OpenAI: GPT-5.4 Nano (batch)

GPT-5.4 Nano (batch) is a text generation model from OpenAI, listed on api14 under the model id openai/gpt-5.4-nano:batch. It belongs to the GPT-5.4 family, where it is positioned as the most lightweight and cost-efficient variant, optimised for speed-critical and high-volume work. The batch designation indicates that this endpoint is intended for asynchronous, batched request patterns rather than interactive, single-request use.

The model accepts text, image and file inputs, and it returns text only. Its context window is 400,000 tokens, with a maximum output of 128,000 tokens, so it can hold very large inputs and still produce long responses in a single call. The knowledge cutoff is 2025-08-31, which matters if your task depends on recent events or data. Supported request parameters include max_tokens, response_format, structured_outputs, seed, tool_choice and tools, along with reasoning controls: reasoning, reasoning_effort, include_reasoning and verbosity. That combination means you can steer both how much reasoning effort the model spends and how verbose its answers are.

Because it handles images and files alongside text, the model fits tasks such as document question answering, extracting structured fields from scanned forms or PDFs, summarising long reports, and classifying or tagging large volumes of content. The tool and structured output parameters make it usable for function calling and for producing JSON that downstream code can parse directly. The seed parameter helps when you need repeatable outputs across runs, which is useful for testing and for pipelines where consistency matters more than variety.

To call it through api14, use the OpenAI-compatible chat completions interface with the model id openai/gpt-5.4-nano:batch. Point your existing OpenAI SDK or HTTP client at the api14 base URL, keep your usual message format, and pass any of the supported parameters in the request body: max_tokens, response_format, structured_outputs, seed, tool_choice, tools, reasoning, reasoning_effort, include_reasoning and verbosity. Because the endpoint is batch-oriented, expect results to be returned asynchronously rather than in a tight interactive loop.

Keep the batch nature in mind when choosing this model. If your application needs immediate, turn-by-turn responses, a non-batch variant of the same family will usually be the better fit. Similarly, if a task demands output beyond 128,000 tokens or knowledge past the 2025-08-31 cutoff, look at other options. For high-volume, latency-tolerant jobs where cost efficiency and throughput matter most, this model is a reasonable default.