MAI model releases

Microsoft's upgraded inference-efficient agentic coding model, released Aug 11 2026 and rolling out in production across GitHub Copilot (CLI and VS Code). A post-training-focused successor to MAI-Code-1-Flash, tuned through hundreds of thousands of reinforcement-learning environments built from real GitHub Copilot usage rather than raw scaling. Microsoft reports a +22% gain on Terminal-Bench 2.1 for CLI tasks and +15% on .NET workloads over 1.0, alongside 25% greater token efficiency (25% fewer tokens per task, streaming 25% faster) — at roughly a quarter of the cost of MAI-Code-1.0. Text/code only; size and context window undisclosed, weights not released.

Microsoft's first in-house frontier reasoning model, unveiled at Build 2026. A sparse MoE (~35B active, 256K context) trained entirely on commercially licensed data without third-party distillation. Microsoft reports 97.0% on AIME 2025 and coding parity with Claude Opus 4.6 on SWE-bench Pro.

An inference-efficient agentic coding model from Microsoft (~5B active parameters), trained from the ground up on clean, traceable, enterprise-grade data without third-party distillation. Rolling out in GitHub Copilot; Microsoft reports a +16-point SWE-bench Pro lead over Claude Haiku 4.5.