# What are the benefits of using GLM-5?

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

The biggest benefits of using GLM-5 are practical: it’s designed to be strong on coding-oriented tasks and longer multi-step workflows, and it’s available in forms that let developers choose between hosted usage and self-managed usage. For a product team, that means you can prototype quickly via API and later decide whether you need more control by running the weights yourself. For a developer building internal tools, that flexibility can simplify compliance and data-handling decisions—especially if you want prompts and retrieved documents to stay within your own environment. [Z.ai](http://Z.ai)’s public positioning emphasizes GLM-5 for complex system engineering and long-horizon agent tasks, which aligns with real-world developer needs like iterating on a codebase, generating migration steps, or following a runbook without dropping context. 

Another benefit is the MoE scaling approach: having a large total capacity while keeping per-token compute tied to a smaller “active” subset of parameters. In practice, this can be favorable for throughput if your inference stack is optimized, because you’re not always paying the full cost of a dense model of the same total size. Z.ai also calls out attention optimizations (DSA) intended to reduce deployment cost while retaining long-context ability, which matters if your application needs to read longer inputs (code files, incident logs, or multi-turn discussions). You still have to engineer around KV cache growth and long-context latency, but the model design and serving recipes are pointing toward making those workloads more manageable. 

Finally, GLM-5 benefits a lot from pairing with retrieval, and that pairing is straightforward for developer platforms. If your use case depends on your own knowledge (docs, APIs, tickets), store embeddings and metadata in [Milvus](https://milvus.io) or managed [Zilliz Cloud](https://zilliz.com/cloud), retrieve top-k chunks, and ask GLM-5 to answer using that retrieved context only. This reduces hallucinations, keeps your outputs aligned with your actual source-of-truth, and makes your system easier to debug because you can log exactly what context was provided. A concrete example: for a “SDK helper” assistant, store each function’s docstring and examples as separate chunks with fields like language, sdk_version, and module. Retrieval ensures GLM-5 sees the right versioned snippet before generating code, which is often more important than any raw benchmark score.