z-ai
Z.ai: GLM 5.3 Flash (batch)
z-ai/glm-5.3-flash:batch
Our take
AI-assisted analysis based on the sources below. Suitability is an editorial assessment; test it on your own workload.
OpenRouter catalogs z-ai/glm-5.3-flash:batch as a batch variant of Z.ai's GLM 5.3 Flash, accepting text, image, and video inputs and returning text. The linked GLM-5.3-Flash documentation describes a design built from the outset for multimodal input, with 320B total parameters and 18B active, intended for efficient coding and extended agent workflows. Its hybrid sparse-and-linear attention is meant to preserve long-context behavior. Catalog data indicates reasoning is always enabled and adjustable across low, high, and max effort. Evidence is largely supplier- and catalog-reported, so workload validation remains necessary.[1][2]
Strengths
Limitations
- Only OpenRouter catalog data and Z.ai documentation were supplied; independent evaluation and batch-specific details are missing.[1][2]
- The catalog marks reasoning as mandatory, limiting the ability to disable it through that listing.[1]
- Reported evaluation settings vary by task and do not replace testing on the intended workload.[1][2]
Where to use it
- Evaluate for coding or multi-step agent workflows that require this batch variant, after confirming its endpoint behavior.[1][2]
- Consider for document, image, or video understanding tasks that produce text.[1]
- Consider for long-context analysis or tool-using workflows after validating cost, latency, and reasoning settings.[1][2]
Sources
- [1] OpenRouter catalog · Z.ai: GLM 5.3 Flash (batch)Sources checked: Nov 13, 2026
- [2] Model documentation · Z.ai: GLM 5.3 Flash (batch)Sources checked: Nov 13, 2026
About this model
Description below is the original text supplied by OpenRouter, shown in the source language.
GLM-5.3-Flash is a native multimodal model from Z.ai. It is suited for efficient coding and long-horizon agent tasks. Its hybrid sparse and linear attention architecture maintains accurate long-context behavior while...
Specifications
- Model ID
- z-ai/glm-5.3-flash:batch
- Canonical ID
- z-ai/glm-5.3-flash-20260826
- Creator
- z-ai
- Added to OpenRouter
- 26.08.2026
- Context length
- 1,048,576
- Modality
- text+image+video->text
- Input modalities
- text, image, video
- Output modalities
- text
- Token pricing
- USD per 1M tokens: in $0.050 / out $0.180
- Knowledge cutoff
- —
- Expiration date
- —
Tasks and capabilities
A grade appears only where the source supplies a matching measurement for that task, the model is active and the cohort holds at least 10 comparable models. The capability column lists facts the source declares — supported formats, reasoning and tool parameters — which are not measured quality.
| Task | Grade | Source-declared capability |
|---|---|---|
| General quality | BB — next 30% of the cohort | reasoning: mandatory · default max · efforts: max, high, low · parameters: include_reasoning, reasoning, reasoning_effort |
| Coding | AA — next 20% of the cohort | — |
| Agents | SS — top 10% of the cohort | parameters: structured_outputs, tool_choice, tools |
| Writing | No measurement | output: text |
| Reasoning | No measurement | reasoning: mandatory · default max · efforts: max, high, low · parameters: include_reasoning, reasoning, reasoning_effort |
| Document analysis | No measurement | — |
| Vision | No measurement | input: image |
| Image generation | No measurement | — |
| Audio | No measurement | — |
| Video | No measurement | input: video |
| Embeddings | No measurement | — |
Evaluations
| Source | Benchmark | Value | Grade | Data retrieved |
|---|---|---|---|---|
| Artificial Analysis | Intelligence index | 27.8 | BB — next 30% of the cohort | 13.11.2026 |
| Artificial Analysis | Coding index | 71.5 | AA — next 20% of the cohort | 13.11.2026 |
| Artificial Analysis | Agentic index | 50.9 | SS — top 10% of the cohort | 13.11.2026 |
| Design Arena | models · 3dwin rate 55% · rank 20 | 1,296Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | models · asciiartwin rate 54% · rank 11 | 1,269Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | models · codecategorieswin rate 48.3% · rank 28 | 1,268Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | models · datavizwin rate 50.2% · rank 33 | 1,257Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | models · gamedevwin rate 46.5% · rank 27 | 1,268Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | models · svgwin rate 54.8% · rank 15 | 1,272Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | models · uicomponentwin rate 52.1% · rank 19 | 1,298Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | models · websitewin rate 47.3% · rank 39 | 1,258Elo | AA — next 20% of the cohort | 13.11.2026 |
How grades work
Each letter grade compares a model only against other models scored by the same source and metric in the current snapshot. S means the top 10%, A the next 20%, B the next 30%, C the rest. Models without a published measurement are shown as not rated — a supported format alone never implies measured quality.
- S — S — top 10% of the cohort
- A — A — next 20% of the cohort
- B — B — next 30% of the cohort
- C — C — below the B band
Providers
Optional live provider statistics from OpenRouter. Missing values are unknown, not zero.
| Provider | Context length | Price in /1M | Price out /1M | Latency p50 (30m, s) | Throughput p50 (30m, tok/s) | Uptime (30m) |
|---|---|---|---|---|---|---|
| Sail Researchfp4 | 1,048,576 | $0.050 | $0.180 | — | — | — |
| DeepInfrafp4 | 1,048,576 | $0.120 | $0.400 | — | — | — |
Provider data retrieved: 13.11.2026
Related articles
No published articles identify this model yet.