Microsoft's new MAI models
Microsoft has introduced two new text LLMs, MAI-Thinking-1 and MAI-Code-1-Flash, aimed at enhancing performance and reducing costs. The MAI-Thinking-1 model features 35 billion parameters and is currently available to select early partners, while MAI-Code-1-Flash is designed for GitHub Copilot users. Both models are built using clean and commercially licensed data, raising questions about their training sources.
- ▪Microsoft announced two new text LLMs: MAI-Thinking-1 and MAI-Code-1-Flash.
- ▪MAI-Thinking-1 has 35 billion parameters and is available to select early partners.
- ▪MAI-Code-1-Flash is purpose-built for GitHub Copilot and VS Code, rolling out to individual users.
2 outlets in our directory ran this story, first to last over 6 hours. All of the coverage we found sits in one bucket: centre. That one-sidedness is itself worth noticing.
Simon Willison files mainly under blogs. We currently carry 73 of its stories.
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| Original publisher | Simon Willison's Weblog |
| Canonical URL | https://simonwillison.net/2026/Jun/2/microsofts-new-models/#atom-everything |
| Publication time | 2026-06-02T22:21:52+00:00 |
| Retrieval time | 2026-06-02T22:46:18.190Z |
| Last seen | 2026-06-02T22:46:18.190Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | -hbER003D4Sh · 2 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Microsoft announced two new text LLMs this morning - MAI-Thinking-1 (reasoning, 35B parameters, available to "select early partners") and MAI-Code-1-Flash (5B parameters, "purpose-built for GitHub Copilot and VS Code to deliver high performance and lower cost [...] rolling out to GitHub Copilot individual users in Visual Studio Code"). I've not been able to try either of them just yet. It's very interesting to see Microsoft releasing models with such low parameter counts, especially given how expensive larger models are to access right now. They claim MAI-Thinking-1 "is preferred to Sonnet 4.6 in our blind human side-by-side evaluations", which is impressive for a 35B model seeing as I frequently run models larger than that on my own laptop.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Simon Willison's Weblog.
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