GLM-5 supports multilingual text input and output, with particularly strong emphasis on Chinese and English for general conversation and developer use cases. For most developers, “language support” has three layers: (1) can the model understand and generate the language fluently, (2) can it follow instructions and formatting constraints reliably in that language, and (3) can it perform domain tasks (coding, documentation Q&A, extraction) with the right terminology. In practice, GLM-5 is generally used in bilingual settings: English prompts for programming and infrastructure tasks, and Chinese prompts for product documentation, support, and writing. You should still validate your target languages in your specific domain—especially if you need specialized vocabulary (legal, medical, financial) or strict formatting.
To evaluate language support properly, don’t rely on “it answered one example.” Build a small benchmark with representative tasks in each language: summarize a doc, extract a schema, write unit tests, explain an error log, and answer an FAQ using retrieved context. Also test mixed-language prompts (common in developer workflows): for example, “用中文解释这段 Python 代码，并输出英文变量名的注释.” Some models handle mixed scripts well, others drift. In addition, test tooling around the model: tokenization cost can differ by language, so your maximum safe prompt length and your latency profile may shift between English and Chinese. If your product must enforce a single language output, set that in a top-level instruction and add post-validation checks (for example, reject responses containing too many non-target-language characters).
Language support is also where retrieval quality becomes very visible. If your knowledge base is multilingual, you need a retrieval strategy that can fetch the right language documents. The simplest approach is to store language metadata per chunk (e.g., lang = "en" or lang = "zh") and filter by it during search. With a vector database such as Milvus or managed Zilliz Cloud, you can store each document chunk with fields like lang, product, version, and region, then run vector search plus metadata filtering so GLM-5 sees the correct language and correct versioned content. If you expect cross-lingual queries (user asks in Chinese, docs are in English), consider storing bilingual embeddings or using an embedding model that supports cross-lingual similarity, then validate with offline tests. This makes your GLM-5 outputs feel “native” in the user’s language without requiring you to fine-tune the model for every locale.