Introduction
We are now witnessing the emergence of advanced agentic intelligence that is capable of handling multiple automated processes, developing complex digital worlds, and revolutionizing the design and development of applications through goal-oriented automation. The current state of AI is engaged in self-contained, multi-step processes that occur over time. Perhaps the most astonishing feature of current AI systems is the capability to automatically transform unstructured and disparate data into finished, native deliverables for the enterprise in an instant without requiring any manual formatting.
The new large language model has, in essence, bridged the gap between a simple chat interface and a common workspace engine. It is a highly powerful open-weight AI that matches up with some of the best proprietary systems in the world. By considering intelligence as a single flow of activity and not as simple prompt-response, the large language model provides teams with a robust platform for automating their most labor-intensive planning, auditing, and operational processes. This new AI model is referred to as GLM-5.
What is GLM-5?
GLM-5 is a foundation language model created by Z.ai, designed specifically to help move artificial intelligence from a reactive conversational interface to a proactive, vital work tool. GLM-5 is intended to be the central intelligence engine for long-horizon operational processes, multi-turn collaborative settings, and high-risk system deployments. Instead of being concerned with vibe coding or surface-level user interface design, GLM-5 is concerned with the deep, structural execution of full enterprise processes.
Key Features of GLM-5
- The total parameters of GLM-5 are 744B with 40B active per token, which is a huge improvement over its previous version i.e. GLM-4.5 that has 355B & 32B active.
- The pre-training dataset size was raised to 28.5T tokens from 23T in GLM-4.5, which is a large enough data source for the model to rely on for its capability to execute complex logic and structure reasoning.
- GLM-5 is capable of automatically generating documents directly from source materials (such as fragmented meeting notes or raw data logs) into professionally formatted documents (.doc/.pdf/.xls). The model is capable of handling the entire process of document generation without requiring you to copy the text at each stage of the way.
- GLM-5 is capable of being an agent within the chat interface, and it has multi-turn collaboration capabilities. This means that it is capable of being your workspace and generating actual, tangible results where the model is used as part of the workspace.
- With unique optimization of kernel and quantization of Models, GLM-5 can run on virtually all chips apart from NVIDIA like Huawei's Ascend, Moore Thread’s Graphics Processing Units, Cambricon, Kunle Chip, MetaX, Enflame, and Hygon, providing complete independence in strategy as it pertains to scaling intelligence throughout a wide variety of physical infrastructures.
Use Cases of GLM-5
These use case scenarios illustrate how GLM-5 can be applied to fundamentally change the way organisations operate and conduct their business.
- Long-Horizon Operational Management: GLM-5 is intended for automating and streamlining the decision-making process related to the long-term development of an organisation’s business cycle; it can be used as a tool to assist in managing the process of making long-term strategic decisions rather than merely reactively responding to isolated incidents as they occur. GLM-5 enables organisations to effectively manage the fluctuations of different operational variables such as inventory levels, dynamic pricing initiatives, and capital allocation plans that would pose substantial challenges to organisations if not managed effectively, while simulating multi-quarter business scenarios and still maintaining focus on achieving the ultimate goal.
- Orchestration Of Complex Systems: GLM-5 can be applied to large-scale engineering projects by enabling the rapid deployment of the system through orchestration as the main orchestrator for handling parallel frontend design, strong backend logic, and complex API integrations. The GLM-5 provides the entire enterprise platform in terms of functionality and scalability.
- Strategic Independence Of An Organization: Organizations can minimize the impact of a major supply chain disruption by adopting the use of advanced agentic workflows that involve multiple non-standard compute stacks. The GLM-5 has broad hardware support to ensure that enterprise intelligence continues to function at an optimal level and performs well, irrespective of vendor lock-in or the geopolitical chip shortage.
- Enterprise Security Compliance: Organizations can enhance and maintain their risk profile significantly by adopting the GLM-5 as a self-contained security solution. The model has the ability to perform a deep audit of large multi-million line code bases and enable the detection, analysis, and repair of embedded architectural flaws, beyond the shallow bug fix, before exploitation.
How Does GLM-5 Work?
Under the hood, the architecture of GLM-5 is based on a number of architectural advancements. Being a Mixture-of-Experts (MoE) model, it has an enormous knowledge base that is managed in an efficient manner. The most important part of this architecture is the incorporation of DeepSeek Sparse Attention (DSA). The incorporation of DSA is essential as it helps in cutting down the deployment costs while retaining the ability to perform long-context reasoning, which is used in windows of up to 200K tokens.
In the post-training phase, GLM-5 employs slime, which is a new asynchronous Reinforcement Learning (RL) framework. This is a significant technical achievement that has been developed to overcome the inefficiencies of training RL models. With a significantly improved training speed and efficiency, slime facilitates more fine-grained post-training steps. The improvements in pre-training, which involved 28.5T tokens, and post-training phases help GLM-5 close the gap between competence and excellence, thereby providing a model that is optimized for systems engineering and long-horizon agentic tasks.
Performance Evaluation with Other Models
In comparison with other models on rigorous benchmarking, GLM-5 has always shown elite-level performance. The performance test on the Vending Bench 2 benchmark assesses a model’s capability to deal with long-term operational management by simulating business activities over a complete one-year cycle. In this complex economic simulation, GLM-5 showed exemplary performance in achieving a final balance of 4,432.12. This performance is nearly double that of its predecessor, GLM−4.7, which scored 2,376.82. The importance of this benchmark indicates that GLM-5 has the high stability and strategic planning capability to deal with real-world business cycles, outperforming open-source models in long-term autonomous execution.
source - https://z.ai/blog/glm-5
On the CyberGym benchmark, which deals with security vulnerability analysis and effective code generation, GLM-5 scored 43.2. Although elite-level proprietary frontier models such as Claude Opus 4.5 scored higher on this particular benchmark (50.6), GLM-5 is still the best open-source model for security-related tasks. This benchmark test further verifies that GLM-5 has the capability to deal with complex architectural integrity, making it an extremely useful tool in a business environment where software vulnerabilities pose a threat to the organization.
source - https://z.ai/blog/glm-5
In addition to these results, GLM-5 was comprehensively tested on the internal CC-Bench-V2 benchmark suite, assessing its skill level in agentic coding. In terms of frontend development, backend system rewrite, and long-term programming, the model closed the performance gap with Claude Opus 4.5. Further benchmarking on infrastructure such as SWE-bench Verified (scoring 77.8) and Terminal-Bench 2.0 (scoring 56.2) further cements its position as a leader in open-source AI, demonstrating its ability to complete complex workflows.
How to Access and Use GLM-5
In line with the open research philosophy, GLM-5's model weights are made available under the MIT License. Developers can easily download the model from Hugging Face and ModelScope. For local use, it can be hosted using inference engines such as vLLM, SGLang, or xLLM. For those who want instant access, the model can be accessed via a live interface on the Z.ai website. To use its orchestration features for multi-agent tasks, it can be accessed via the Z Code environment. Moreover, the official GitHub Repository offers comprehensive documentation on its compatibility with the coding agents Claude Code and OpenClaw.
Limitations
GLM-5 presents innovative features but has some limitations, including running time. The first major limitation is that GLM-5 is a massive model with 744 billion parameters, which results in significant compute resource cost. In other words, using GLM-5 by making API requests will use a much higher plan usage, compared with other small frontier models, such as GLM-4.7. Because of the extreme compute capacity limitations, this model is being incrementally rolled out to before the completion of the overall rollout to subscribers.
Future Work
Future research efforts are very focused on developing the Chat to Work transition with the goal of developing these foundational models into common workplace tools like common applications (i.e., standard office productivity software). In addition, the research team is focused on AGI scaling strategies and developing ways for Coding Agents to autonomously self-improve through continuously interacting and receiving feedback.
Conclusion
GLM-5 offers a clear path forward in enterprise intelligence over the next ten years by separating deep reasoning from simple conversational chat and refocusing on building resilient, long-term agentic systems. Using a model of this size is an essential strategic move to keep pace with the global automation trend rather than being purely a technological upgrade, no matter how complex your data pipeline, how many massive security audits you have done, or how many multi-agent business operations you need to coordinate.
Sources:
Blog: https://z.ai/blog/glm-5
GitHub Repo: https://github.com/zai-org/GLM-5
Hugging Face: https://huggingface.co/zai-org/GLM-5
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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