OpenAI: GPT-4.1 Nano

GPT-4.1 Nano is a text model from OpenAI, part of the GPT-4.1 series. OpenAI positions it as the smallest and fastest member of that family, aimed at tasks where low latency matters. It was released by the provider on 14 April 2025, and its knowledge cutoff is 30 June 2024, so anything after that date is outside what it learned during training.

The model accepts text, images and files as input, and returns text only. Its context window is 1,047,576 tokens, which is large enough to hold very long documents, whole codebases or extended conversation histories in a single request. Output is capped at 32,768 tokens per response. Because input can include images and files alongside text, you can send a mix of formats in one call rather than converting everything to plain text first.

Those capabilities point to a fairly specific set of uses. Long-context work such as summarising or querying large documents, searching across a big corpus, and extracting structured data from mixed text and image inputs all fit within the window and the supported input types. The model also supports tool calling, so it can drive function calls and simple agent loops. Its low latency makes it a reasonable choice for interactive features where a user is waiting on the response, and for high-volume background jobs where you want fast turnaround on each item.

You call it through api14's OpenAI-compatible API using the model id "openai/gpt-4.1-nano", which means existing OpenAI SDKs and clients work with only a base URL and key change. The request parameters it supports are max_completion_tokens, max_tokens, response_format, seed, structured_outputs, temperature, tool_choice, tools and top_p. In practice that covers output length control, deterministic sampling via seed, JSON and structured output modes, sampling controls, and tool or function calling with a choice of how tools are selected.

Two limits are worth keeping in mind when you decide. Output stops at 32,768 tokens, so tasks that need a very long single response may not fit, and the knowledge cutoff of 30 June 2024 means the model has no built-in awareness of later events. If your work depends on either of those, a different model in the series may suit you better.