Today we’re introducing Microsoft-Decision-1, our new model for fast decision-scoring, available in Microsoft Foundry and through OpenRouter. This model is designed for routing, classification, prioritization, verification, and workflow control, making it easier to incorporate decision intelligence into existing applications, agents, and workflows in a secure, trusted environment. Microsoft-Decision-1 delivers top performance in latency and quality on structured decision tasks to outperform both LLMs and other decision models.
Microsoft-Decision-1 achieved the highest accuracy in our 36-benchmark comparison, spanning nearly 150,000 questions across benchmarks kept blind from training. And in our benchmarking, it was the fastest measured: 2.5 times quicker than H2O-Lightning-4B v1.1, the runner-up, and 35 times quicker than GPT-6 Sol.
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. When given a fixed set of answer options, Microsoft-Decision-1 provides a calibrated probability score for each option. The model supports yes/no, multiple-choice, and rating options, as well as rubric-based grading of AI responses and agent actions, all through a simple structured API call.
Microsoft-Decision-1 P50 latency is ~35x faster than GPT-6 Sol. We evaluated Microsoft-Decision-1 across dozens of benchmarks kept blinded from training, spanning routing, ranking, long context, multilingual and out-of-distribution tasks, reasoning, and safety. Microsoft-Decision-1 changes its decision on 1.3% of perturbations on average with zero flips when option descriptions are paraphrased or when options are reversed or shuffled.
Developers can get started with Microsoft-Decision-1 today in Microsoft Foundry and through OpenRouter. Input tokens cost $0.042 USD per million tokens. Output tokens are free.
Internal use cases cited include Xbox Research theme labeling, Copilot response quality grading, on-call incident knowledge retrieval, and Microsoft Discovery adaptive replanning for scientific experiments.
Potential use cases include agent controls, model routing, skill-based decisions, data labeling, AI judging, intent analysis, incident response routing, data validation, recommendations, search relevance, content classification, code scanning, safety screening, computer/UI use, robotics, and scientific discovery screening.