Microsoft releases MAI-Thinking-1 reasoning model at 35B parameters, MAI-Code-1-Flash for GitHub Copilot
Microsoft announced two new language models: MAI-Thinking-1, a 35B parameter reasoning model available to select early partners, and MAI-Code-1-Flash, a 5B parameter coding model rolling out to GitHub Copilot individual users in VS Code. Both models were trained on commercially licensed data without distillation from third-party models.
Microsoft releases MAI-Thinking-1 reasoning model at 35B parameters, MAI-Code-1-Flash for GitHub Copilot
Microsoft announced two new language models on June 2nd, 2026: MAI-Thinking-1, a 35B parameter reasoning model, and MAI-Code-1-Flash, a 5B parameter coding model designed specifically for GitHub Copilot and VS Code.
MAI-Thinking-1: 35B parameter reasoning model
MAI-Thinking-1 is currently available only to select early partners. According to Microsoft, the model "is preferred to Sonnet 4.6 in our blind human side-by-side evaluations."
Microsoft claims the model was "trained from the ground up on enterprise grade, clean and commercially licensed data, without distillation from third-party models." Pricing and broader availability have not yet been disclosed.
MAI-Code-1-Flash: 5B parameters for coding
MAI-Code-1-Flash is described as "purpose-built for GitHub Copilot and VS Code to deliver high performance and lower cost." The model is rolling out to GitHub Copilot individual users in Visual Studio Code.
Microsoft states the model is "built end-to-end by Microsoft using clean and appropriately licensed data." Specific details about the training data sources have not been provided.
Technical specifications
- MAI-Thinking-1: 35B parameters, reasoning-focused
- MAI-Code-1-Flash: 5B parameters, code-specialized
- Training data: Both models claim to use commercially licensed data without third-party model distillation
- Availability: MAI-Thinking-1 limited to early partners; MAI-Code-1-Flash rolling out to GitHub Copilot users
Context window size, benchmark scores on standard evaluations like HumanEval or MBPP, and pricing details have not been disclosed.
What this means
Microsoft's focus on small parameter counts—35B and 5B—runs counter to the industry trend toward larger models. If MAI-Thinking-1's claimed performance against Claude Sonnet 4.6 holds up in independent testing, it would represent a significant efficiency gain for reasoning tasks.
The emphasis on "appropriately licensed" training data is notable given ongoing legal challenges around AI training datasets. If verified, these could be among the first commercially viable code models trained without scraping unlicensed code repositories, though Microsoft has not provided specifics on what "appropriately licensed" means in practice.
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