MAI-Code-1-Flash

MAI-Code-1-Flash

Microsoft AI
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About

GLM-5.3-Flash is Z.ai’s natively multimodal model in the GLM-5 series (previously previewed as Ox Alpha), designed to deliver strong coding, agentic, visual, and knowledge-work performance at relatively low inference cost. It uses 320 billion total parameters with 18 billion active parameters, along with a hybrid architecture that combines sparse and linear attention to reduce the cost of long-context processing. The model supports context lengths of up to one million tokens and was trained on a 30-trillion-token multimodal corpus. GLM-5.3-Flash can reason across text, images, documents, interfaces, dashboards, and other visual information while using that feedback to refine its own outputs. Z.ai reports substantial gains over GLM-5.2 on coding and agentic benchmarks, including DeepSWE and AutomationBench, while approaching higher-cost frontier models on several evaluations.

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.

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, AI engineers, agent builders, researchers, and organizations that need cost-efficient multimodal reasoning, long-context processing, advanced coding, visual analysis, and autonomous workflow capabilities

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

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

$0.15 per 1M tokens (input)
Input: $0.15 per 1M tokens
Output: $0.50 per 1M tokens
Cached input: $0.03 per 1M tokens
Free Version
Free Trial

Pricing

No information available.
Free Version
Free Trial

Reviews/Ratings

Overall 5.0 / 5
ease 5.0 / 5

Reviews/Ratings

Overall 5.0 / 5
features 4.0 / 5

Pros & Cons from Real Users

Pros

  • What makes it exciting is that it seems built for the exact workloads developers care about right now: long-horizon coding, complex reasoning, big-context analysis, and agentic workflows. A million-token context window is especially useful if you want to drop in a large repo, long spec, research corpus, or messy project history and have the model reason across it.

Cons

  • I would treat it as something exciting to test, not something to blindly trust with sensitive work. Even the independent Ox Alpha site warns that messages are processed by the upstream model API, so I would keep secrets, private code, and customer data out of it until there is a clearer owner, model card, privacy policy, and production story.

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

Z.ai
Founded: 2019
China
z.ai

Company Information

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

Alternatives

Alternatives

GPT-5.6 Sol

GPT-5.6 Sol

OpenAI
Kimi K2.7 Code

Kimi K2.7 Code

Moonshot AI
MiniMax M3

MiniMax M3

MiniMax
MAI-Code-1.1-Flash

MAI-Code-1.1-Flash

Microsoft AI
Qwen3.5

Qwen3.5

Alibaba

Categories

Categories

Integrations

Cheaper Inference
Claude Code
DeepSeek Harness
GLM Coding Plan
GitHub Copilot
Hermes Agent
Microsoft Azure
Microsoft Foundry
OpenClaw
OpenCode Go
OpenCode Zen
OpenRouter
Pi Agent
Visual Studio Code
Z.ai
omp

Integrations

Cheaper Inference
Claude Code
DeepSeek Harness
GLM Coding Plan
GitHub Copilot
Hermes Agent
Microsoft Azure
Microsoft Foundry
OpenClaw
OpenCode Go
OpenCode Zen
OpenRouter
Pi Agent
Visual Studio Code
Z.ai
omp
Claim GLM-5.3-Flash and update features and information
Claim GLM-5.3-Flash and update features and information
Claim MAI-Code-1-Flash and update features and information
Claim MAI-Code-1-Flash and update features and information