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Z.ai's natively multimodal Mixture-of-Experts model with a 1M+ token context window and mandatory reasoning.
Overview. GLM-5.3-Flash is a natively multimodal Mixture-of-Experts model from Z.ai, released in August 2026 under an MIT licence. It pairs a 320-billion-parameter total footprint with only 18 billion active parameters per token, using a hybrid sparse-and-linear attention architecture, and accepts text, image, and video input with a context window of just over one million tokens. Developers building on long documents, large codebases, or mixed-media inputs get a wide working context without needing a specialised long-context variant.
Key strengths. The Mixture-of-Experts design routes each token through 8 of 288 experts plus a shared expert, giving GLM-5.3-Flash the working memory of a much larger dense model while activating a fraction of its total parameters. It is the first model in the GLM-5 line built for native image and video understanding alongside text, rather than bolting on vision through a separate adapter. Every response is produced through a mandatory internal reasoning pass (reasoning effort set to maximum by default), which favours depth of analysis over raw turnaround speed.
When to use it. Reach for GLM-5.3-Flash when a task genuinely needs its million-token-plus context — large codebases, long transcripts, or multi-document analysis — combined with image or video inputs in the same request. Its mandatory reasoning step makes it a fit for multi-step or agentic tasks where getting the answer right matters more than shaving milliseconds off the response. It is not the model to reach for when the task is a short, latency-critical exchange, since the reasoning pass runs on every call.
Limitations. Output is capped at 131,072 tokens even though the input context extends past a million, so very long generations still need to be chunked. The model has no audio input or output, and Z.ai has not disclosed a training-data cutoff date for this release. Because reasoning is mandatory rather than optional, it cannot be switched off for use cases that only need a fast, non-reasoning pass.
Sovereignty note. GLM-5.3-Flash is currently served through the Fireworks endpoint as a hosted commercial API. No findings confirm the creator's jurisdiction, CLOUD Act exposure, training-on-input policy, or endpoint data residency, so no sovereignty claim can be made until that information is verified.
| Inference Provider | Sovereignty | Runs in | In / out per 1M | Status | Pin |
|---|---|---|---|---|---|
| L1 | €0.15 / 0.54 | Online | |||
| L1 | €0.18 / 0.45 | Online | |||
| L1 | €0.25 / 0.50 | Online | |||
| L0 | €0.14 / 0.45 | Online | |||
| L0 | €0.14 / 0.45 | Online | |||
| L0 | €0.14 / 0.45 | Online |
15 use cases classified by the curator. The note says why.
Tool-calling plus a 1M-token context window is well suited to RAG pipelines needing large retrieved-context ingestion.
1M-token context supports Q&A over full long documents without retrieval-based truncation.
Explicit tool-calling capability is the core requirement for function-calling app architectures.
1,048,576-token context window is class-leading for maintaining coherence across very long documents.
Extremely large context window enables summarising very long source material without chunking.
Native tool-calling support combined with a 1M-token context window makes this a strong fit for orchestrating multi-tool workflows that need large working memory.
Tool-calling plus a 1M-token context window suits repo-scale agentic coding tasks, though no SWE-Bench or similar score is available to confirm quality.
1M-token context window allows reviewing very large codebases in a single pass, though no code-specific benchmark is published yet.
1M-token context window can hold multiple full papers simultaneously, useful for literature review workflows.
Tool-calling supports agentic loops, but no reasoning/extended-thinking benchmarks are published to confirm planning depth.
Large context window supports synthesising many long sources at once, though no reasoning benchmarks are published to confirm synthesis quality.
Large context window and tool-calling support RAG-adjacent search tasks, though no retrieval-specific benchmark is available.
No vision capability is listed, so chart image extraction is unsupported.
No vision capability documented; OCR requires image input support this model lacks.
No vision capability is listed for this model, ruling out image-based Q&A.
Sourced numbers, not our own evaluations. Percentiles are against the whole Stav catalogue.
Computed from aa-intelligence-index, gpqa-diamond, aa-engineering-index, aa-coding-index, livecodebench, terminal-bench-v4, aa-lcr.
curl https://api.stav.ai/v1/chat/completions \
-H "Authorization: Bearer $STAV_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "zai-org/glm-5-3-flash",
"reasoning_effort": "high",
"messages": [{"role": "user", "content": "Hello!"}]
}'→ X-Stav-Request-Id · X-Stav-Model · X-Stav-Provider → X-Stav-Sovereignty-Level — the rung the request was served at