openai
OpenAI: GPT-5.4 Nano (batch)
openai/gpt-5.4-nano:batch
Our take
AI-assisted analysis based on the sources below. Suitability is an editorial assessment; test it on your own workload.
openai/gpt-5.4-nano:batch is the batch endpoint of OpenAI's GPT-5.4 Nano, described in the OpenRouter catalog as the lightest, most cost-efficient member of the GPT-5.4 family for speed-critical, high-volume work. The catalog lists text, image and file inputs with text output, a 400,000-token context window, and a knowledge cutoff of August 31, 2025. Optional reasoning supports effort levels from none to xhigh, alongside tools, structured outputs, seed and verbosity controls. All evidence here is catalog-reported; independent verification was not available in this source pack.[1]
Strengths
- Catalog-listed multimodal input covering text, images and files, with text output and a 400,000-token context window.[1]
- Configurable optional reasoning with effort levels from none to xhigh, plus tools, structured outputs, seed and verbosity parameters.[1]
- Positioned by the catalog as the family's lightweight, cost-efficient option for latency-sensitive, high-volume work; this is source-reported positioning, not measured performance.[1]
Limitations
- Evidence is catalog-only: no independent model card or first-party documentation was available in this source pack, so capabilities are vendor-catalog claims that need workload validation.[1]
- Reasoning is disabled by default, and no quality, latency or throughput measurements specific to this batch variant are provided in the evidence.[1]
- Batch-specific operational details such as turnaround times, rate limits and availability are not documented in the supplied source.[1]
Where to use it
- High-volume asynchronous text jobs such as summarization, classification and extraction, where a batch endpoint may fit; suggested, not benchmarked for this variant.[1]
- Document and image ingestion pipelines producing text, given the catalog-reported file and image inputs and large context; worth piloting on representative data.[1]
- Batch structured-data generation using the listed tools, response_format and structured_outputs parameters; suitability should be tested rather than assumed.[1]
Sources
- [1] OpenRouter catalog · OpenAI: GPT-5.4 Nano (batch)Sources checked: Nov 13, 2026
About this model
Description below is the original text supplied by OpenRouter, shown in the source language.
GPT-5.4 nano is the most lightweight and cost-efficient variant of the GPT-5.4 family, optimized for speed-critical and high-volume tasks. It supports text and image inputs and is designed for low-latency...
Specifications
- Model ID
- openai/gpt-5.4-nano:batch
- Canonical ID
- openai/gpt-5.4-nano-20260317
- Creator
- openai
- Added to OpenRouter
- 17.03.2026
- Context length
- 400,000
- Modality
- text+image+file->text
- Input modalities
- file, image, text
- Output modalities
- text
- Token pricing
- USD per 1M tokens: in $0.100 / out $0.625
- Knowledge cutoff
- 31.08.2025
- 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 | BB — next 30% of the cohort | — |
| Agents | BB — next 30% of the cohort | 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 |
|---|---|---|---|---|
| Artificial Analysis | Coding index | 56.1 | BB — next 30% of the cohort | 13.11.2026 |
| Artificial Analysis | Agentic index | 16 | BB — next 30% 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) |
|---|---|---|---|---|---|---|
| OpenAI | 400,000 | $0.100 | $0.625 | — | — | — |
Provider data retrieved: 13.11.2026
Related articles
No published articles identify this model yet.