MAI-Code-1-Flash

MAI-Code-1-Flash

Microsoft AI
MAI-Code-1.1-Flash

MAI-Code-1.1-Flash

Microsoft AI
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About

MAI-Code-1-Flash is a Microsoft coding model built for fast, efficient assistance in everyday developer workflows. Built end-to-end by Microsoft using clean and appropriately licensed data, the model is rolling out to GitHub Copilot individual users in Visual Studio Code through the model picker and the default Auto picker. It is designed around the goal of delivering high-quality coding help with better efficiency, helping engineering teams write better code faster through a lightweight, agentic model integrated into GitHub Copilot and VS Code. MAI-Code-1-Flash was trained directly with GitHub Copilot production harnesses, allowing it to interact with surrounding tools and systems in real developer environments rather than being optimized only for static benchmarks. It supports agentic coding, strong instruction-following across single-turn and multi-turn scenarios, repository question answering, refactoring, telemetry-grounded tasks, and adaptive thinking.

About

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.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

Developers and engineering teams that need a fast, efficient coding model for GitHub Copilot workflows, refactoring, repository Q&A, and agentic software engineering tasks

Audience

Software engineering teams and developers seeking to write and complete code faster with an efficient AI coding model integrated into their development workflow

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Pricing

No information available.
Free Version
Free Trial

Pricing

No information available.
Free Version
Free Trial

Reviews/Ratings

Overall 5.0 / 5
features 4.0 / 5

Reviews/Ratings

Overall 4.0 / 5
features 4.0 / 5

Pros & Cons from Real Users

Pros

  • What I like most is that it is not trying to be the biggest “solve everything” model. It is aimed at the work developers actually do all day: quick edits, explanations, refactors, small bug fixes, code cleanup, and iterative Copilot-style assistance. The GitHub Copilot and VS Code integration is the real advantage. A coding model becomes much more useful when it sits directly inside the editor instead of forcing me to bounce between tools. The efficiency angle matters too. Microsoft calls it a small-tier, inference-efficient coding model, and GitHub says it has been rolling out across more Copilot surfaces. For daily use, speed and cost can matter just as much as raw benchmark power.

Cons

  • The tradeoff is that I would not use it for every hard engineering problem. For deep architecture work, large multi-file changes, or tricky production bugs, I would still compare it against heavier reasoning models and review everything carefully.

Pros & Cons from Real Users

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.

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

Microsoft AI
Founded: 2024
United States
microsoft.ai/news/introducingmai-code-1-flash/

Company Information

Microsoft AI
Founded: 2024
United States
microsoft.ai/news/mai-code-1-1-flash-br-better-faster-at-a-quarter-of-the-cost/

Alternatives

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Kimi K2.7 Code

Kimi K2.7 Code

Moonshot AI
Claude Mythos 5

Claude Mythos 5

Anthropic
MAI-Code-1.1-Flash

MAI-Code-1.1-Flash

Microsoft AI
MAI-Code-1-Flash

MAI-Code-1-Flash

Microsoft AI

Categories

Categories

Integrations

GitHub Copilot
Microsoft Azure
Microsoft Foundry
Visual Studio Code
.NET

Integrations

GitHub Copilot
Microsoft Azure
Microsoft Foundry
Visual Studio Code
.NET
Claim MAI-Code-1-Flash and update features and information
Claim MAI-Code-1-Flash and update features and information
Claim MAI-Code-1.1-Flash and update features and information
Claim MAI-Code-1.1-Flash and update features and information