Audience
Software engineering teams and developers seeking to write and complete code faster with an efficient AI coding model integrated into their development workflow
About MAI-Code-1.1-Flash
MAI-Code-1.1-Flash is a small, efficient coding model designed to help engineering teams write better code faster. Now in production in GitHub Copilot and built into VS Code, it focuses on real-world developer workflows, with particular improvements for command-line tasks and .NET development based on developer feedback. Compared with the version introduced at Microsoft Build in June, the model produces higher-quality code while using fewer tokens and streaming responses faster. Microsoft reports a 22% improvement on Terminal-Bench 2.1 in GitHub Copilot CLI and a 15% improvement on .NET tasks. Production results also showed a 4% increase in code survival and a 9% increase in return visits. In GitHub Copilot, tokens stream 25% faster and the model uses 25% fewer tokens to complete a task, aiming to deliver faster answers, less waiting, and more useful work from every token. Its gains come from improved training and serving efficiency, with optimization centered on real-world use.
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MAI-Code-1.1-Flash Frequently Asked Questions
MAI-Code-1.1-Flash Product Features
MAI-Code-1.1-Flash Verified User Reviews
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"Pretty good" Posted 2026-08-12
Pros: The agentic coding angle is the best part. It can plan, reason, and execute across coding tasks, which makes it useful beyond simple autocomplete. I also like the screenshot-to-prototype feature. Being able to understand screenshots, diagrams, and designs could save a lot of time when turning UI ideas into working code.
Cons: The main downside is that I would still review everything carefully. Even a strong coding model can make bad assumptions, miss edge cases, or produce code that looks right but fails in a real project.
Overall: Overall, MAI-Code-1.1-Flash looks like a strong everyday developer model: fast, efficient, editor-native, and useful for practical coding work rather than just benchmark bragging.
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