openai
OpenAI: o4 Mini (batch)
openai/o4-mini: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/o4-mini:batch as the batch variant of OpenAI's o4-mini-2025-04-16. Its catalog describes a compact o-series reasoning model intended for fast, cost-efficient operation, with reported multimodal, agentic, and tool-use support. The listing accepts text, image, and file inputs and returns text, offers a large context window, and marks reasoning as mandatory. These are catalog-reported characteristics rather than independent verification; the pack's measured evidence is limited to selected design-arena results.[1]
Strengths
- The catalog reports tool calling, structured-output controls, seed, response formatting, and agentic operation, which may help build text-producing workflows.[1]
- It accepts text, image, and file inputs while producing text, so mixed-material prompts can be handled when non-text output is unnecessary.[1]
- A large context window is listed, potentially allowing longer prompts or bundled materials; actual fit should be tested.[1]
Limitations
- Only OpenRouter catalog evidence was supplied; an independent model card was not available, so provider claims need external verification.[1]
- The catalog marks reasoning as mandatory and lists no supported effort levels, which could limit control over reasoning behavior.[1]
- Measured evidence covers only selected design-arena categories and does not establish performance across general tasks.[1]
Where to use it
- Consider for prototypes that combine documents, images, and text prompts when a text-only answer is acceptable; validate extraction quality on representative inputs.[1]
- May fit agent-style flows that need tool selection, structured outputs, or response schemas; confirm behavior end to end before production.[1]
- Could be evaluated for queued, noninteractive processing of many mixed-input prompts because this listing is a batch variant; confirm latency and routing needs.[1]
Sources
- [1] OpenRouter catalog · OpenAI: o4 Mini (batch)Sources checked: Nov 13, 2026
About this model
Description below is the original text supplied by OpenRouter, shown in the source language.
OpenAI o4-mini is a compact reasoning model in the o-series, optimized for fast, cost-efficient performance while retaining strong multimodal and agentic capabilities. It supports tool use and demonstrates competitive reasoning...
Specifications
- Model ID
- openai/o4-mini:batch
- Canonical ID
- openai/o4-mini-2025-04-16
- Creator
- openai
- Added to OpenRouter
- 16.04.2025
- Context length
- 200,000
- Modality
- text+image+file->text
- Input modalities
- image, text, file
- Output modalities
- text
- Token pricing
- USD per 1M tokens: in $0.550 / out $2.2
- Knowledge cutoff
- 30.06.2024
- 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: mandatory · parameters: include_reasoning, reasoning |
| Coding | No measurement | — |
| Agents | No measurement | parameters: structured_outputs, tool_choice, tools |
| Writing | No measurement | output: text |
| Reasoning | No measurement | reasoning: mandatory · parameters: include_reasoning, reasoning |
| 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 · 3dwin rate 34% · rank 133 | 837Elo | CC — below the B band | 13.11.2026 |
| Design Arena | models · codecategorieswin rate 46.4% · rank 136 | 966Elo | CC — below the B band | 13.11.2026 |
| Design Arena | models · datavizwin rate 50% · rank 120 | 989Elo | CC — below the B band | 13.11.2026 |
| Design Arena | models · gamedevwin rate 50% · rank 126 | 993Elo | CC — below the B band | 13.11.2026 |
| Design Arena | models · uicomponentwin rate 46.9% · rank 124 | 974Elo | CC — below the B band | 13.11.2026 |
| Design Arena | models · websitewin rate 47.1% · rank 136 | 978Elo | 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 | 200,000 | $0.550 | $2.2 | — | — | — |
Provider data retrieved: 13.11.2026
Related articles
No published articles identify this model yet.