MAI-Code-1-Flash

MAI-Code-1-Flash

Microsoft AI
StarCoder

StarCoder

BigCode
+
+

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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

StarCoder and StarCoderBase are Large Language Models for Code (Code LLMs) trained on permissively licensed data from GitHub, including from 80+ programming languages, Git commits, GitHub issues, and Jupyter notebooks. Similar to LLaMA, we trained a ~15B parameter model for 1 trillion tokens. We fine-tuned StarCoderBase model for 35B Python tokens, resulting in a new model that we call StarCoder. We found that StarCoderBase outperforms existing open Code LLMs on popular programming benchmarks and matches or surpasses closed models such as code-cushman-001 from OpenAI (the original Codex model that powered early versions of GitHub Copilot). With a context length of over 8,000 tokens, the StarCoder models can process more input than any other open LLM, enabling a wide range of interesting applications. For example, by prompting the StarCoder models with a series of dialogues, we enabled them to act as a technical assistant.

Platforms Supported

Windows Not Supported
Mac Not Supported
Linux Not Supported
Cloud Supported
On-Premises Not Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported

Platforms Supported

Windows Supported
Mac Supported
Linux Supported
Cloud Not Supported
On-Premises Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported

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

Developers interested in an LLM for code generation

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Supported

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Not Supported

API

Offers API Supported

API

Offers API Not Supported

Screenshots and Videos

Screenshots and Videos

Pricing

No information available.
Free Version Not Supported
Free Trial Not Supported

Pricing

Free
Free Version Supported
Free Trial Not Supported

Reviews/Ratings

Overall 5.0 / 5
features 4.0 / 5

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

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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.

Training

Documentation Supported
Webinars Not Supported
Live Online Not Supported
In Person Not Supported

Training

Documentation Supported
Webinars Not Supported
Live Online Not Supported
In Person Not Supported

Company Information

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

Company Information

BigCode
Founded: 2023
huggingface.co/blog/starcoder

Alternatives

Alternatives

CodeGemma

CodeGemma

Google
Kimi K2.7 Code

Kimi K2.7 Code

Moonshot AI
CodeQwen

CodeQwen

Alibaba
DeepSeek Coder

DeepSeek Coder

DeepSeek
MAI-Code-1.1-Flash

MAI-Code-1.1-Flash

Microsoft AI

Categories

AI Coding Models Supported
AI Models Supported

Categories

AI Code Generators Supported
AI Coding Models Supported
AI Copilots Supported
AI Models Supported
AI Tools Supported

Integrations

Visual Studio Code Supported
ChatGPT Not Supported
CodeQwen Not Supported
Git Not Supported
GitHub Not Supported
GitHub Copilot Supported
LM Studio Not Supported
Microsoft Azure Supported
Microsoft Foundry Supported
OpenAI Not Supported
Python Not Supported
Tabby Not Supported
Taylor AI Not Supported

Integrations

Visual Studio Code Supported
ChatGPT Supported
CodeQwen Supported
Git Supported
GitHub Supported
GitHub Copilot Not Supported
LM Studio Supported
Microsoft Azure Not Supported
Microsoft Foundry Not Supported
OpenAI Supported
Python Supported
Tabby Supported
Taylor AI Supported
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