GLM-4.5 series are foundation models designed for intelligent agents, unifying reasoning, coding, and agent capabilities in a single framework. They offer both thinking mode for complex reasoning and tool usage, and non-thinking mode for immediate responses, making them suitable for complex intelligent agent applications.
One Minute Overview#
GLM-4.5 is a large language model series developed by Zhipu AI, focusing on three core capabilities: agentic functionality, reasoning, and coding. It comes in two variants: the standard version (355B total parameters, 32B active) and the lightweight GLM-4.5-Air (106B total parameters, 12B active). Its thinking mode enables it to handle complex tasks and tool usage, making it suitable for researchers, developers, and enterprises working on intelligent agent applications, code generation, and complex reasoning scenarios.
Core Value: Unifies reasoning, coding, and agent capabilities in a single architecture to meet the complex demands of intelligent agent applications.
Quick Start#
Installation Difficulty: High - Requires high-performance GPU resources and specialized knowledge
# Launch vLLM service with Docker
docker pull vllm/vllm-openai:nightly
# Use SGLang service
docker pull lmsysorg/sglang:dev
pip install sglang
Is this suitable for me?
- ✅ Intelligent Agent Development: GLM-4.5 offers powerful agent capabilities and tool usage functionality, ideal for building complex intelligent systems
- ✅ Code Generation & Optimization: Performs excellently on programming benchmarks, supporting multi-language programming tasks
- ❌ Personal Computer Deployment: Requires high-performance GPU clusters, cannot run full version on standard PCs
- ❌ Simple Conversational Tasks: For basic conversations, GLM-4.5 may be overly complex and resource-intensive
Core Capabilities#
1. Thinking Mode - Complex Reasoning Tasks#
- The model thinks before responding, improving instruction following and generation quality
- Supports Interleaved Thinking, Preserved Thinking, and Turn-level Thinking for more stable and controllable complex tasks Actual Value: Enables the model to reason like humans, improving accuracy and consistency when solving complex problems
2. Multilingual Coding Capabilities - Code Generation & Optimization#
- Supports multilingual agentic coding and terminal tasks, with excellent performance on benchmarks like SWE-bench Actual Value: Helps developers generate, optimize, and debug code efficiently, reducing development time and error rates
3. Tool Usage Capabilities - Enhanced Practical Functionality#
- Significant improvements in tool usage, supported by benchmarks like τ²-Bench Actual Value: The model can call external tools to expand its capabilities and complete a wider range of tasks
4. Long Context Processing - Complex Information Handling#
- GLM-4.6 expands context window to 200K tokens, handling more complex agent tasks Actual Value: Can process long documents, multi-turn conversations, and complex reasoning without losing critical information
Technology Stack & Integration#
Development Language: Python Main Dependencies: transformers, vLLM, SGLang and other inference frameworks Integration Method: API / SDK / Library
Ecosystem & Extensions#
- Multiple Model Variants: Offers base models, hybrid reasoning models, and FP8 versions to meet different scenario requirements
- Various Deployment Methods: Supports platforms like Hugging Face and ModelScope, as well as inference frameworks like vLLM and SGLang
- Model Variants: From the full 355B parameter version to the lightweight 30B Flash version, providing different performance and resource consumption options
Maintenance Status#
- Development Activity: Actively updated, with iterations up to GLM-4.7
- Recent Updates: Recently released GLM-4.7 and GLM-4.6 versions with continuous improvements in performance and capabilities
- Community Response: Has official community support including WeChat and Discord communities
Commercial & Licensing#
License: MIT Open Source License
- ✅ Commercial Use: Permitted
- ✅ Modification: Allowed for secondary development
- ⚠️ Restrictions: Must comply with MIT license terms, including copyright and license notices
Documentation & Learning Resources#
- Documentation Quality: Comprehensive, providing technical reports, deployment guides, and API documentation
- Official Documentation: https://docs.z.ai/guides/capabilities/thinking-mode
- Example Code: Provides inference and API call example code