openai
OpenAI: GPT-5 Mini (batch)
openai/gpt-5-mini:batch
Our take
AI-assisted analysis based on the sources below. Suitability is an editorial assessment; test it on your own workload.
GPT-5 Mini (batch) is a compact text-output model listed on OpenRouter as openai/gpt-5-mini:batch, described by the catalog as a lighter-weight version of GPT-5 aimed at simpler reasoning tasks with reduced latency and cost. The listing reports text, image, and file inputs over a 400,000-token context window, mandatory reasoning with configurable effort levels, and parameters for tools, structured outputs, seed, and verbosity. Only the OpenRouter catalog was provided as evidence; an independent model card was not available in this source pack, so behavior should be validated on real workloads.[1]
Strengths
- The catalog reports a 400,000-token context window accepting text, image, and file inputs, which suits long or mixed-document prompts.[1]
- Reasoning effort is configurable across four levels, and the listing reports support for tool calling, structured outputs, response formatting, seed, and verbosity parameters.[1]
- OpenRouter's description presents this variant as retaining GPT-5's instruction-following and safety tuning while reducing latency and cost, though this is a vendor-reported positioning rather than a measured result.[1]
Limitations
- Evidence is catalog-only: no independent model card or usage documentation was supplied, so reported capabilities and output quality need validation on your own workloads.[1]
- Reasoning is marked mandatory and cannot be disabled, only adjusted by effort level; the catalog also lists a knowledge cutoff of 2024-05-31.[1]
- The listing does not explain how the batch variant's delivery or behavior differs from the standard endpoint, and it shows an expiration date of 2026-12-11, suggesting availability may be time-limited.[1]
Where to use it
- May suit high-volume, latency-tolerant text jobs such as summarization or classification over long documents, if the batch endpoint fits your pipeline.[1]
- Worth evaluating for extraction workflows that read image or file inputs and emit structured output via the supported response_format and structured_outputs parameters.[1]
- A candidate for tool-using assistants where adjustable reasoning effort and reported lower cost matter more than maximum capability.[1]
Sources
- [1] OpenRouter catalog · OpenAI: GPT-5 Mini (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 Mini is a compact version of GPT-5, designed to handle lighter-weight reasoning tasks. It provides the same instruction-following and safety-tuning benefits as GPT-5, but with reduced latency and cost....
Specifications
- Model ID
- openai/gpt-5-mini:batch
- Canonical ID
- openai/gpt-5-mini-2025-08-07
- Creator
- openai
- Added to OpenRouter
- 07.08.2025
- Context length
- 400,000
- Modality
- text+image+file->text
- Input modalities
- text, image, file
- Output modalities
- text
- Token pricing
- USD per 1M tokens: in $0.125 / out $1
- Knowledge cutoff
- 31.05.2024
- Expiration date
- 11.12.2026
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: mandatory · default medium · efforts: high, medium, low, minimal · parameters: include_reasoning, reasoning, reasoning_effort |
| Coding | CC — below the B band | — |
| Agents | CC — below the B band | parameters: structured_outputs, tool_choice, tools |
| Writing | No measurement | output: text |
| Reasoning | No measurement | reasoning: mandatory · default medium · efforts: high, medium, low, minimal · 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 |
|---|---|---|---|---|
| Artificial Analysis | Coding index | 15.6 | CC — below the B band | 13.11.2026 |
| Artificial Analysis | Agentic index | 6.8 | CC — below the B band | 13.11.2026 |
| Design Arena | models · 3dwin rate 36.9% · rank 116 | 1,020Elo | CC — below the B band | 13.11.2026 |
| Design Arena | models · asciiartwin rate 44.8% · rank 57 | 1,120Elo | CC — below the B band | 13.11.2026 |
| Design Arena | models · codecategorieswin rate 43.5% · rank 106 | 1,105Elo | CC — below the B band | 13.11.2026 |
| Design Arena | models · datavizwin rate 43.7% · rank 92 | 1,129Elo | CC — below the B band | 13.11.2026 |
| Design Arena | models · gamedevwin rate 46.5% · rank 93 | 1,120Elo | CC — below the B band | 13.11.2026 |
| Design Arena | models · svgwin rate 45.8% · rank 71 | 1,083Elo | CC — below the B band | 13.11.2026 |
| Design Arena | models · uicomponentwin rate 41.9% · rank 99 | 1,097Elo | CC — below the B band | 13.11.2026 |
| Design Arena | models · websitewin rate 44.3% · rank 105 | 1,118Elo | 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 | $0.125 | $1 | — | — | — |
Provider data retrieved: 13.11.2026
Related articles
No published articles identify this model yet.