Microsoft just released Microsoft-Decision-1, a decision-scoring model that returns calibrated probabilities instead of text. It is post-trained from Qwen3.5-9B and built for routing, classification, verification and agent control.

The core idea is single-pass decision scoring. You give it a situation, a question and a fixed set of options. It returns one calibrated probability per option, so your app can act, defer or escalate in a single call.

In Microsoft’s 36-benchmark comparison (147,137 questions), it scored 83.5% average accuracy, ahead of Quyet-1.0-Large at 81.9%. It ran at 85 ms p50 latency, 35x quicker than GPT-6 Sol. The trade-off: it is text-only, gives no explanations, and its weights are closed. Calibration (92.2) trails Quyet’s 93.1.

At $0.042 per 1M input tokens with free output, it is priced for agent loops that make thousands of small decisions......

Full analysis: https://marktechpost.com/2026/10/09/microsoft-ai-releases-microsoft-decision-1-a-qwen3-5-9b-decision-scoring-model/

Model: https://ai.azure.com/catalog/models/Microsoft-Decision-1

Announcement: https://commandline.microsoft.com/microsoft-decision-1-model-foundry/