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Average Ratings 1 Rating

Total
features

Description

GLM-5.3 is Z.ai’s advanced coding and agentic reasoning model built through scaled post-training on top of the GLM-5.2 base model. The release focuses on frontier coding, long-horizon software engineering, agent tasks, cyber evaluation, and reinforcement learning at scale. GLM-5.3 improves significantly over GLM-5.2 on complex coding benchmarks, real-world engineering environments, Terminal Bench 3.0, DeepSWE, Agents’ Last Exam, and Z.ai’s internal Code Bench. The model is trained on environments that resemble real professional work, including tasks involving codebases, infrastructure, documentation, compute clusters, experiments, bottleneck diagnosis, implementation, testing, and measurable optimization. Z.ai’s post-training stack includes IndexShare for efficient long-context processing, SAO for reinforcement learning on long-horizon tasks, and slime for large-scale asynchronous training. GLM-5.3 supports three thinking effort levels, including low, high, and max, with max recommended for coding tasks. The model also demonstrates emergent cyber capabilities across vulnerability discovery and exploitation benchmarks, prompting continued safety evaluation and hardening before weights are released. GLM-5.3 can be used through the GLM Coding Plan, ZCode, Claude Code, OpenCode, and other coding agent workflows. By combining stronger coding performance, long-horizon task execution, post-training scale, cyber evaluation, reasoning effort controls, and coding-agent integrations, GLM-5.3 supports advanced developer and research workflows.

Description

MAI-Code-1.1-Flash is a compact and effective coding model aimed at enhancing the speed and quality of code development for engineering teams. Currently implemented in GitHub Copilot and integrated into VS Code, it caters to the actual workflows of developers, specifically enhancing command-line operations and .NET tasks based on user input. When compared to the version unveiled at Microsoft Build in June, this model showcases a significant improvement in code quality, achieved with reduced token usage and quicker streaming responses. Microsoft claims a 22% enhancement on Terminal-Bench 2.1 for GitHub Copilot CLI and a 15% boost in .NET task performance. Additionally, production outcomes indicate a 4% rise in code survival rates and a 9% increase in users returning to the platform. Notably, in GitHub Copilot, tokens are streamed 25% faster, and the model requires 25% fewer tokens for task completion, which translates to quicker responses, reduced wait times, and enhanced productivity from each token processed. These advancements stem from refined training methods and improved operational efficiencies, with a strong focus on practical application in real-world scenarios. Ultimately, MAI-Code-1.1-Flash represents a significant leap forward in coding assistance technology.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

.NET
CSS
Cheaper Inference
ClinePass
Cursor
GitHub Copilot
JetBrains Junie
Kilo Code
Microsoft Foundry
Roo Code
Shiori
Sup AI
Swift
Tabbit Browser
Together AI
TypeScript
Vercel AI Gateway
Visual Studio Code
Zo Computer
scikit-learn

Integrations

.NET
CSS
Cheaper Inference
ClinePass
Cursor
GitHub Copilot
JetBrains Junie
Kilo Code
Microsoft Foundry
Roo Code
Shiori
Sup AI
Swift
Tabbit Browser
Together AI
TypeScript
Vercel AI Gateway
Visual Studio Code
Zo Computer
scikit-learn

Pricing Details

Free
Open source
Free Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Z.ai

Founded

2023

Country

China

Website

z.ai/

Vendor Details

Company Name

Microsoft AI

Founded

2024

Country

United States

Website

microsoft.ai/news/mai-code-1-1-flash-br-better-faster-at-a-quarter-of-the-cost/

Product Features

Alternatives

Alternatives

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