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GLM-5.3-Flash is the first natively multimodal model in Z.ai's GLM-5 series. With 320B total parameters and just 18B active, it outperforms GLM-5.2 across benchmarks and real-world workloads at one-tenth the cost, while approaching Claude Opus 4.8 on coding and agentic benchmarks. It runs on Ollama's hosted infrastructure in the United States and Europe.
visiontoolsthinkingcloud
Context window1M
ModalitiesText, Image
Size321B
Pulls17.5K
Tags1
Updated2 days ago
ollama run glm-5.3-flash:cloud
Cloud tags
A single cloud tag serves the 320B MoE model (18B active) on Ollama's hosted infrastructure in the United States and Europe, with zero data retention per Ollama's privacy policy.
Benchmark highlights — GLM-5.3-Flash vs. GLM-5.2
Key features
- Agentic coding — built for sustained, multi-step coding in agent harnesses.
- Long context — a 1M context window holds project-scale codebases and long-running sessions.
- Flexible thinking — reasoning is always on, with effort tunable per request across low, high, and max levels.
- Multimodal input — accepts image and video alongside text, for workflows that mix code with visual context.
- License — MIT.
Best practices
- Thinking effort — reasoning is always on; tune effort per request across low, high, and max levels to balance quality and cost.
- Vision in the coding loop — the model can inspect its own rendered output (interfaces, games, 3D) and refine it iteratively; it also reads documents, spreadsheets, and dashboards directly as images.
- Long context — the 1M window plus sparse attention (4.4× smaller KV cache) keeps long sessions and large codebases efficient.
- Efficient inference — linear + sparse attention with IndexPool cuts attention compute 3.0× vs. GLM-5.3, making the flash tier cheap at scale.