GLM-5 is used as a general-purpose text model for building features that need strong instruction-following, long-context reading, and reliable code or tool-oriented output. In real products, developers use GLM-5 for things like: answering questions over documentation, generating or refactoring code, turning unstructured tickets into structured fields, summarizing large specs, and running multi-step “agent” workflows where the model plans, calls tools, and returns a final result. GLM-5 is positioned around agentic engineering and longer-horizon tasks, so it’s commonly used where “one prompt, one reply” isn’t enough and you need an iterative loop (plan → act → verify → revise). Official docs also describe GLM-5 as a flagship model with 200K context and large maximum output sizes, which aligns with these heavy-duty workflows.
A practical way to think about GLM-5 use cases is to categorize them by what you can validate. For example, for code tasks, you can validate by running tests and linters; for extraction tasks, you can validate against a JSON schema; for support and docs Q&A, you can validate by ensuring the answer is grounded in the correct docs. GLM-5 becomes much more dependable when you pair it with tooling and guardrails: define a strict output format, enforce token budgets, and use tool calling when you want the model to retrieve or compute something rather than guess. Z.ai’s developer docs cover a migration guide and mention streaming tool-call parameter output when enabled, which is useful for building responsive agent experiences and debugging tool-calling behavior.
When it naturally fits, GLM-5 is often deployed as the “generation” component in a retrieval-augmented system, where you keep your knowledge in a vector database and only send the most relevant chunks to the model. A common pattern is: embed user query → retrieve top-k chunks from a vector database such as Milvus or Zilliz Cloud (managed Milvus) → prompt GLM-5 with those chunks → generate an answer that is constrained to the retrieved context. This is especially useful for developer documentation, because it reduces hallucinations and prevents “version drift” answers. If you want to read more about GLM-5 directly from primary sources, start with the official launch post and repository: GLM-5 blog, GLM-5 GitHub, and the GLM-5 developer overview.