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Z.ai / FAMILY RECORD

GLM-5.3 Flash

A natively multimodal sparse GLM checkpoint released under MIT terms for long-context agent and coding work.

Checked release: GLM-5.3-Flash
Sources checked 2026-09-19

GLM-5.3-Flash release
· Primary source

The profile describes this checkpoint, not every release carrying the family name. Developer documentation is not independent reproduction.

THE MODEL / THE TERMS

Know what you are picking up.

Parameters
320B total / 18B active
Architecture
Mixture-of-experts transformer with hybrid sparse and linear attention.
Modalities
text · image
Context
1M tokens
Weights license
MIT
Commercial use
Permitted by the MIT license for this Flash checkpoint; other GLM 5.3 variants have different terms.
Memory
No reliable universal minimum is stated; the 320B checkpoint requires distributed or quantized serving.
Known quantizations
Runtime-specific community quantizations exist; the canonical release should be checked first

Specifications are developer-reported. Primary release documentation ↗ · All sources and terms

NINE DIMENSIONS / NO OPENNESS SCORE

What is actually available?

Each component has its own evidence. “Not established” means this source review did not verify availability; it is not a claim that nothing exists.

WHY THIS RELEASE MATTERS

What to explore.

  • Long context
  • Native multimodality
  • Sparse inference
  • Permissive checkpoint license

WHAT THE RECORD DOES NOT ESTABLISH

Keep the limits.

  • Training data is not disclosed in enough detail
  • Large server-class checkpoint

The vLLM recipe corroborates deployability, not vendor capability claims.

Explore the memory tradeoffs

FOLLOW THE RECORD

Sources, not superlatives.

  1. Z.ai GLM-V release note (repository)
  2. GLM-5.3-Flash model card (registry)
  3. vLLM GLM-5.3-Flash recipe (repository)

Source check: 2026-09-19. AI-assisted editorial review, with no named external expert or independent hardware validation claimed. Licenses and model cards can change. Review the linked terms for the specific artifact and intended use.

Understand open source vs open weights ↗