GLM-4.5

Overview

The world of AI models is constantly evolving, and GLM-4.5 represents a significant advancement in open-weight AI technology. This groundbreaking Mixture-of-Experts (MoE) model is specifically designed to excel in reasoning, coding, and agentic applications. With its impressive 355B total parameters utilizing 32B active parameters during inference, plus a more accessible 106B Air version with 12B active parameters, GLM-4.5 delivers state-of-the-art performance while maintaining computational efficiency through its intelligent parameter activation system.

Key Features

GLM-4.5 incorporates advanced architectural innovations that position it as a leader in the open-source AI landscape:

• Advanced MoE Architecture: Features 355B total parameters with intelligent activation of only 32B parameters per inference, optimizing both performance and computational efficiency through sparse expert routing.
• Hybrid Reasoning Modes: Supports both thinking mode for complex reasoning and tool usage, and non-thinking mode for immediate responses, allowing flexible adaptation to different task requirements.
• Native Agentic Capabilities: Purpose-built for autonomous agent applications with integrated function calling, 128K context length, and seamless tool integration capabilities.
• Comprehensive Open-Weight Design: Released under MIT license with full model weights available on HuggingFace and ModelScope, enabling unrestricted commercial use and community innovation.
• Dual Model Variants: Includes both the flagship GLM-4.5 and the efficient GLM-4.5-Air version, providing scalability options for different computational requirements and deployment scenarios.

How It Works

GLM-4.5 leverages a sophisticated Mixture-of-Experts architecture that revolutionizes efficiency in large-scale language modeling. Rather than activating all 355 billion parameters simultaneously, the model intelligently engages only the most relevant 32 billion parameters for each specific query, dramatically reducing computational overhead while maintaining exceptional performance quality.

The model employs hybrid reasoning capabilities through its dual-mode operation system. In thinking mode, GLM-4.5 performs deliberate step-by-step reasoning for complex problems, making it ideal for mathematical proofs, code debugging, and multi-step logical tasks. The non-thinking mode provides immediate responses for straightforward queries, ensuring optimal user experience across different interaction types.

GLM-4.5’s training methodology incorporates a comprehensive three-stage approach: initial pre-training on 15 trillion tokens of general data, followed by specialized training on 7 trillion tokens of code and reasoning data, and final reinforcement learning optimization for agentic capabilities. This extensive training regime enables unified performance across reasoning, coding, and autonomous agent tasks within a single model framework.

Use Cases

GLM-4.5 opens extensive possibilities across multiple domains and applications:

• Advanced AI Research and Development: Ideal for exploring frontier AI capabilities, developing novel algorithms, and conducting research in reasoning, code generation, and autonomous systems within academic and industrial settings.
• Enterprise Software Development: Powers comprehensive full-stack development workflows, from frontend interface creation to backend system implementation, with integrated database management and deployment automation.
• Intelligent Agent Systems: Enables sophisticated autonomous agents capable of complex decision-making, multi-step planning, tool usage, and adaptive learning in dynamic environments across various industries.
• Scientific and Mathematical Computing: Excels in complex problem-solving scenarios including advanced mathematics, scientific modeling, data analysis, and research applications requiring multi-step logical reasoning.
• Multilingual Content Processing: Provides robust capabilities for content generation, translation, and cross-cultural communication across diverse languages and specialized domains.

Pros \& Cons

Advantages

• Industry-Leading Parameter Efficiency: Delivers exceptional performance through intelligent MoE architecture while using significantly fewer active parameters than comparable models, resulting in superior cost-effectiveness and faster inference times.
• Unified Capability Integration: Successfully combines reasoning, coding, and agentic functionalities in a single model, eliminating the need for multiple specialized systems and simplifying deployment architectures.
• Open-Source Accessibility: MIT licensing and open-weight availability foster rapid innovation, community development, and unrestricted commercial adoption across diverse use cases and organizations.
• Superior Agentic Performance: Achieves highest tool calling success rate at 90.6% among tested models, demonstrating exceptional reliability for autonomous agent applications and complex workflow automation.

Disadvantages

• Substantial Infrastructure Requirements: Despite efficiency optimizations, the full 355B parameter model still demands considerable computational resources for optimal performance, potentially limiting accessibility for smaller organizations.
• Complex Deployment Considerations: Advanced MoE architecture and dual-mode capabilities may require specialized expertise for optimal configuration and deployment, particularly in production environments with specific performance requirements.

How Does It Compare?

In the rapidly evolving landscape of large language models in 2025, GLM-4.5 competes among several powerful alternatives, each offering distinct advantages for different use cases.

When compared to OpenAI’s latest models, GLM-4.5 distinguishes itself through its open-weight architecture and cost-effectiveness. While OpenAI’s o3 and GPT-4.1 demonstrate superior performance on certain benchmarks, GLM-4.5 offers comparable reasoning capabilities at a fraction of the cost, with the added benefit of complete transparency and customization freedom that closed-source models cannot provide.

Against Anthropic’s Claude 4 series, GLM-4.5 shows competitive performance in coding tasks and matches Claude 4 Sonnet in agentic benchmarks. However, Claude 4 models may have advantages in certain reasoning tasks and safety considerations, while GLM-4.5 provides superior accessibility through its open-source nature and significantly lower operational costs.

Compared to DeepSeek-V3, another prominent open-source alternative with 671B total parameters, GLM-4.5 achieves similar performance levels while using fewer total parameters, demonstrating superior efficiency. GLM-4.5’s integrated agentic capabilities and hybrid reasoning modes provide additional advantages for autonomous applications.

Among Google’s Gemini 2.5 Pro and other multimodal models, GLM-4.5 currently focuses on text-based reasoning and coding, trading multimodal capabilities for specialized excellence in its core domains. This focused approach enables superior performance in reasoning and coding tasks compared to more generalized multimodal alternatives.

In the Chinese AI ecosystem, GLM-4.5 competes with models like Qwen3 and Kimi K2, offering competitive performance with distinctive advantages in unified reasoning-coding-agent capabilities and comprehensive open-source accessibility under permissive licensing terms.

Final Thoughts

GLM-4.5 represents a significant milestone in open-source AI development, successfully unifying reasoning, coding, and agentic capabilities within an efficient and accessible framework. Its innovative MoE architecture, comprehensive training methodology, and hybrid reasoning modes position it as a compelling choice for organizations seeking powerful AI capabilities without the constraints of proprietary systems.

The model’s open-weight availability under MIT licensing, combined with its superior parameter efficiency and strong performance across diverse benchmarks, makes it particularly attractive for research institutions, enterprises, and developers requiring transparent, customizable AI solutions. While computational requirements remain substantial for the full model, the availability of the GLM-4.5-Air variant provides scalable options for different deployment scenarios.

As the AI landscape continues evolving rapidly, GLM-4.5’s foundation in unified capabilities and open accessibility positions it well for continued development and adaptation to emerging use cases, making it a valuable contribution to the global AI ecosystem.