GLM-4.5

GLM-4.5: A Powerful AI Model for Reasoning, Coding, and Agentic Tasks

GLM-4.5 is the latest frontier model from the GLM family, designed to excel in reasoning, coding, and agentic tasks. It is built with 355 billion total parameters and 32 billion active parameters, making it a robust tool for complex applications. GLM-4.5 is a hybrid reasoning model, offering both a 'thinking' mode for complex reasoning and tool use, and a 'non-thinking' mode for instant responses.

Benefits

GLM-4.5 offers several key advantages:

• Unified Capabilities: Combines reasoning, coding, and agentic abilities into a single model, making it versatile for various tasks.
• High Performance: Ranked 3rd in overall performance among models from OpenAI, Anthropic, Google DeepMind, and others.
• Long Context Length: Supports 128k context length, allowing it to handle complex, multi-step tasks.
• Native Function Calling: Enables seamless integration with existing coding toolkits.
• Superior Coding Abilities: Excels in building coding projects from scratch and solving coding tasks in existing projects.

Use Cases

GLM-4.5 can be used in a variety of scenarios, including:

• Agentic Tasks: Such as web browsing, which requires complex reasoning and multi-turn tool use.
• Reasoning: Solving complex problems in mathematics, science, and logic.
• Coding: Building projects from scratch, solving coding tasks, and full-stack development.
• Artifact Creation: Generating interactive mini-games, physics simulations, and other sophisticated standalone artifacts.
• Slides Creation: Developing presentation materials, including slides and posters, with enhanced capabilities when integrated with agentic tools.

Vibes

GLM-4.5 has demonstrated strong performance in various benchmarks, matching or outperforming other leading models in agentic tasks, reasoning, and coding. For instance, it matches the performance of Claude 4 Sonnet in agentic tasks and outperforms Claude-4-Opus in web browsing tasks.

Additional Information

GLM-4.5 is built using a Mixture of Experts (MoE) architecture, which improves compute efficiency. It undergoes several training stages, including pre-training on a general corpus and specialized instruction data. The model also benefits from reinforcement learning techniques to enhance its agentic capabilities.

For those interested in using GLM-4.5, it is accessible through various platforms and can be integrated with coding agents like Claude Code. Model weights are publicly available, and detailed instructions for local deployment are provided in the official documentation.