OpenAI: GPT-5 Nano

GPT-5 Nano is a text generation model from OpenAI, positioned as the smallest and fastest variant in the GPT-5 system. It is built for developer tools, rapid interactions and ultra low latency environments. The model was released by the provider on 2025-08-07 and has a knowledge cutoff of 2024-05-31. Its defining tradeoff is speed and cost efficiency at the expense of reasoning depth: OpenAI describes it as limited in reasoning depth compared to its larger siblings in the GPT-5 family. For a developer, that means it is best treated as a fast, high throughput workhorse rather than a deep analytical engine.

The model accepts text, image and file inputs and produces text output only. It has a context window of 400,000 tokens, which is large enough to hold long documents, extended conversation histories or sizeable code files in a single request. The maximum output length is 128,000 tokens. Because images and files are accepted as input, the model can be used for tasks that mix prose with visual or document attachments, though everything it returns is text. There is no audio or video input, and no non text output such as embeddings or images.

Those capabilities point to a fairly specific set of use cases. High volume classification, tagging and routing of incoming text fit well, since latency matters more than deep reasoning there. Summarisation of long inputs, including documents and images passed in as files, is another natural fit given the large context window. Extraction into a fixed schema, quick chat assistants that need to answer in milliseconds, and agentic loops where a model is called many times per task are all reasonable. Tasks that hinge on multi step reasoning, subtle judgement or long chains of deduction are where the reduced reasoning depth becomes a liability, and a larger GPT-5 variant would be the better choice.

Through api14 you call the model with the id "openai/gpt-5-nano" using the OpenAI compatible API, so existing OpenAI SDKs and HTTP clients work with only a base URL and key change. The request parameters it supports are include_reasoning, max_completion_tokens, max_tokens, reasoning, reasoning_effort, response_format, seed, structured_outputs, tool_choice, tools and verbosity. In practice that means you can cap output with max_tokens or max_completion_tokens, control reasoning behaviour through reasoning_effort and the reasoning and verbosity options, request JSON or schema constrained output with response_format and structured_outputs, make generation reproducible with seed, and give the model tools with tools and tool_choice.

The main limitation to weigh is reasoning depth. If your workload depends on hard problem solving, careful multi step analysis or high stakes judgement, this variant is the wrong tool and a larger GPT-5 model is the safer pick. For everything else, where the job is fast, repeated and well specified, GPT-5 Nano is the variant to reach for first.