Introduction
Breakthroughs in agentic AI and coding models leading to more advanced and autonomous systems. These models are now advanced to proactive agents that can reason, plan, and perform complex, multi-step actions. But there are obstacles. One main challenge has been fragmentation of capabilities; models tend to be excellent at either reasoning, coding, or being an agent, but hardly all three at once. This has resulted in clumsy and inefficient arrangements which involve the handling of many specialist models.
New Model is designed , to solve this very issue, by integrating reasoning, coding, and agentic functions into one, complete system. By combining these fundamentals, this new model intends to satisfy the sophisticated needs of intelligent agent applications, ushering in a new age of AI that is more productive, powerful, and integrated seamlessly. This new model is known as GLM-4.5.
The Visionaries Behind the Model
The GLM-4.5 series is the creation of Zhipu, originating from the technological advancements of Tsinghua University's Computer Science Department, is an artificial intelligence company with the mission of teaching machines to think like humans. The underlying philosophy for creating GLM-4.5 was to develop one comprehensive system that integrates reasoning, coding, and agentic capabilities. This ambitious aim was taken up in order to meet the increasing sophistication of intelligent agent applications.
What is GLM-4.5?
GLM-4.5 is a sequence of cutting-edge, open-source AI models that aim to be one system for reasoning, coding, and agentic work. It is constructed to manage the complex needs of contemporary intelligent agent applications by offering an extensive and cohesive set of skills.
Model Variants
The GLM-4.5 line consists of two different foundation models, each designed for different user use cases while sharing a common design of combined capabilities and a hybrid mode of thinking.
- GLM-4.5 (The Flagship): This behemoth model has an impressive 355 billion total parameters and 32 billion active parameters. It has a huge 128k context length capacity, which means it can have very long and rich interactions. To be more efficient when inferring, an FP8 variant (GLM-4.5-FP8) exists. Its API cost is 60 cents per 1 million input tokens and $2.20 per 1 million output tokens.
- GLM-4.5-Air (The Efficient Compact): This model is for users who value efficiency without sacrificing much on power. It has 106 billion total parameters with 12 billion active parameters and also has a 128k context length. There is also an FP8 variant (GLM-4.5-Air-FP8) for this model. The API cost for the Air model is very low at 20 cents per 1 million input tokens and $1.10 per 1 million output tokens, rendering it very cost-effective.
Key Features of GLM-4.5
GLM-4.5 is filled with cutting-edge features that set it apart from the rest.
- Hybrid Thinking Modes: The two models each employ a dynamic hybrid reasoning model. They are able to alternate between a 'thinking' mode for sophisticated reasoning and tool employment, and a 'non-thinking' mode for fast, direct answers as per the complexity of the task.
- Task-Oriented Optimized for Agentic: GLM-4.5 is naturally optimized as a foundation model for agentic tasks. It supports native function calling and has recorded the highest average tool calling success rate of 90.6% when compared to the likes of Claude-4-Sonnet, Kimi K2, and Qwen3-Coder.
source - https://z.ai/blog/glm-4.5 - Novel MoE Architecture: GLM-4.5 follows a novel Mixture-of-Experts (MoE) architecture. Contrary to other MoE models, which are width-oriented, GLM-4.5 deepens (increases layers) but thins (decreases hidden dimension and number of routed experts). The design followed from the observation that deeper models have better reasoning abilities.
- Innovative Reinforcement Learning Infrastructure ('slime'): One of GLM-4.5's main technical strengths is its tailor-made, open-sourced Reinforcement Learning (RL) infrastructure called 'slime'. 'slime' is designed for extremely fast training and has a hybrid architecture that is flexible enough to accommodate both synchronous and asynchronous training. This is especially important for advanced agentic RL where data generation may become a bottleneck.
Capabilities and Use Cases of GLM-4.5
The integrated design of GLM-4.5 opens up a wide range of sophisticated uses.
- End-to-End Full-Stack Development: The framework can automatically produce complete web applications, from frontend coding to backend deployment and database handling.
Use Case: An e-commerce site could be built using GLM-4.5 to quickly prototype and deploy a full-fledged e-commerce site, with an easy-to-use interface, product database, and payment gateway, all from a single set of high-level specifications. - Sophisticated Artifact Creation: In addition to regular code, the model may create advanced, standalone artifacts.
Use Case: A game designer might create the full code for an interactive mini-game such as Flappy Bird, or a physicist might develop a working physics simulation right inside the development platform. - Sophisticated Frontend and Visual Design: GLM-4.5 is also great at designing beautifully crafted frontend interfaces in different forms.
Use Case: A UI/UX designer may have the model create complex SVG graphics, i.e., a detailed drawing of a butterfly, or create a responsive and visually good-looking web page utilizing HTML and Python. - Agent-Augmented Content Creation: The model may utilize its agentic tools to create rich content.
Use Case: A business analyst may assign GLM-4.5 to develop a complete slide deck for a market analysis report. The model would employ its web search feature to collect current market information and then create the presentation, including charts and editable HTML code.
Training and architecture
The great performance in GLM-4.5 is based on the new architecture of the design. This strategy of the model to focus on depth rather than width has allowed the model to have an advantage in its depth, which improves reasoning abilities. It uses lossless balance routing, and sigmoid gates as its MoE layers. It has a self-attention component using Grouped-Query Attention with partial RoPE, and 96 attention heads with a 5120 hidden dimension to achieve a high level of reasoning that provides enormous gains on reasoning benchmarks. QK-Norm has been used to stabilize attention logits in the model and Muon optimizer to speed up convergence. To be used at a faster rate, a Multi-Token Prediction (MTP) layer is inserted to facilitate speculative decoding.
source - https://z.ai/blog/glm-4.5
In addition to its architecture, the capabilities of GLM-4.5 are the direct consequence of an enormous and state-of-the-art multi-stage training process. It started with using an astounding 22 trillion tokens in a process of pre-training by imaginatively splitting newer into a larger 15-trillion-token general corpus then a 7-trillion-token corpus specially concentrated on code and reasoning. This base was then refined with a decisive post-training process that follows the reinforcement learning (RL) concept to develop elite agentic and reasoning capabilities. To reason, the model was trained in a one-stage RL on full length of context representation, using a curriculum that was decided based on difficulty. In the case of agentic work, it was trained to handle testable domains such as software engineering and answering information-seeking Q&A where execution-based correction was used to guarantee practical value. All of this is fueled by, what we call, slime, an innovative RL infrastructure that has a decoupled agent-first design and mixed-precision data generation (FastFormat8 to accelerate training, BetterFloat16 to ensure stability) to address the common training bottlenecks.
Performance Evaluation
Thoroughly tested on 12 industry benchmarks, GLM-4.5 had an outstanding aggregate score of 63.2, 3rd among all proprietary and open-source models. Its lighter version, GLM-4.5-Air, also registered a high 59.8, showing a better cost to performance ratio that makes high-end AI more affordable.
source - https://z.ai/blog/glm-4.5
The model's agentic capability is its defining characteristic, supported by a best-in-class 90.6% tool-calling success rate—a key statistic for dependable automaton. On agentic metrics such as TAU-bench and BFCL v3, it outperformed peers such as GPT-4 consistently. This capability reaches into coding, where it not only recorded leading win rates over Kimi K2 (53.9%) and Qwen3-Coder (80.8%) on agentic coding tasks but also beat GPT-4 on real-world problems such as SWE-bench Verified.
source - https://z.ai/blog/glm-4.5
This real-world power is founded on an elite-level of reasoning. GLM-4.5 exhibits state-of-the-art performance on challenging reasoning tests, matching the performance of top Google and Anthropic models on tough math and science problems such as AIME24 and MATH 500. This is evidence that the model's novel deep-network architecture has effectively translated to enhanced reasoning ability.
How to Access and Usage
GLM-4.5 is intended to be access-friendly. You can access it via the Z.ai API platform, which provides OpenAI-compatible interfaces, and the Z AI chatbot. For people who want local deployment, the open weights of the base and hybrid reasoning models, including the FP8 variants, are hosted on Hugging Face and ModelScope. The models integrate with mainstream inference frameworks such as vLLM and SGLang. Importantly, GLM-4.5 is open-source with a permissive MIT license for commercial use and secondary development, encouraging a thriving innovation ecosystem. Developers' main resource is the GitHub repository, which includes all the information needed for local deployment and integration.
Limitations and Future Work
While GLM-4.5 is a major leap towards unified AI, the process of reaching human-level capability in all areas remains underway. The developers admit that while the model goes a long way in unifying various capabilities, total proficiency in all tasks is an aspiration for subsequent versions. In particular, there are 'further optimization opportunities' in agentic coding tasks compared to certain competitors. Moreover, though effective as a reinforcement learning curriculum, further broadening to more complex, real-world situations may make the model even more adaptable.
Conclusion
Availability as good as it is (open-source), along with incomparable performance and price make GLM-4.5 a very tempting option in the eyes of a developer, a researcher, or a business. Generation 4.5 will use GLM-4.5 to construct the kind of smarter, more capable, and more autonomous systems of tomorrow. With GLM-4.5 released, it is clear to think that the future does not solely belong to AI on a large scale, but rather an intelligent, built-in, accessible design.
Source:
Tech blog: https://z.ai/blog/glm-4.5
GitHub Repo: https://github.com/zai-org/GLM-4.5
Model collections: https://huggingface.co/collections/zai-org/glm-45-687c621d34bda8c9e4bf503b
Base Model Weight; https://huggingface.co/zai-org/GLM-4.5
Air Model Weight: https://huggingface.co/zai-org/GLM-4.5-Air
Disclaimer - This article is intended purely for informational purposes. It is not sponsored or endorsed by any company or organization, nor does it serve as an advertisement or promotion for any product or service. All information presented is based on publicly available resources and is subject to change. Readers are encouraged to conduct their own research and due diligence.
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