Microsoft has introduced Microsoft-Decision-1, a new artificial intelligence model designed specifically for decision-scoring tasks such as routing, classification, prioritization, verification, and workflow control. Unlike large language models that generate text or reason through complex problems, decision models produce structured outputs that software can act on immediately. The model was announced by Achint Srivastava, Vice President of Software Engineering in Microsoft's Office of the CTO, and is made available through Microsoft Foundry and OpenRouter for developers to integrate into applications, agents, and automated workflows requiring fast, reliable decisions.
The image shows the promotional card used in Microsoft's announcement of the Decision-1 model, which is described in the article as being introduced by Achint Srivastava and available via Microsoft Foundry and OpenRouter.
According to Microsoft's announcement, Microsoft-Decision-1 achieves top performance in both latency and quality on structured decision tasks. In a 36-benchmark comparison that kept nearly 150,000 questions blind from training, the model attained the highest accuracy score. Regarding speed, Microsoft reports that Decision-1 operates 4.5 times faster than Quyet-1.0-Large, the next-best performer in those benchmarks, and 35 times faster than GPT-6 Sol. These metrics position the model for use in time-sensitive scenarios where decisions must be made rapidly and reliably, such as routing requests, classifying inputs, prioritizing tasks, verifying outcomes, or controlling workflow processes.
The hero image accompanies the article's discussion of the Decision-1 model's performance metrics, including its speed and accuracy advantages over other models.
While the announcement highlights speed and accuracy advantages, it does not disclose technical details such as the model's architecture, size, or training data. This omission leaves open questions about how Decision-1 compares to alternative decision models like Jev in real-world deployments, particularly regarding cost, price competitiveness, and practical performance beyond the controlled benchmark environment. Without further information, developers must rely on Microsoft's reported benchmark results when evaluating the model for their own use cases.