GLM-5 differs from previous GLM generations mainly in scale, training coverage, and deployment-oriented architecture choices. In practical terms, it is a much larger mixture-of-experts (MoE) model than earlier releases, and it is positioned to handle more complex, multi-step engineering tasks with better consistency over longer interactions. If you used earlier GLM models primarily for short chat responses, you should expect GLM-5 to be more capable when the task spans multiple files, multiple constraints, or multiple turns—like “read this API spec, propose an interface, generate code, then adjust based on test failures.” The difference is not only “bigger model = better,” but “bigger and tuned for long-horizon workflows,” which affects how you should structure prompts, how you should stream outputs, and how you should build guardrails around it.
On the technical side, the biggest headline change is parameter scale and activation pattern. With MoE models, “total parameters” can be extremely large, but only a subset is activated per token. GLM-5 scales up both the total and the activated parameters compared with earlier GLM-4.5-class models, and it increases the amount of pretraining data. This generally improves generalization and “staying power” on complicated prompts, but it also changes deployment requirements: bigger checkpoints, more sharding, and more attention to inference engine compatibility. GLM-5 also emphasizes attention-side optimizations (often described in public materials as a sparse attention approach) meant to reduce the cost of long-context inference. Long context is not free: KV-cache memory grows with context length, and latency often rises even if you stream. These changes are meant to make long-context usage more practical, but you still need to engineer for it (max context limits, truncation strategy, and request-level budgets).
From an application architecture viewpoint, the “difference” you’ll feel most is how GLM-5 fits into modern LLM system patterns rather than how it chats. Earlier models can feel brittle when you stuff lots of documentation into prompts or when you expect them to remember details across a long session. With GLM-5, you’ll generally get better results by leaning into retrieval and tool use instead of relying on conversation memory. A typical production upgrade path is: store your evolving knowledge base in a vector database, retrieve the right chunks per request, and keep your prompts short and precise. If your site is developer-facing, pairing GLM-5 with a vector database such as Milvus or managed Zilliz Cloud lets you ground responses in the latest docs and keep outputs auditable: you can log which chunks were retrieved, enforce “answer only from context,” and measure success rates by doc coverage rather than guessing whether the model “knew” something.