Microsoft
Latest stories covering Microsoft, with summaries and links to related coverage.
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Models in this coverage
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- Claude Opus 5.5
Anthropic
· Closed
Anthropic’s model for complex reasoning and longer agent tasks. Built for work such as large codebase changes, code review, and multi-step problem solving.
1 story · 1 report on this topic
- Gemini 4 Argon
Google
· Closed
Google’s reasoning model for software engineering, mathematical and scientific problems, and multi-step agents. Designed to maintain context across longer tasks.
1 story · 1 report on this topic
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Latest stories
Microsoft Decision-1: 9B Qwen Model Launch Explained
tool
21 min read
Microsoft introduced Microsoft-Decision-1 on October 9, 2026, a specialized model that scores predefined answer choices rather than generating text. Built by post-training Alibaba’s 9-billion-parameter open-weight Qwen3.5-9B model, it returns calibrated probability scores for tasks including routing, classification, prioritization, verification and workflow control. It supports text inputs of up to 32,000 tokens but does not provide written explanations or process images, audio or video. Microsoft said future versions will be rebased on Microsoft AI and OpenAI models. The model launched in public preview through Microsoft Foundry and became available on OpenRouter as microsoft/microsoft-decision-1. Its weights are not distributed, limiting use to hosted APIs. OpenRouter and secondary reports list pricing at $0.042 per million input tokens with no output-token charge. Microsoft said Decision-1 achieved 83.5% accuracy across 36 benchmarks containing roughly 150,000 held-out questions, and reported approximately 35 times lower P50 latency than GPT-6 Sol and 4.5 times faster performance than Quyet-1.0-Large; those results have not been independently reproduced. Microsoft also reported 98.7% perturbation stability and said it is testing the model internally for incident response, quality control and scientific discovery. Xbox Research reportedly used it to categorize more than 10,000 feedback items into predefined themes.
New AI Is More Than Twice as Good at Diagnosing Diseases as Doctors
research
3 min read
Microsoft’s AI Diagnostic Orchestrator, or MAI-DxO, achieved 83.2% diagnostic accuracy on complex cases from The New England Journal of Medicine, compared with 36.1% for generalist physicians using reference materials. Physicians without those materials scored about 22%. In a separate emergency-department admissions test, MAI-DxO scored 80.3%, versus 46.8% for generalist physicians. Microsoft reported its strongest results with OpenAI’s GPT-5 and additional improvements with GPT-5.5. MAI-DxO begins with a patient’s chief complaint, then sequentially selects follow-up questions and diagnostic tests before reaching a conclusion. It assigns AI agents roles including proposing diagnoses, choosing tests, challenging assumptions, and weighing costs. Microsoft said simulated-case estimates indicated the system reached diagnoses using fewer tests and procedures at roughly half to one-third of the cost, though those estimates were not actual medical expenses. The findings, first posted as an arXiv preprint last year, have been published in Science. Microsoft AI chief Mustafa Suleyman said real-world use would require clinical testing. Dr. Dominic King, Microsoft’s AI health lead, said testing was expected to begin at Beth Israel Deaconess Medical Center in Boston within months. The researchers said the performance gap would likely narrow against specialist physicians rather than generalists, and medical experts have said diagnostic benchmark performance does not establish that AI should replace doctors.