openai
OpenAI: GPT-4.1 Nano (batch)
openai/gpt-4.1-nano:batch
Our take
AI-assisted analysis based on the sources below. Suitability is an editorial assessment; test it on your own workload.
According to the OpenRouter catalog, openai/gpt-4.1-nano:batch is the batch-mode listing of OpenAI's GPT-4.1 Nano, which the catalog describes as the fastest and lowest-cost model in the GPT-4.1 series, positioned for low-latency tasks. It accepts text, image and file inputs and returns text, with a context window of roughly one million tokens plus support for tool calls and structured outputs. The catalog also reports arena results and a coding index, but no independent model card was available in this source pack, so its capabilities should be validated on real workloads.[1]
Strengths
- The OpenRouter catalog describes GPT-4.1 Nano as the fastest and cheapest entry in its series, aimed at low-latency tasks; this is a vendor-provided description, not an independently measured result.[1]
- Multimodal input support (text, image and file) with text output, a roughly one-million-token context window, and support for tools, tool choice and structured outputs are listed in the catalog.[1]
- The catalog reports design-arena entries across several categories and an Artificial Analysis coding index for the underlying canonical model.[1]
Limitations
- The only evidence is the OpenRouter catalog; no independent model card was available in this source pack, so vendor-stated claims are unverified.[1]
- The catalog's reasoning field is null and its agentic and intelligence index fields are empty, leaving those capability areas uncharacterized.[1]
- This is a batch variant; the source does not document its serving behavior, so suitability for interactive versus deferred workloads needs validation.[1]
Where to use it
- May suit high-volume, non-interactive text processing where a batch endpoint and low per-token catalog pricing are priorities; validate throughput and accuracy first.[1]
- A candidate for large-context document or multimodal (image, file) pipelines given the catalog's listed input modalities and large context window.[1]
- Could be trialed for structured extraction or tool-using workflows, since the catalog lists structured_outputs, tools and tool_choice as supported parameters.[1]
Sources
- [1] OpenRouter catalog · OpenAI: GPT-4.1 Nano (batch)Sources checked: Nov 13, 2026
About this model
Description below is the original text supplied by OpenRouter, shown in the source language.
For tasks that demand low latency, GPT‑4.1 nano is the fastest and cheapest model in the GPT-4.1 series. It delivers exceptional performance at a small size with its 1 million...
Specifications
- Model ID
- openai/gpt-4.1-nano:batch
- Canonical ID
- openai/gpt-4.1-nano-2025-04-14
- Creator
- openai
- Added to OpenRouter
- 14.04.2025
- Context length
- 1,047,576
- Modality
- text+image+file->text
- Input modalities
- image, text, file
- Output modalities
- text
- Token pricing
- USD per 1M tokens: in $0.050 / out $0.200
- 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 | — |
| Coding | CC — below the B band | — |
| Agents | No measurement | parameters: structured_outputs, tool_choice, tools |
| Writing | No measurement | output: text |
| Reasoning | No measurement | — |
| 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 | 11.1 | CC — below the B band | 13.11.2026 |
| Design Arena | models · 3dwin rate 46% · rank 128 | 908Elo | CC — below the B band | 13.11.2026 |
| Design Arena | models · codecategorieswin rate 47.3% · rank 137 | 953Elo | CC — below the B band | 13.11.2026 |
| Design Arena | models · datavizwin rate 41.1% · rank 132 | 889Elo | CC — below the B band | 13.11.2026 |
| Design Arena | models · gamedevwin rate 49.6% · rank 131 | 961Elo | CC — below the B band | 13.11.2026 |
| Design Arena | models · uicomponentwin rate 43.9% · rank 134 | 907Elo | CC — below the B band | 13.11.2026 |
| Design Arena | models · websitewin rate 48.1% · rank 139 | 966Elo | 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 | 1,047,576 | $0.050 | $0.200 | — | — | — |
Provider data retrieved: 13.11.2026
Related articles
No published articles identify this model yet.