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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsMicrosoft-Decision-1 is a decision-scoring model for bounded choices: it returns structured selections, probabilities, or scores that software can use to route an AI agent’s next step. Microsoft says agents can use it to decide whether to continue, stop, retry, or hand work to a person or another system. It is not designed to write explanations or other free-form text.
What Microsoft-Decision-1 does
Microsoft announced Microsoft-Decision-1 on October 9, 2026. The model is aimed at fast decision scoring, routing, classification, prioritization, verification, and workflow control. Microsoft says it is available in Microsoft Foundry and through OpenRouter. Microsoft’s announcement describes choices such as selecting a next step from a known set, picking a model for a task, or applying a rubric to an AI response or agent action.
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The key distinction is that the application defines the options. Rather than asking for an open-ended answer, a developer can ask the model to assess a state and choose among known outcomes. Potential uses Microsoft lists include search relevance, safety screening, computer and user-interface use, robotics, and scientific screening.
How an agent can use its scores
A decision model can sit between an agent’s proposed action and the next workflow step. For example, an agent might propose retrying a failed operation; a decision model could score “retry,” “stop,” and “hand off,” after which the application applies its own rules for acting on the result. The model supplies a structured signal, while workflow code determines what happens next.
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Microsoft Learn documents requests that provide a text or JSON state plus one or more questions. Supported question patterns include a yes-or-no probability, a choice among named options with probabilities, and a score on an ordered scale. The result is numerical and does not include a written rationale. The Microsoft Learn guide shows version 1 deployed in Foundry and called through the Microsoft provider’s decision endpoint using the deployment name.
How it differs from a generative model
A generative model is the better fit when the task is to create, summarize, rewrite, explain, or reason through an open-ended request. Microsoft-Decision-1 is intended for a defined decision whose possible outputs are known in advance. The two approaches can be combined: a generative model can propose an action or draft content, and a decision model can score whether to proceed, route the work, or request review.
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- Use a decision model when an application needs a repeatable selection, probability, or score from predefined options.
- Use a generative model when the user needs prose, synthesis, explanation, or an answer that cannot be reduced to a fixed choice.
- Combine them when generated work needs a structured gate or routing decision before the workflow advances.
What Microsoft reports about performance
Microsoft says Microsoft-Decision-1 achieved the highest accuracy in its comparison of 36 benchmarks, covering nearly 150,000 questions across benchmarks it says were kept blind from training. Microsoft also reports that it was 2.5 times quicker than H2O-Lightning-4B v1.1, the runner-up, and 35 times quicker than GPT-6 Sol in its measurements. These are results reported by Microsoft, not independently verified findings in the sources cited here. Performance on a particular application may differ, so published comparisons do not replace testing on representative workload data. Microsoft’s announcement describes the benchmark results.
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Microsoft identifies Microsoft Foundry and OpenRouter as access routes. Its deployment guide covers model version 1 in Foundry, with GlobalStandard and selected-region DataZoneStandard deployment types. Foundry deployment requires an Azure subscription with a valid payment method, Foundry access and a project, deployment permissions, and authentication. The deployment interface provides pricing information; Microsoft says terms and prices vary by provider and deployment type, so there is no single universal rate established here. See the deployment instructions and Foundry’s model overview.
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- Realistic Movements: 12 powerful servos enable 32 actions, including walking, sitting, standing, shaking its head, wagging its tail, and performing playful tricks, closely mimicking a real and providing an engaging experience
- Rich Sensor Suite for Interactive Experiences: features ultrasonic, touch, gyroscope, sound, camera, speaker and microphone. These provide it with advanced hearing, vision, and touch, enabling it to see, detect obstacles, respond to touch, and recognize sounds, making interactions highly engaging
- Engaging Interactions with ChatGPT-4o: with ChatGPT-4o enables voice interactions and visual recognition, making it smarter and more responsive. Users can have natural conversations, solve math problems via the camera, and interpret gestures, creating diverse and fun interactions
- Comprehensive Learning Resources and Support: offers detailed online documentation, video tutorials, prompt technical support, and an active forum community, ensuring beginners can easily complete all projects and enjoy a great experience
How to use decision scores responsibly
A probability or score is not an explanation. Microsoft recommends setting confidence thresholds with the costs of false positives and false negatives in mind, and allowing abstention when the system should not be forced to choose. A low-confidence result can trigger a retry, another model, or human review rather than automatic action.
- Test accuracy and calibration on cases representative of the application, not only broad benchmark results.
- Check whether changing the order of answer options changes the scores or selected option.
- Monitor errors and unfair disparities after deployment, and revisit thresholds as conditions change.
- For consequential decisions involving people, use the model as decision support with meaningful human review, not as the sole decision-maker.
The model’s practical value depends on whether a structured decision improves the workflow enough to justify its latency, cost, data-processing requirements, and safeguards. Compare it with alternatives on the same tasks, using the same representative cases and criteria.
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