MAI-Code-1-Flash

MAI-Code-1-Flash

Microsoft AI
StarCoder

StarCoder

BigCode
+
+

Related Products

  • Gemini Enterprise Agent Platform
    999 Ratings
    Visit Website
  • Google AI Studio
    30 Ratings
    Visit Website
  • Checksum.ai
    1 Rating
    Visit Website
  • JAMS Scheduler
    279 Ratings
    Visit Website
  • LTX
    182 Ratings
    Visit Website
  • AnalyticsCreator
    46 Ratings
    Visit Website
  • Expedience Software
    34 Ratings
    Visit Website
  • Virtuoso QA
    131 Ratings
    Visit Website
  • JetBrains Junie
    12 Ratings
    Visit Website
  • Gravity Software
    45 Ratings
    Visit Website

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
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

Developers interested in an LLM for code generation

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

Free
Free Version
Free Trial

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:

Review this Software

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
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

BigCode
Founded: 2023
huggingface.co/blog/starcoder

Alternatives

Alternatives

CodeGemma

CodeGemma

Google
CodeQwen

CodeQwen

Alibaba
Kimi K2.7 Code

Kimi K2.7 Code

Moonshot AI
DeepSeek Coder

DeepSeek Coder

DeepSeek
MAI-Code-1.1-Flash

MAI-Code-1.1-Flash

Microsoft AI
Mercury Coder

Mercury Coder

Inception Labs

Categories

Categories

Integrations

Visual Studio Code
ChatGPT
CodeQwen
Git
GitHub
GitHub Copilot
LM Studio
Microsoft Azure
Microsoft Foundry
OpenAI
Python
Tabby
Taylor AI

Integrations

Visual Studio Code
ChatGPT
CodeQwen
Git
GitHub
GitHub Copilot
LM Studio
Microsoft Azure
Microsoft Foundry
OpenAI
Python
Tabby
Taylor AI
Claim MAI-Code-1-Flash and update features and information
Claim MAI-Code-1-Flash and update features and information
Claim StarCoder and update features and information
Claim StarCoder and update features and information