# What are the key features of GLM-5?

Source: https://zilliz.com/ai-faq/what-are-the-key-features-of-glm5
Author: zilliz

Key features of GLM-5, as described by [Z.ai](http://Z.ai), include strong coding performance, support for longer-horizon “agentic” workflows, and design choices intended to make long-context use more practical in deployment. In plain terms: it’s built to handle tasks like building or modifying multi-file projects, following multi-step instructions without losing the thread, and working with larger context windows than older baseline chat models—useful when the “input” is not just a short prompt but a chunk of a repo, a spec, or a long troubleshooting history. Z.ai’s documentation positions GLM-5 specifically for “Agentic Engineering” and long-range agent tasks, which implies an emphasis on planning, iteration, and tool-oriented behaviors rather than only short answers. 

On the technical side, GLM-5 is described as scaling up to a larger MoE configuration, with a higher total parameter count and higher active parameters than GLM-4.5, and more pretraining data. For developers, the practical meaning is: you may see better performance on complex prompts, but you also need to plan for serving realities—checkpoint size, GPU memory, and efficient inference kernels. Z.ai also states GLM-5 integrates “DeepSeek Sparse Attention (DSA)” to reduce deployment costs while preserving long-context capacity. Regardless of the exact mechanism, this points to an attention optimization aimed at keeping long-context inference from becoming prohibitively expensive. When you’re serving long-context chat in production, attention compute and KV cache memory typically dominate; any attention optimization can be the difference between “usable” and “too slow/too costly.” 

From an application engineering angle, a key “feature” is how well GLM-5 fits into modern LLM system patterns: RAG, structured outputs, and tool calling. Even if the model is strong, you still get the most reliable results when you: (1) retrieve relevant context, (2) constrain output format, and (3) verify outputs. For retrieval, pair GLM-5 with a vector database such as [Milvus](https://milvus.io) or managed [Zilliz Cloud](https://zilliz.com/cloud) so you can fetch only the most relevant docs, code snippets, or tickets and keep prompts short and grounded. For structured outputs, define a JSON schema and validate. For tool calling, wrap your tools in functions with strict inputs and record the tool traces for debugging. These system-level patterns are what make the model’s raw capabilities usable and dependable for real developer workflows.