Microsoft Announces Judgment-Specialized AI Model "Microsoft-Decision-1" | 35x Faster Than GPT-6 for Business Efficiency
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On October 9, 2026, Microsoft announced "Microsoft-Decision-1," a judgment-specialized AI model. Rather than generating text like ChatGPT, it selects the best option from a fixed set of choices and returns a probability score.
Imagine a customer support scenario where the system must determine: "Is this inquiry a billing question, a technical issue, or an account problem?" A conventional AI would explain the answer in a lengthy passage. Microsoft-Decision-1 responds numerically — for example, "Billing: 80%, Technical: 15%, Account: 5%." Instead of text for humans to read, it returns a judgment result that systems can directly consume.
Microsoft announced on its official blog that the new model is now available through Microsoft Foundry and OpenRouter.
Microsoft-Decision-1 is built on "Qwen3.5-9B," an open-source AI model developed by Alibaba. Rather than building from scratch, Microsoft chose to apply additional training on top of an existing high-quality model.
The "9B" refers to the model having 9 billion parameters (the elements it learns from). This is relatively compact for a large language model (LLM), making it faster and more cost-effective to run.
Pricing is $0.042 per million input tokens (approximately 750,000 characters), with output being free. There is no cost to receive judgment results. The pricing is roughly on par with the competing OpenAI Jev.
According to Microsoft's announcement, Microsoft-Decision-1 achieved 83.5% accuracy across 36 benchmarks (performance measurement tests) — the highest figure among competing models.
Its speed is remarkable. It has been reported to make judgments approximately 35 times faster than OpenAI's large-scale model "GPT-6 Sol." The median processing time is 85 milliseconds — shorter than the time it takes a human to blink (approximately 100–150 milliseconds).
That said, this speed comparison is not without debate. GPT-6 Sol is a general-purpose model capable of not only making judgments but also generating lengthy text. There is an element of comparing models with different intended uses. Nevertheless, it is undeniably far faster when it comes to judgment tasks specifically.
The judgment-specialized AI model space has seen rapidly intensifying competition since early 2026. TypeSafe AI's "Jev" led the way, becoming publicly available in the first half of 2026. OpenAI followed by announcing its "Decisions API" at DevDay 2026 on September 30, and Microsoft entered on October 9 with Microsoft-Decision-1.
Jev is a model designed exclusively for judgment and is structurally incapable of generating free-form text. OpenAI's Decisions API, on the other hand, constrains its large-scale model Luna to handle only judgment tasks. Microsoft-Decision-1 sits somewhere in between, taking a compact base model and specializing it for judgment.
In terms of performance, Jev's median processing time has been reported at 210 milliseconds. Microsoft-Decision-1's 85 milliseconds is more than twice as fast. However, since measurement environments and conditions differ, independent verification is awaited.
Real-world use cases have also been reported. According to Microsoft's internal tests, the Xbox division used the model to label more than 10,000 pieces of feedback. Quality was reportedly on par with GPT-6 Sol, while speed was more than 14 times faster and cost was reduced to 1/200th.
In customer support classification tasks, the model achieved 93% accuracy in areas such as tagging, routing, urgency assessment, and spam detection. Meanwhile, more complex tasks — such as order inquiries and drafting reply messages — remain the domain of conventional text-generating AI. The takeaway is that dividing responsibilities between judgment and generation is the most efficient approach.
Other use cases include AI agents (autonomous AI systems) determining what to do next, data labeling, content filtering, and relevance evaluation of search results.
Why are judgment-specialized AI models emerging in rapid succession right now? The backdrop is the arrival of the AI agent era.
Conventional ChatGPT was built for conversing with humans. Recently, however, development of "AI agents" — AI that autonomously carries out tasks — has accelerated. Agents do not need to report back to humans; they simply need to decide on the next action quickly and pass it to the system.
Standard LLMs increase processing time in proportion to the length of the text they generate. Judgment-specialized models return only a probability score in a single computation, keeping response time constant. This is why they can handle large volumes of judgments at high speed.
Furthermore, since results are returned in JSON format (a data format easily readable by programs), downstream systems can process them directly. There is no need to interpret text, which also reduces errors.
What does this development mean for Japanese companies?
First, the cost of operational efficiency improvements falls. Classification tasks previously handled by high-performance models like GPT-4 can be replaced with faster, cheaper alternatives — for example, email sorting, inquiry prioritization, and data validation as routine judgment tasks.
A key consideration when implementing the model is determining the acceptable tolerance for incorrect judgments in advance. Microsoft recommends setting a confidence threshold that takes into account the cost of false positives (flagging something as a match when it isn't) and false negatives (missing something that should have been flagged). It is also important to have a "no judgment" option available for cases where the model lacks sufficient confidence.
Regarding Japanese language support, Qwen3.5-9B was primarily trained on Chinese and English, but has been reported to handle Japanese to some degree. However, in actual production environments, accuracy validation for Japanese-specific expressions will likely be necessary.
The emergence of Microsoft-Decision-1 signals a major shift in the AI industry: a transition from a single AI that does everything to an era where the optimal AI is selected for each specific use case.
Large-scale models like GPT-6 for text generation; specialized models like Microsoft-Decision-1 or Jev for judgment; dedicated models for image generation. As this division of roles advances, overall system cost and speed will be optimized.
The fact that Microsoft, OpenAI, TypeSafe AI, and others announced judgment-specialized models around the same time underscores the importance of this domain. Going forward, similar models from other companies are sure to appear, intensifying competition further.
For Japanese companies, testing these new tools early and identifying where they can be integrated into internal business workflows will be a source of competitive advantage. The wave of judgment-specialized AI has only just begun.
This article is a cross-post from AI Friends.
