GPT-5 Nano
Neural Network
GPT-5 Nano is a compact, fast OpenAI model: gpt-5-nano API for developer tools and low-latency tasks.
Max answer length
(in tokens)
Context size
(in tokens)
Prompt cost
(per 1M tokens)
Answer cost
(per 1M tokens)
How it works GPT-5 Nano?
GPT-5 Nano is the most compact variant of OpenAI's GPT-5 line, designed for developer tools, frequent short requests, and environments where minimal latency is crucial. While it lacks the reasoning depth of larger GPT-5 models, it responds faster, making it ideal for high-volume request streams where response time is critical. In BotHub, it works without a VPN or foreign card: pay with a Russian card in rubles only for tokens actually used, which never expire. Access over 250 models in one window to compare responses or switch to a heavier model without rewriting your integration—all via a single OpenAI-compatible API with AES-GCM encrypted chats. BotHub provides contracts, invoices, and electronic document management for companies, plus an admin panel with limits. It's useful for embedding assistants into product interfaces, categorizing incoming requests, generating short prompts and code completions, processing large volumes of similar requests, or prototyping multimodal services.Frequently asked questions about GPT-5 Nano
You can use generated results for commercial purposes. You own all rights to the content you create. The only restriction: make sure your prompt does not include copyrighted third-party material. You are responsible for respecting the rights to any input data.
gpt-5-nano is a compact OpenAI model for text tasks: answering questions, rewriting, summarizing, coding, and analyzing documents and images. A 400,000-token context window allows loading large files, and function calling helps integrate it into services.
Up to 128,000 tokens per response—dozens of pages of text. Enough for a long article, detailed document analysis, or large code snippets without splitting. In practice, the volume depends on the task and response detail settings.
Yes, the model builds a chain of thought before answering. You can adjust the depth using the effort parameter: faster and shorter for simple queries, more detailed for logic, math, and code analysis. This is noticeable in tasks where the reasoning path is as important as the answer.
Yes, both are supported: the model calls external functions and returns structured JSON based on a schema. This is useful for agents, parsing documents into fields, and integrations. In BotHub, it's available via a single OpenAI-compatible API, so switching models won't break your code.
Images and documents, yes: you can send scans, PDFs, or screenshots for analysis. Audio and video are not supported as input formats, so for transcribing recordings or analyzing video in BotHub, it's better to use a specialized model.
The model data doesn't specify language performance, so we won't promise exact phrasing—it's best to test it yourself. The BotHub interface is in Russian, accessible from Russia without a VPN or foreign card, with pay-as-you-go billing in rubles, so testing takes just a couple of minutes.