Key Features

It is the first natively multimodal model announced in the GLM-5 series.
The model activates 18B of its 320B total parameters.
A hybrid of linear and sparse attention reduces attention computation.
IndexPool pools indexing keys to reduce long-context indexer overhead.
Training emphasizes visual self-evaluation and iterative frontend or artifact refinement.
The architecture targets context lengths up to one million tokens.
Z.ai links publicly available GLM-5.3-Flash weights on Hugging Face.
The announcement names SGLang, vLLM, and TokenSpeed support.

The model has 320B total parameters with 18B active and uses hybrid linear and sparse attention. IndexPool compresses indexing keys for long contexts, while manifold-constrained hyper-connections support information flow. A redesigned training recipe includes a thirty-trillion-token multimodal corpus and visual coding tasks.


GLM-5.3-Flash is available through Z.ai services, Coding Plan, and public weights. Its visual coding workflow is useful for frontend, game, and document tasks where success depends on the rendered result. Local deployment is supported by documented inference frameworks but still requires hardware appropriate to the full checkpoint.

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