Microsoft AI Releases Decision-1 Model Based on Qwen3.5-9B

Microsoft has released Microsoft-Decision-1, a novel decision-scoring model designed for tasks such as routing, classification, verification, and agent control. Unlike generative AI models that produce text, Microsoft-Decision-1 is engineered to return a calibrated probability for each predefined answer option. This approach aims to provide outputs that software can act upon immediately, positioning decision models as a new category of AI.

The model is post-trained from Alibaba's Qwen3.5-9B, though its exact parameter count has not been disclosed. Microsoft-Decision-1 features a substantial 32,768-token context window. It is currently accessible exclusively through hosted APIs, specifically within Microsoft Foundry and via OpenRouter on Azure. The model does not offer open weights or quantized variants, and details regarding its hardware specifications are not public. Microsoft has indicated plans to rebase future versions of its decision models on architectures from MAI and OpenAI.

In performance evaluations conducted by Microsoft across 36 benchmarks, Microsoft-Decision-1 achieved the highest average accuracy, reaching 83.5%. This performance metric places it ahead of other models, such as Quyet-1.0-Large, which recorded an average accuracy of 81.9%. The model also demonstrated a p50 latency of 85 milliseconds. While its average accuracy was superior, its calibration score was 92.2%, ranking second to Quyet-1.0-Large's 93.1%. Microsoft-Decision-1 is strictly text-only and does not provide explanations for its outputs. The model's output format is JSON, supporting various question types including yes/no, multiple-choice, rating, classification, and the grading of AI responses and proposed agent actions. It also includes explicit abstention options, such as 'cannot tell'.

The cost-effectiveness of Microsoft-Decision-1 is highlighted, with input tokens priced at $0.042 per million and free output tokens. However, a significant drawback noted is the closed nature of its weights. The training data for Microsoft-Decision-1 comprised public datasets acquired under Microsoft's Open Data process, supplemented with synthetic data. OpenRouter has stated that the model's weights are subject to continuous updates while maintaining a stable API structure. The decision model framework is intended to streamline AI integration into software workflows by providing direct, actionable scores rather than free-form text.