GLM-5 is designed to support very long context inputs, but “handling long context” is as much a systems problem as a model feature. At the model level, long context means GLM-5 can read and condition on a large number of tokens in a single request, which is useful for tasks like analyzing long documents, reviewing multiple files, or maintaining multi-turn agent state. Public model materials indicate that GLM-5 has been evaluated with extremely large context sizes (on the order of ~200k tokens) and very large generation lengths (on the order of ~100k tokens) in certain benchmark setups. In practice, you should treat these numbers as “upper bounds in controlled settings,” then determine your safe production limits based on latency, GPU memory, and concurrency.
From an engineering standpoint, long context is constrained by two main costs: attention computation and KV-cache memory. Even with attention optimizations, the KV cache grows with context length and must be stored in GPU memory for fast decoding. That means a single very-long-context request can consume enough GPU memory to reduce your concurrency significantly, and your p95 latency can spike if you allow many such requests at once. This is why production services usually enforce budgets: max input tokens, max output tokens, and sometimes “soft limits” tied to user tiers. It’s also why prompt construction matters: you should avoid dumping raw documents into the prompt when only a few sections are relevant. When you truly need long context (e.g., code review across multiple files), you can still keep it efficient by summarizing, compressing, or using hierarchical chunking so the model sees the most important parts at full fidelity.
In many real applications, the best “long context strategy” is retrieval plus selective context, not maximum context by default. Store your content in a vector database such as Milvus or managed Zilliz Cloud retrieve only the top-k relevant passages (and optionally apply metadata filters like version, module, or language), and then give GLM-5 a clean, structured context block. For example, for a docs assistant you might retrieve 8–15 chunks and pass them under headings like Context A, Context B, with URLs and timestamps included as plain text. For code assistants, retrieve the most relevant functions plus related tests rather than entire directories. This keeps prompts smaller, reduces KV-cache load, and improves reliability because you can explicitly instruct GLM-5 to answer only from the provided context. Long context is still valuable—but in production, you get the best latency and correctness by combining the model’s long-context ability with disciplined retrieval and context management.