openai
OpenAI: GPT-5.2 (batch)
openai/gpt-5.2:batch
Our take
AI-assisted analysis based on the sources below. Suitability is an editorial assessment; test it on your own workload.
OpenRouter lists openai/gpt-5.2:batch as an OpenAI text-output model accepting text, image, and file inputs, with a 400,000-token context window. The catalog describes the underlying GPT-5.2 line as offering stronger agentic and long-context performance than GPT-5.1 and using adaptive reasoning; these are catalog claims, not independently verified results. Listed parameters cover tools, structured outputs, reasoning controls, seed, response format, and verbosity. Because the only supplied evidence is OpenRouter, an independent model card was not available, and real workloads should be validated.[1]
Strengths
- Accepts text, image, and file inputs and emits text, according to the OpenRouter catalog.[1]
- Provides a 400,000-token context window, which may reduce chunking for long documents or conversations.[1]
- Exposes optional reasoning controls with several effort levels, plus parameters for tools, structured outputs, response format, seed, and verbosity.[1]
Limitations
- The only supplied evidence is OpenRouter's catalog, and an independent model card was not available in this source pack; its category-level design-arena results should not be treated as workload validation.[1]
- The catalog does not explain the batch variant's queueing, latency, availability, or execution guarantees, so those operational properties remain unknown.[1]
- Knowledge cutoff and expiration date are left blank, limiting assessment of recency and lifecycle status.[1]
Where to use it
- Consider for workloads that transform text, images, and files into text, provided the batch variant's operating model is verified first.[1]
- Consider for long-document or long-conversation synthesis when a very large context window could reduce splitting, subject to quality tests.[1]
- Consider for tool-using or structured-output workflows that benefit from adjustable reasoning effort, subject to task-specific evaluation.[1]
Sources
- [1] OpenRouter catalog · OpenAI: GPT-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.
GPT-5.2 is the latest frontier-grade model in the GPT-5 series, offering stronger agentic and long context perfomance compared to GPT-5.1. It uses adaptive reasoning to allocate computation dynamically, responding quickly...
Specifications
- Model ID
- openai/gpt-5.2:batch
- Canonical ID
- openai/gpt-5.2-20251211
- Creator
- openai
- Added to OpenRouter
- 10.12.2025
- Context length
- 400,000
- Modality
- text+image+file->text
- Input modalities
- file, image, text
- Output modalities
- text
- Token pricing
- USD per 1M tokens: in $0.875 / out $7
- 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 medium · efforts: xhigh, high, medium, low, none · parameters: include_reasoning, reasoning, reasoning_effort |
| Coding | No measurement | — |
| Agents | No measurement | parameters: structured_outputs, tool_choice, tools |
| Writing | No measurement | output: text |
| Reasoning | No measurement | reasoning: optional · default medium · efforts: xhigh, high, medium, low, none · parameters: include_reasoning, reasoning, reasoning_effort |
| Document analysis | No measurement | input: file |
| Vision | No measurement | input: image |
| Image generation | No measurement | — |
| Audio | No measurement | — |
| Video | No measurement | — |
| Embeddings | No measurement | — |
Evaluations
| Source | Benchmark | Value | Grade | Data retrieved |
|---|---|---|---|---|
| Design Arena | models · websitewin rate 54.4% · rank 69 | 1,187Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | agents · androidnativewin rate 49.1% · rank 38 | 1,045Elo | CC — below the B band | 13.11.2026 |
| Design Arena | agents · fullstackwin rate 44.3% · rank 39 | 1,020Elo | CC — below the B band | 13.11.2026 |
| Design Arena | agents · godotgamedevwin rate 48.1% · rank 22 | 1,142Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | agents · mobileappswin rate 46.8% · rank 43 | 1,088Elo | CC — below the B band | 13.11.2026 |
| Design Arena | agents · webappswin rate 45.5% · rank 41 | 1,068Elo | CC — below the B band | 13.11.2026 |
| Design Arena | models · 3dwin rate 41.5% · rank 106 | 1,062Elo | CC — below the B band | 13.11.2026 |
| Design Arena | models · codecategorieswin rate 49.6% · rank 80 | 1,160Elo | CC — below the B band | 13.11.2026 |
| Design Arena | models · datavizwin rate 56.1% · rank 59 | 1,202Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | models · gamedevwin rate 56% · rank 63 | 1,185Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | models · uicomponentwin rate 51.3% · rank 70 | 1,181Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | models · asciiartwin rate 50.7% · rank 45 | 1,147Elo | CC — below the B band | 13.11.2026 |
| Design Arena | models · svgwin rate 53.7% · rank 59 | 1,129Elo | CC — below the B band | 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) |
|---|---|---|---|---|---|---|
| OpenAI | 400,000 | $0.875 | $7 | — | — | — |
Provider data retrieved: 13.11.2026
Related articles
No published articles identify this model yet.