MAI-Code-1.1-Flash

MAI-Code-1.1-Flash

Microsoft AI
MiniMax M3

MiniMax M3

MiniMax
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+

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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.

About

MiniMax M3 is an open-weight multimodal AI model designed for coding, agentic workflows, long-context reasoning, and complex automation tasks. The model combines frontier-level coding performance, native multimodal understanding, and a context window of up to 1 million tokens. MiniMax M3 uses MiniMax Sparse Attention to improve long-context efficiency while reducing compute requirements for large-scale inputs. It supports text, image, and video understanding, making it useful for workflows that combine code, documents, visual references, and tool-driven tasks. The model is built for repository-scale reasoning, software engineering, autonomous task execution, tool calling, and multi-step agent workflows. MiniMax M3 helps developers, AI teams, and enterprises build capable agents that can reason across large contexts and work with multimodal information.

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

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

Audience

MiniMax M3 is best suited for developers, AI engineers, coding assistant builders, enterprise automation teams, research teams, data teams, agent developers, and organizations that need open-weight AI for coding, long-context reasoning, multimodal understanding, tool use, repository analysis, and autonomous workflows

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

$0.30 per million input tokens
$0.30 per million input tokens and $1.20 per million output tokens
Free Version
Free Trial

Reviews/Ratings

Overall 4.0 / 5
features 4.0 / 5

Reviews/Ratings

Overall 5.0 / 5
ease 4.0 / 5
features 4.0 / 5

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.

Pros & Cons from Real Users

Pros

  • The multimodal side also makes it more flexible than a code-only model. Being able to work across text, images, video-style understanding, and code gives it more room to support real workflows instead of being boxed into one narrow use case. For agent builders, the best part is that M3 is clearly designed around tool use and multi-step execution. MiniMax specifically calls out autonomous task decomposition and tool invocation, which is exactly what matters when a model is powering coding assistants, workflow agents, or automated dev tools.

Cons

  • The main catch is infrastructure and trust. Even with sparse attention and MoE efficiency, this is still a large model, so deployment is not casual. I would also want to benchmark it on my own repos before depending on it for production work.

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/mai-code-1-1-flash-br-better-faster-at-a-quarter-of-the-cost/

Company Information

MiniMax
Founded: 2021
Singapore
www.minimax.io

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MAI-Code-1-Flash

MAI-Code-1-Flash

Microsoft AI
MiniMax

MiniMax

MiniMax AI
MiniMax M1

MiniMax M1

MiniMax

Categories

Categories

Integrations

.NET
APIFree
Alibaba AI Coding Plan
BLACKBOX AI
Claude Code
Clawd.run
Cline
Factory Droid
Fireworks AI
Hermes Agent
Kilo Code
Microsoft Azure
Microsoft Foundry
MiniMax Code
Ollama
OpenClaw
Roo Code
Tabbit Browser
Vision Agents
Visual Studio Code

Integrations

.NET
APIFree
Alibaba AI Coding Plan
BLACKBOX AI
Claude Code
Clawd.run
Cline
Factory Droid
Fireworks AI
Hermes Agent
Kilo Code
Microsoft Azure
Microsoft Foundry
MiniMax Code
Ollama
OpenClaw
Roo Code
Tabbit Browser
Vision Agents
Visual Studio Code
Claim MAI-Code-1.1-Flash and update features and information
Claim MAI-Code-1.1-Flash and update features and information
Claim MiniMax M3 and update features and information
Claim MiniMax M3 and update features and information