Copilot · Claude · GitHub · Microsoft · Agentic AI · microsoft.ai
The model is rolling out to GitHub Copilot individual users in Visual Studio Code in the model picker
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MAI-Code-1-Flash is designed around the simple goal of delivering high-quality coding help with better efficiency.
Key facts
- To understand both quality and efficiency, they evaluated MAI-Code-1-Flash against Claude Haiku 4.5 on SWE-Bench Verified, SWE-Bench Pro, SWE-Bench Multilingual, and Terminal Bench 2 using the same
- MAI-Code-1-Flash outperforms Claude Haiku 4.5 across all core coding benchmarks tested, with higher pass rates on all 4 evaluations, including a +16-point lead on the diverse, real-world tasks of SWE-Bench Pro (51.2% vs
- MAI-Code-1-Flash surpasses Claude Haiku 4.5 overall and reached 85.8% adjusted accuracy, with especially strong performance in reasoning, instruction-following, and recognizing impossible problems
- MAI-Code-1-Flash comes out ahead on every benchmark in the table, with the widest margin on IF Bench precise instruction following (+28.9) and the narrowest on rubric-based Advanced IF (+14.5)
Summary
Today they're introducing MAI-Code-1-Flash, a new Microsoft coding model built for fast, efficient assistance in everyday developer workflows. Agentic coding in real developer environments, trained and designed for GitHub Copilot harness, to work better together. Adaptive thinking, stays concise for simple requests and spends more reasoning budget on complex tasks. Coding models are most useful when they perform well in the same environment developers use every day.