Microsoft AI’s MAI-Base-2 base model

In a Lab Notes post on Oct 15, 2026, Microsoft AI described MAI-Base-2, the base (pre-trained, not yet post-trained) model behind its next generation of in-house models. It is a sparse mixture-of-experts with 2.8 trillion total and 80 billion active parameters. It was trained on a 50-trillion-token mixture that includes images from the start, on 16,000 Nvidia GB200 GPUs over 38 days in August and September. Microsoft stresses that its data is “organic” and not distilled from other models.

Microsoft measures quality as bits-per-byte on held-out text, its own unpublished code and commissioned STEM and Q&A sets. On that basis it says MAI-Base-2 beats every open-weight model it tested. It puts the model at 4.2–12.3 times more FLOPs-efficient in pre-training than DeepSeek-V4-Pro-Base, the strongest open base model it could test, and 6.3–12.7 times more than its own MAI-Base-1. The post does not list per-benchmark scores in text; they are in figures.

Business Insider then reported that Mustafa Suleyman told employees MAI-Base-2 is a stronger base model than the OpenAI model behind GPT-5.6, trained with about a quarter of the compute. This entry relies on secondary coverage of that report, since the Business Insider article was not read directly. The comparison rests on Microsoft’s own information about OpenAI’s models, and OpenAI did not comment.

Under the restructured Microsoft–OpenAI deal, Microsoft can build frontier models of its own. This is the first time it has given detailed pre-training numbers at frontier scale (2.8T parameters, ~16k GB200s) and said publicly that its base model beats its partner’s. All efficiency figures are Microsoft’s own, measured on evaluation sets it chose and has not published, so they are unverified. The model is not available yet. Microsoft says it plans to license weights to trusted partners rather than release them openly.