GLM-5.3

Overview. GLM-5.3 is Z.ai's flagship large language model, announced in August 2026. It is a sparse Mixture-of-Experts model with roughly 743 billion total parameters and around 40 billion active per token, built directly on the GLM-5.2 base and advanced entirely through additional post-training rather than a new pretraining run. Developers reach for it when a task calls for sustained, multi-step coding or agent work rather than a single quick completion.

Key strengths. Z.ai's own model card describes GLM-5.3 as its most capable open-weights model for coding, with post-training specifically targeted at complex coding and long-horizon agentic tasks. Independent write-ups corroborate improved agentic reliability over the already-strong GLM-5.2 base, meaning it tends to hold up better across extended tool-use loops. It also retains the 1-million-token context window from GLM-5.2, useful for reasoning over large codebases or long conversation histories.

When to use it. Use GLM-5.3 for complex coding tasks that require holding a large codebase or long spec in context, and for agentic workflows involving many sequential tool calls where reliability across the whole loop matters more than raw single-turn speed. It's a reasonable default when a task is too demanding for a lighter model but doesn't need multimodal input. It is not the right pick for tasks involving images, audio, or other non-text input, since this checkpoint is text-only.

Limitations. GLM-5.3 handles text input and output only — vision and audio are handled by a separate, differently-trained variant (GLM-5.3-Flash), not this model. It ships under a custom, non-permissive licence rather than a standard open-source one, so usage terms should be checked before redistribution or commercial deployment. Total and active parameter counts vary slightly across independent sources (743B–753B total, 39B–40B active), and Z.ai has not published a training data cutoff or a definitive max-output-tokens spec for this checkpoint.

Sovereignty note. GLM-5.3 is created by Z.ai, based in China. Its weights are published openly on Hugging Face under a custom licence, which allows self-hosted deployment for organizations that want full control over where inference runs and how data is handled. No specific CLOUD Act exposure or data-residency details are available for the hosted endpoint Stav currently serves this model through.