z-ai
Z.ai: GLM 5.2 (batch)
z-ai/glm-5.2:batch
Our take
AI-assisted analysis based on the sources below. Suitability is an editorial assessment; test it on your own workload.
z-ai/glm-5.2:batch is the OpenRouter catalog's text-to-text entry for Z.ai's GLM-5.2-20260616 under this batch variant ID. The catalog reports a million-token context window, optional reasoning enabled by default at high effort, tool use, structured outputs, and broad generation controls. The linked Z.ai model card presents GLM-5.2 as an MIT-licensed model for long-horizon tasks and project-level software engineering. Vendor benchmarks and catalog-listed arena/index results are source-reported measurements, not guarantees; validate the exact batch endpoint on your workload.[1][2]
Strengths
Limitations
- Only text input and output are reported; other modalities are unknown, not confirmed unsupported.[1]
- The supplied evidence does not state the knowledge cutoff, batch-specific service semantics, endpoint limits, or safety behavior.[1][2]
- The supplied sources do not independently verify this exact batch endpoint; validate quality, latency, throughput, and availability on your workload.[1][2]
Where to use it
- Evaluate for project-level software engineering assistants or long-context code analysis.[1][2]
- Consider for long-document or long-horizon agent workflows where a large context and tool calls are relevant.[1][2]
- Pilot for structured text-generation pipelines that need adjustable reasoning effort and response formatting.[1]
Sources
- [1] OpenRouter catalog · Z.ai: GLM 5.2 (batch)Sources checked: Nov 13, 2026
- [2] Model documentation · Z.ai: GLM 5.2 (batch)Sources checked: Nov 13, 2026
About this model
Description below is the original text supplied by OpenRouter, shown in the source language.
GLM 5.2 is a large-scale reasoning model from Z.ai. It supports text input and output with a 1M-token context window, and is suited for long-horizon agent workflows, project-level software engineering,...
Specifications
- Model ID
- z-ai/glm-5.2:batch
- Canonical ID
- z-ai/glm-5.2-20260616
- Creator
- z-ai
- Added to OpenRouter
- 16.06.2026
- Context length
- 1,048,576
- Modality
- text->text
- Input modalities
- text
- Output modalities
- text
- Token pricing
- USD per 1M tokens: in $0.298 / out $0.916
- 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 | No measurement | reasoning: optional · default high · efforts: xhigh, high, none · parameters: include_reasoning, reasoning, reasoning_effort |
| Coding | AA — next 20% of the cohort | — |
| Agents | AA — next 20% of the cohort | parameters: structured_outputs, tool_choice, tools |
| Writing | No measurement | output: text |
| Reasoning | No measurement | reasoning: optional · default high · efforts: xhigh, high, none · parameters: include_reasoning, reasoning, reasoning_effort |
| Document analysis | No measurement | — |
| Vision | No measurement | — |
| Image generation | No measurement | — |
| Audio | No measurement | — |
| Video | No measurement | — |
| Embeddings | No measurement | — |
Evaluations
| Source | Benchmark | Value | Grade | Data retrieved |
|---|---|---|---|---|
| Artificial Analysis | Coding index | 68.8 | AA — next 20% of the cohort | 13.11.2026 |
| Artificial Analysis | Agentic index | 38.4 | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | agents · agenticgamedevwin rate 49.7% · rank 9 | 1,157Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | agents · androidnativewin rate 52.7% · rank 23 | 1,169Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | agents · blender3dwin rate 36.1% · rank 22 | 983Elo | CC — below the B band | 13.11.2026 |
| Design Arena | agents · fullstackwin rate 60.1% · rank 14 | 1,219Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | agents · godotgamedevwin rate 40.1% · rank 21 | 1,142Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | agents · htmlslideswin rate 49.4% · rank 15 | 1,071Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | agents · mobileappswin rate 47.5% · rank 29 | 1,155Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | agents · pdfwin rate 59.3% · rank 8 | 1,243Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | agents · python-pptxslideswin rate 48.5% · rank 18 | 1,156Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | agents · spreadsheetwin rate 37.9% · rank 27 | 1,071Elo | CC — below the B band | 13.11.2026 |
| Design Arena | agents · webappswin rate 55.7% · rank 19 | 1,205Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | models · 3dwin rate 52.8% · rank 24 | 1,269Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | models · asciiartwin rate 46.4% · rank 30 | 1,186Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | models · codecategorieswin rate 50.6% · rank 26 | 1,278Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | models · datavizwin rate 51.7% · rank 16 | 1,283Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | models · gamedevwin rate 49.2% · rank 31 | 1,258Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | models · svgwin rate 47.5% · rank 29 | 1,198Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | models · uicomponentwin rate 55.4% · rank 23 | 1,288Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | models · websitewin rate 49.9% · rank 25 | 1,278Elo | 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) |
|---|---|---|---|---|---|---|
| Morphfp8 | 1,048,576 | $0.298 | $0.916 | — | — | — |
Provider data retrieved: 13.11.2026
Related articles
No published articles identify this model yet.