GLM-5.1

GLM-5.1

Z.ai
MiniMax M3

MiniMax M3

MiniMax
+
+

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About

GLM-5.1 is the latest iteration of Z.ai’s GLM series, designed as a frontier-level, agent-oriented AI model optimized for coding, reasoning, and long-horizon workflows. It builds on the GLM-5 architecture, which uses a Mixture-of-Experts (MoE) design to deliver high performance while keeping inference costs efficient, and is part of a broader push toward open-weight, developer-accessible models. A core focus of GLM-5.1 is enabling agentic behavior, meaning it can plan, execute, and iterate across multi-step tasks rather than simply responding to single prompts. It is specifically designed to handle complex workflows such as debugging code, navigating repositories, and executing chained operations with sustained context. Compared to earlier models, GLM-5.1 improves reliability in long interactions, maintaining coherence across extended sessions and reducing breakdowns in multi-step reasoning.

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 Supported
Mac Supported
Linux Supported
Cloud Supported
On-Premises Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported

Platforms Supported

Windows Supported
Mac Supported
Linux Supported
Cloud Supported
On-Premises Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported

Audience

Developers and AI teams who need a cost-efficient, agent-capable model for coding, automation, and long multi-step workflows with flexible deployment options

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 Not Supported
24/7 Live Support Not Supported
Online Supported

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Supported

API

Offers API Supported

API

Offers API Supported

Screenshots and Videos

Screenshots and Videos

Pricing

Free
Open source
Free Version Supported
Free Trial Not Supported

Pricing

$0.30 per million input tokens
$0.30 per million input tokens and $1.20 per million output tokens
Free Version Supported
Free Trial Not Supported

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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Reviews/Ratings

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

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 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

Z.ai
Founded: 2023
China
z.ai/

Company Information

MiniMax
Founded: 2021
Singapore
www.minimax.io

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Categories

Categories

Integrations

APIFree Supported
Claude Code Supported
Cline Supported
Factory Droid Supported
Hermes Agent Supported
Kilo Code Supported
Ollama Supported
OpenClaw Supported
Roo Code Supported
Tabbit Browser Supported
Together AI Supported
CSS Supported
Claw Code Supported
GLM-5-Turbo Supported
PHP Supported
Sup AI Supported
Swift Supported
Vercel AI Gateway Supported
Yonoo Supported
scikit-learn Supported

Integrations

APIFree Supported
Claude Code Supported
Cline Supported
Factory Droid Supported
Hermes Agent Supported
Kilo Code Supported
Ollama Supported
OpenClaw Supported
Roo Code Supported
Tabbit Browser Supported
Together AI Supported
CSS Not Supported
Claw Code Not Supported
GLM-5-Turbo Not Supported
PHP Not Supported
Sup AI Not Supported
Swift Not Supported
Vercel AI Gateway Not Supported
Yonoo Not Supported
scikit-learn Not Supported
Claim GLM-5.1 and update features and information
Claim GLM-5.1 and update features and information
Claim MiniMax M3 and update features and information
Claim MiniMax M3 and update features and information