GLM 4.5: Z.ai’s Open-Source AI Revolution
Table of Contents:
- Introduction
- What is the Agent-Native Architecture of GLM 4.5?
- How Does the Hybrid Reasoning Framework Work?
- Mixture of Experts: A Technical Overview
- Diving Deeper: Architectural Details
- How Does GLM 4.5 Perform Against Its Rivals?
- Is GLM 4.5 Cost-Effective?
- How Accessible is GLM 4.5?
- Summary of GLM 4.5
- FAQ
GLM 4.5: Z.ai’s Open-Source AI Revolution
Are you ready to explore the future of open-source AI? The landscape is shifting, with the arrival of GLM 4.5, a groundbreaking open-source large language model (LLM) developed by Z.ai, a Chinese AI company, released in mid-2025. It is an important advancement, merging a never-before-seen agent-native design with a large-scale Mixture of Experts (MoE) to give great results in thinking, coding, along with agent jobs.
GLM 4.5 is released in two versions:
- The main GLM-4.5 model has 355 billion total parameters (32 billion active).
- The smaller GLM-4.5-Air has 106 billion total parameters (12 billion active).
The two versions provide options for different deployment needs.
What is the Agent-Native Architecture of GLM 4.5?
The heart of GLM 4.5 is its agent-native architecture. It combines thinking, understanding, as well as doing abilities straight into the model’s makeup. This design lets the model do actions with many steps on its own, picture complex data, also take actions depending on the context, all without outside tools.
This design makes GLM 4.5 especially useful for agent uses. These include:
- Enterprise automation
- AI agents needing both knowledge as well as independence
How Does the Hybrid Reasoning Framework Work?
A clear feature of GLM 4.5 is its hybrid reasoning structure. It supports two operating modes inside a single model. First is the Thinking Mode. It helps with complex thinking, using tools, making plans in steps, not only autonomous agent work. Second, the Non-Thinking Mode. It’s improved for fast, simple answers. This makes the model flexible for both hard thinking work and quick talks.
The two-mode power lets GLM 4.5 well balance correctness and speed, meeting a wide variety of AI uses.
Mixture of Experts: A Technical Overview
From a technical point of view, GLM 4.5 uses a Mixture of Experts (MoE) design. It turns on only some of the model’s parameters when making guesses, improving how well it computes along with making responses faster. The main model uses 32 billion active parameters out of 355 billion. For the smaller GLM-4.5-Air, 12 billion out of 106 billion are active. This special turning on means the models keep great performance while cutting operating costs.
Diving Deeper: Architectural Details
The building of GLM 4.5 stresses depth over width. Some other models like DeepSeek-V3 do not. It uses advanced systems such as QK-Norm, Grouped Query Attention, Multi-Token Prediction, and the Muon optimizer. It is Depth over Width. These new parts help make learning faster along with improve thinking powers.
The model learned from a big collection of 22 trillion tokens. Of these, 7 trillion tokens were just for code along with thinking jobs. Learning also used reinforcement learning, using Z.ai’s private asynchronous agentic RL system. This was made to make the most of how much work is done along with help with jobs that take a long time.
How Does GLM 4.5 Perform Against Its Rivals?
Performance tests put GLM 4.5 as a leading model across the globe, especially among open-source options. Across 12 global AI tests, including thinking, coding, in addition to agent powers, GLM 4.5 was third best around the globe. It was also the top among open-source, also Chinese models. Its average test score was 63.2. GLM-4.5-Air got a competitive 59.8, leading models with about 100 billion parameters. The model showed special skill in Chinese language jobs as well as coding tests, often getting the best results.
Moreover, GLM 4.5 did better than well-known rivals in using tools well, getting 90.6%. This did better than models such as Claude 3.5 Sonnet as well as Kimi K2.
Is GLM 4.5 Cost-Effective?
Cost savings are also an important part of GLM 4.5. Z.ai says the model works at a token cost of about 11 cents. This makes it one of the most affordable models in its category. How affordable it is, combined with its great performance and open-source availability, makes GLM 4.5 a strong option to models from Western companies like OpenAI plus Anthropic. It shows China’s plan to be a leader in the global AI area by giving an open-source answer to leading proprietary systems.
How Accessible is GLM 4.5?
GLM 4.5 is easy to get to as well as friendly for developers. The model weights can be found on places such as Hugging Face, also ModelScope. This makes it easy to put it to use locally along with to experiment. It helps with working with well-known guessing systems like vLLM as well as SGLang. It also be got to straight through Z.ai’s API. Plus, GLM 4.5 may be put into already there coding agents, including Claude Code as well as Roo Code, adding to their powers with its advanced thinking and agent abilities.
Summary of GLM 4.5
To sum things up, GLM 4.5 by Z.ai is a important open-source language model. It moves AI ahead through its agent-native makeup, hybrid thinking modes, and good Mixture of Experts build. Its two-version release plan helps with both high-power and resource-saving uses. Its great test results and low working costs make it a strong rival in the global AI group.
By combining hard thinking, coding skills, as well as independent agent powers inside a single model, GLM 4.5 shows the next group of flexible, expandable, as well as easy to get to AI systems.
FAQ
What is the main difference between GLM-4.5 and GLM-4.5-Air?
The primary difference lies in the size and performance. GLM-4.5 is the flagship model with 355 billion parameters (32 billion active), designed for high-capacity, demanding tasks. GLM-4.5-Air is a lighter version with 106 billion parameters (12 billion active), optimized for resource-efficient applications where speed and lower operational costs are more important.
Where I download the GLM 4.5 model weights?
You are able to access the model weights on platforms such as Hugging Face plus ModelScope.
Does GLM 4.5 support multiple languages?
Yes, GLM 4.5 demonstrates particularly skill in Chinese language tasks and coding challenges, as well as consistently achieving state-of-the-art results.
