5%，德国的BornCity出口也进行了转发。
速度声称遵循了大而具体数字的相同模式。 工业通讯AI Weekly和AI追踪网站TestingCatalog都报告说，Microsoft 声称决定-1运行速度比它所称的Quyet-1.0-Large快4.5倍，快35倍于GPT-6 Sol. 由Data Studios单独编写，35x数字作为头条框架。 Microsoft自有的发布材料描述出GPT-6 Sol的结果大约是用自有的基准测试测量的35×更低的P50耐用度，而称决定一比H2O-Lightning-4B v1.1快了2.5×，在比较中是跑步。 这是具体的P50测量，而不是通用速度优势，因为系统执行不同的任务：第1个决策得分固定选项，而GPT-6 Sol生成文本。 台湾的"自由时报"报道了85毫秒左右的响应时间，虽然没有具体说明该数字背后的硬件或测试条件。
这才是健康怀疑的所在。 这些是Microsoft自己的基准测试结果，通过该公司的发布材料报告，并由报道公告的渠道转发。 本作被审查的覆盖范围中，没有一个包括独立，第三方复制了36-基准测试比较，4.5x和35x速度乘数，或者85毫升的空想数字。 这并不意味着数字是虚假的，但确实意味着它们应该被解读为销售商报告的业绩声称
根据AI Weekly引述的公司发布摘要，其中98.7%的时间里，在小输入变化下的稳定性是任何使下游系统自动运作的二进制或多选项呼叫的模型的真正关切。 如果一个路由决定因微小的重写而翻转，那就是生产错误等待着发生，因此，一个高稳定性的数字，如果在外部测试中保持的话，将成为工程团队评估模型的真正有用的数据点。
定价：每百万个输入卡，免费输出，0.042美元
Microsoft自己的公告和Microsoft铸币局的文件并没有在为这篇文章审查的资料来源中公布确认的价格。 转而流传的是OpenRouter自己的型号上市，这种定价结构对于一个整个输出都是概率分数的短清单而不是生成文本的模型来说是有道理的，因为产出方面实际上没有什么可计量的。
以这一价格，第1号决定被定位为对高容量的路由和分类工作真正廉价的选择，目前这种任务被引导到较小的通用模型，主要是因为没有人愿意支付完整的模型价格来要求“这是垃圾邮件，是还是不是 ”。 这与推动Anthropic切开Claude的成本逻辑类似。 Haiku今年早些时候定价75%：廉价而快的模型赢得了API交通派的高频、低复杂度片段，即使旗舰模型在技术上也能完成这项工作。
可用性：创建者第一，打开路透关闭后面
Microsoft-Decision-1 发布在Microsoft Foundation上的公开预览中，文档描述它可用于分类，路由，排名，分级，和二进制等决定。 兹克 1Z的发布文章最初将OpenRouter的可用性描述为"快来了"，但OpenRouter自己的模型上市确认该模型在公布后的大约一天内在识别码microsoft/microsoft-决定一下活了下来，OpenRouter贴出"活在OpenRouter上".
与完全开放的Qwen3.5-9B相比，模型的重量并没有在Data Studios上发布，这意味着开发者完全通过主机API访问决定1，这对开放式模型的商业衍生物来说是一种常见的模式，但对于假设基于Qwen的产品的团队来说，这种模式与基础模型一样开放。
在创办公司执行决定1
该模型接受文本并支持最多为32,000个令牌的输入。 它返回预定义选项的校正分数，不生成解释或处理图像，音频，或视频。 需要书面理由或多式联运投入的团队必须将其与通用模式相配合，而不是期望第1号决定涵盖这项工作。
置信分数是应用程序运行的输出部分。 概率本身是API的一部分，而不仅仅是排名分数，应用程序使用信心来决定何时行动，推迟，或请求审查。 一个路由示例是：如果紧急程度高于0.9，优先程度高于1.5，请立即向待命工程师提供页面。 另一个例子设置了下限：如果最高概率低于0.65，则本地政策可以避免降级，保留当前模型或请求人工选择。
文件显示的一个实例是，该模型以98.08%的置信度返回1.9868的加权优先分。 在代表性案例上，90%的预测应该正确9倍于10倍左右，这是标准队伍在自己数据测试时应该持有的分数。 快速起跑需要Python 3.10或后期，因此第一个飞行员可以很小。 因为Microsoft描述定价是动态的，所以在对部署进行分量之前，应该先检查一下目前的铸币局列表。
Nadella的皮奇和Microsoft的内部使用案例
萨特雅·纳德拉(Satya Nadella)亲自撰写了X上的模型推出，写道：“引入Microsoft-决定1，我们快速决策的新模型。 它在结构化决策任务上表现优异，在耐久性和质量上都优于LLMs和其他决策模式。 我们已在Microsoft测试，Microsoft不只是将决定1传递给第三方开发商。 公司首先通过自己的业务工作流程运行。
事件反应是合乎逻辑的第一站：一个决定分数模型可以分辨出警示，分数严重性，或者决定一张票是否需要升级为人，所有任务都完全符合"固定选项加信心分数"形状。 质量控制和科学发现是更广义的声称，除了纳德拉以外，没有被命名为"Microsoft"的行政主管记录了这些工作流程是如何实际使用该模型的，尽管Microsoft所出版的材料此后更详细地描述了其他内部试验。
早期内部审判和实例裁决
Microsoft报告，Xbox Research使用"决定一"将一万多件可自由参加的回馈分解为被预先确定的主题，其质量被描述为与GPT-5具有竞争力，所报告执行速度为80-100倍。 其他Microsoft公布的材料称团队也评价了Copilot反应评估和适应r的模型。
本文由 AI宝库自动抓取并翻译，版权归原作者所有；如需查看原文请点下方链接。
Microsoft used October 9, 2026 to introduce a model that refuses to write you a paragraph. Microsoft-Decision-1 doesn’t chat, summarize, or draft emails. It scores a fixed list of answer options in a single forward pass and hands back a probability for each one, then gets out of the way. The company built it on top of Alibaba’s Qwen3.5-9B, made it available through Microsoft Foundry immediately and through OpenRouter shortly after, and is already pointing to internal uses ranging from incident response to scientific discovery.
It’s a narrow launch by design, and that’s exactly what makes it interesting. While the rest of the industry races to ship bigger, chattier assistants, Microsoft spent its October announcement on a model built to do one thing: pick an answer from a list, fast, and tell you how confident it is. For engineering teams building agent pipelines, routing layers, and automated verification systems, that’s arguably more useful than another general-purpose chatbot.
Don't miss new tech stories on Google
Add Shattered once in the Google app and our stories show up in your news suggestions.
What Microsoft-Decision-1 Actually Does
Most AI headlines this year have been about models that generate more: more tokens, more context, more creative output. Microsoft-Decision-1 moves in the opposite direction. According to the company’s own announcement, the model reads supplied content alongside a set of predefined answer options and returns a calibrated probability score for each one, instead of generating open-ended text. That’s a meaningfully different job than what GPT-6 Sol, Gemini 4 Argon, or Claude Opus 5.5 are built to do.
Microsoft named four core use cases for the model at launch: routing, classification, prioritization, and verification, plus what it calls workflow control. Think of a support ticket that needs to be routed to the right team, a piece of user content that needs a yes/no moderation call, or an AI agent’s output that needs a pass/fail check before it’s allowed to proceed. Those are high-volume, low-latency decisions, and they’re exactly the kind of task that a 9-billion-parameter specialist model can do more cheaply than a full-sized reasoning model.
The distinction matters for anyone building with large language models today. A chatbot generates a response and you have to parse it, guess at its confidence, and hope it followed your formatting instructions. A decision-scoring model skips that parsing step entirely: the output format is already a probability distribution over options you defined. That’s a smaller, more mechanical problem, and it’s one Microsoft is betting plenty of production systems have in volume.
Inside the Model: Why Qwen3.5-9B as the Base
The detail that generated the most commentary wasn’t the use case, it was the foundation. Microsoft-Decision-1 is post-trained from Qwen3.5-9B, a 9-billion-parameter open-weight model from Alibaba. The Register flagged the choice directly, framing the launch as Microsoft leaning on a Chinese AI lab’s open-weight release to get the product out the door quickly, while noting Microsoft’s own stated intent to migrate the model’s foundation over time.
Microsoft has been explicit that this is a starting point, not a destination. In its launch post, the company said: “To build Microsoft-Decision-1, we post trained Qwen3.5-9B for fast, single-pass decision scoring and will soon rebase it on other models, including Microsoft AI (MAI) and OpenAI.” That’s a notable admission for a company that has poured billions into its own model development through the Microsoft Foundry platform and its OpenAI partnership. It suggests the Qwen base was chosen for speed to market on a narrow, well-defined task, not as a long-term architectural commitment.
Using someone else’s open-weight model as scaffolding isn’t unusual in 2026. Plenty of smaller, task-specific tools get built this way, the same logic that drove projects like JetBrains’ Mellum2.1 coding model to pick a compact parameter count over raw scale. What’s notable here is that it’s Microsoft, a company with its own frontier model ambitions, publicly choosing a rival lab’s weights for a flagship-named product, then telling customers up front that the underlying engine will change later.
The Benchmark Claims: 36 Tests, Nearly 150,000 Questions
Microsoft is leaning hard on numbers to justify the product. The company says Decision-1 achieved the highest accuracy across a comparison spanning 36 benchmarks and approximately 150,000 held-out questions, a scale meant to signal breadth rather than a single cherry-picked test. Microsoft reported 83.5 percent accuracy across that evaluation, a figure the German outlet BornCity also relayed.
Speed claims followed the same pattern of big, specific numbers. Industry newsletter AI Weekly and the AI-tracking site TestingCatalog both reported that Microsoft claims Decision-1 runs 4.5 times faster than a model it calls Quyet-1.0-Large, and 35 times faster than GPT-6 Sol. A separate write-up from Data Studios led with the 35x figure as its headline framing. Microsoft’s own launch material describes the GPT-6 Sol result as approximately 35× lower P50 latency, measured on its own benchmark, and says Decision-1 was 2.5× faster than H2O-Lightning-4B v1.1, the runner-up in its comparison. That is a specific P50 measurement rather than a general-purpose speed advantage, since the systems perform different tasks: Decision-1 scores fixed options while GPT-6 Sol generates text. Taiwan’s Liberty Times reported response times in the neighborhood of 85 milliseconds, though it didn’t specify the hardware or test conditions behind that number.
Here’s where a healthy dose of skepticism belongs. These are Microsoft’s own benchmark results, reported through the company’s launch materials and relayed by outlets covering the announcement. None of the coverage reviewed for this piece includes an independent, third-party reproduction of the 36-benchmark comparison, the 4.5x and 35x speed multipliers, or the 85-millisecond latency figure. That doesn’t make the numbers false, but it does mean they should be read as vendor-reported performance claims rather than peer-reviewed results, at least until someone outside Microsoft runs the same tests.
One figure got less attention but may matter more in production: Microsoft says the model’s decisions were “perturbation-stable” 98.7 percent of the time, according to the company’s launch summary cited by AI Weekly. Stability under small input changes is a real concern for any model making binary or multi-option calls that downstream systems act on automatically. If a routing decision flips because of a trivial rewording, that’s a production bug waiting to happen, so a high stability number, if it holds up under outside testing, would be a genuinely useful data point for engineering teams evaluating the model.
Pricing: $0.042 per Million Input Tokens, Free Output
Microsoft’s own announcement and Microsoft Foundry documentation don’t publish a confirmed price in the sources reviewed for this article. What’s circulating instead comes from OpenRouter’s own model listing and was picked up by AI Weekly and the Italian outlet Pasquale Pillitteri: $0.042 per million input tokens, with output free. That pricing structure makes sense for a model whose entire output is a short list of probability scores rather than generated text, since there’s effectively nothing to meter on the output side.
At that price, Decision-1 is positioned as a genuinely cheap option for high-volume routing and classification work, the kind of task that currently gets routed to smaller general-purpose models mostly because nobody wants to pay full-model prices to ask “is this spam, yes or no.” It’s a similar cost logic to what pushed Anthropic to cut Claude Haiku pricing by 75 percent earlier this year: cheap, fast models win the high-frequency, low-complexity slice of the API traffic pie, even when a flagship model would technically do the job too.
Availability: Foundry First, OpenRouter Close Behind
Microsoft-Decision-1 launched in public preview on Microsoft Foundry, where documentation describes it as available for classification, routing, ranking, grading, and binary decisions. Microsoft’s launch post initially described OpenRouter availability as “coming soon,” but OpenRouter’s own model listing confirmed the model had gone live under the identifier microsoft/microsoft-decision-1 within roughly a day of the announcement, with OpenRouter posting that it was “live on OpenRouter.”
Weights for the model aren’t being distributed, based on reporting from Data Studios, meaning developers access Decision-1 exclusively through hosted APIs rather than downloading and self-hosting it, a contrast with the fully open Qwen3.5-9B it’s built on. That’s a common pattern for commercial derivatives of open-weight models, but it’s worth flagging for teams that assumed a Qwen-based product would come with the same openness as the base model.
Working With Decision-1 in Foundry
The model accepts text and supports inputs up to 32,000 tokens. It returns calibrated scores for predefined options, and it does not generate explanations or handle images, audio, or video. Teams that need a written rationale or multimodal input will have to pair it with a general-purpose model rather than expect Decision-1 to cover that work.
The confidence score is the part of the output that applications act on. The probability itself is part of the API, not just a ranking score, and applications use confidence to decide when to act, defer, or ask for review. One routing example reads: if urgent is above 0.9 and priority is above 1.5, page the on-call engineer immediately. Another example sets a floor: if the top probability is below 0.65, a local policy can avoid downgrading, keep the current model or request manual selection.
A worked example in the documentation shows the model returning a weighted priority score of 1.9868 with 98.08% confidence. A 90% prediction should be right about nine times out of 10 on representative cases, which is the standard teams should hold the scores to when they test them on their own data. The quickstart needs Python 3.10 or later, so a first pilot can be small. Because Microsoft describes pricing as dynamic, it is worth checking the current Foundry listing before sizing a deployment.
Nadella’s Pitch and Microsoft’s Internal Use Cases
Satya Nadella personally introduced the model on X, writing: “Introducing Microsoft-Decision-1, our new model for fast decision-making. It delivers top performance on structured decision tasks, outperforming both LLMs and other decision models in latency and quality. We’re already testing it across Microsoft for everything from incident response and quality control to scientific discovery.” That’s a specific, named set of internal applications, three in total, and it signals that Microsoft isn’t just shipping Decision-1 to third-party developers. The company is running it through its own operational workflows first.
Incident response is a logical first stop: a decision-scoring model could triage alerts, score severity, or decide whether a ticket needs to escalate to a human, all tasks that fit the “fixed options plus confidence score” shape perfectly. Quality control and scientific discovery are broader claims, and no named Microsoft executive beyond Nadella has gone on record describing how those workflows actually use the model, although Microsoft-published material has since described other internal trials in more detail.
Early Internal Trials and Example Decisions
Microsoft reported that Xbox Research used Decision-1 to categorize more than 10,000 pieces of open-ended feedback into predefined themes, with quality described as competitive with GPT-5 and execution reported as 80–100 times faster. Other Microsoft-published material says teams also evaluated the model for Copilot-response assessment and adaptive r
Auto-fetched and translated by AI Gem Box. All rights belong to the original author.