MAI-Code-1-Flash

MAI-Code-1-Flash

Microsoft AI
Recursive Flint

Recursive Flint

Recursive
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+

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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

Recursive Flint is a 40-billion-parameter mixture-of-experts language model for agentic coding on local hardware. It activates about 2.5 billion parameters per token and supports reasoning, multi-turn tool use, and context windows up to 512,000 tokens. Developers can use Flint with coding agents to inspect codebases, fix bugs, implement features, and write tests while running inference on their own hardware. Its weights are published under the Apache 2.0 license. Flint works with llama.cpp, Ollama, Unsloth, LM Studio, vLLM, and SGLang; Recursive also provides rec, an inference engine optimized for Apple Silicon M4 Pro and M5 Pro/Max. Setup documentation and examples help developers run the model locally. Flint is for developers and technical teams that want to operate and adapt a code-focused model on their own systems.

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 Not Supported
Mac Supported
Linux Not Supported
Cloud 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

Software developers and technical teams building or using coding agents, especially those seeking to run a coding model locally.

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 Supported

Screenshots and Videos

Screenshots and Videos

No images available

Pricing

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

Pricing

No information available.
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

Recursive
Founded: 2026
United States
recursive.com/updates/introducing-recursive-flint

Alternatives

Alternatives

Laguna XS 2.1

Laguna XS 2.1

Poolside
Kimi K2.7 Code

Kimi K2.7 Code

Moonshot AI
MAI-Code-1.1-Flash

MAI-Code-1.1-Flash

Microsoft AI

Categories

AI Coding Models Supported
AI Models Supported

Categories

AI Coding Models Supported
AI Models Supported

Integrations

GitHub Copilot Supported
LM Studio Not Supported
Microsoft Azure Supported
Microsoft Foundry Supported
Ollama Not Supported
OpenRouter Not Supported
Unsloth Not Supported
Visual Studio Code Supported
vLLM Not Supported

Integrations

GitHub Copilot Not Supported
LM Studio Supported
Microsoft Azure Not Supported
Microsoft Foundry Not Supported
Ollama Supported
OpenRouter Supported
Unsloth Supported
Visual Studio Code Not Supported
vLLM Supported
Claim MAI-Code-1-Flash and update features and information
Claim MAI-Code-1-Flash and update features and information
Claim Recursive Flint and update features and information
Claim Recursive Flint and update features and information