MAI-Code-1.1-Flash:
Better, faster, at a quarter of the cost
Better, faster, at a quarter of the cost
Updated as of: October 7th, 2026
- Bringing MAI-Code-1.1-Flash to on-device hardware: Optimized using 3-bit quantization, it reduces memory requirements while retaining a full 256K context window and comparable coding performance to its full-precision counterpart on SWE-Bench Verified and Terminal-Bench 2.1.
- On-device coding, with zero inference charges for local model calls. MAI-Code-1.1-Flash gives Copilot’s router an on-device option, enabling eligible coding work to run on your hardware while complementing cloud models for tasks that need more capability.
- MAI-Code-1.1-Flash is available to download and run locally. For best performance, we recommend devices with more than 120GB of RAM.
- Experimental access will be available in GitHub Copilot across the GitHub Copilot app, GitHub Copilot CLI, and Visual Studio Code by end of the month.
MAI-Code-1.1-Flash produces higher quality code, at 25% greater token efficiency, and at a quarter of the cost compared to the model we launched in June at Microsoft Build. This small, efficient, coding workhorse is now in production in GitHub Copilot.
We learned from developer feedback that CLI tasks and .NET performance mattered, so that’s where we focused. The result: a 22% improvement on Terminal-Bench 2.1 in GitHub Copilot CLI and a 15% improvement on .NET tasks.
Benchmarks are useful guides but production is where the rubber meets the road. Most importantly, code survival rose 4% and return visits increased 9%.
1.1 is also dramatically more efficient. In GitHub Copilot tokens stream 25% faster and the model uses 25% fewer tokens to complete a task. That means faster answers, less waiting, and more useful work from every token—not simply a bigger model with a bigger bill.
Better training and serving efficiency let us offer a stronger, faster model at one quarter of the price of 1.0—and pass those savings reliably to customers. We achieved this by optimizing for real-world use across more than hundreds of thousands of reinforcement-learning environments in GitHub Copilot.
The loop is simple: ship, learn, improve, repeat. That’s the MAI hill climbing machine.
Help shape future improvements
Try MAI-Code-1.1-Flash today in GitHub Copilot, then tell us what needs improving by opening an issue here.
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