Key features of GLM-5, as described by 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 or managed Zilliz 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.