OpenAI: GPT-5 Nano (batch)

GPT-5 Nano is a text model from OpenAI, listed on api14 as OpenAI: GPT-5 Nano (batch). It is the smallest and fastest variant in the GPT-5 system, built for developer tools, rapid interactions, and ultra-low latency environments. The provider release date is 2025-08-07, and the knowledge cutoff is 2024-05-31. The batch variant is the version you call through api14 under the model id openai/gpt-5-nano:batch.

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. Compared with the larger models in the GPT-5 family, its reasoning depth is limited, so it suits workloads where speed and throughput matter more than deep multi-step reasoning. It supports reasoning related parameters, which means you can control how much reasoning effort it applies rather than accepting a fixed behaviour.

Those capabilities point to a fairly specific set of use cases. Because it handles text, images, and files in one request, it fits document and screenshot processing tasks such as extraction, classification, summarisation, and tagging. The large context window lets you pass long documents or several files at once without chunking. The low latency profile makes it a reasonable choice for interactive developer tools, autocomplete style features, chat front ends, and high volume pipelines where each call is short. Tool calling and structured outputs make it usable as the decision layer in an agent loop or as a component that must return data in a fixed schema.

You call it through api14's OpenAI-compatible API using the model id openai/gpt-5-nano:batch. The supported request parameters are include_reasoning, max_tokens, reasoning, reasoning_effort, response_format, seed, structured_outputs, tool_choice, tools, and verbosity. In practice, max_tokens caps the response length, response_format and structured_outputs shape the output, tools and tool_choice drive function calling, seed helps with reproducibility, and reasoning_effort and verbosity let you trade off how much internal work and how much prose the model produces.

The main limitation to weigh is reasoning depth. OpenAI describes this variant as limited in reasoning depth compared to its larger siblings, so if your task depends on long chains of inference, complex planning, or subtle judgement across many constraints, a larger GPT-5 model is likely a better fit. For straightforward extraction, classification, short generation, and tool driven flows, this model covers the job.