GPT 4.1 Nano Batch
Neural Network
GPT-4.1 Nano is a compact OpenAI model with a 1M token context for fast text processing tasks.
Specifications
- Input
- text, images, documents
- Output
- text
- Context size
- 1 047 576 tokens
- Max answer length
- 32 768 tokens
- Capabilities
- function calling
Prompt cost
(per 1M tokens)
Answer cost
(per 1M tokens)
How it works GPT 4.1 Nano Batch?
GPT-4.1 Nano is a compact model from OpenAI's GPT-4.1 line, designed for latency-critical tasks: its small size allows for faster responses and lower costs compared to other models in the series. The 1M token context allows handling long documents, logs, and large exports in a single request, while the batch mode is suitable for deferred processing of large request queues. Via BotHub, the model is available from Russia without a VPN or foreign card, with pay-as-you-go billing in rubles, no subscription, and non-expiring tokens. Over 250 neural networks are available in one window, and a unified OpenAI-compatible API allows switching between them without rewriting integrations; traffic is encrypted via AES-GCM, and data is not stored. It is useful for labeling and classifying thousands of requests, extracting facts and fields from long contracts and reports, summarizing large texts, providing autocomplete in interfaces, or regularly running batch tasks on a knowledge base. Corporate access is available for teams with contracts, invoices, and an admin panel with limits.Frequently asked questions about GPT 4.1 Nano Batch
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-4.1-nano:batch is a compact model from the GPT-4.1 line in batch mode: you send a large array of requests and receive results asynchronously. It is suitable for classification, labeling, data extraction, document summarization, and image parsing when processing volume is more important than an instant response.
In a single response, the model outputs up to 32,768 tokens — that's dozens of pages of text. It is more than enough for a long report or a detailed analysis of a voluminous instruction, and if you need more, break the task into parts and assemble the result piece by piece.
A separate reasoning mode is not specified in our data. However, you can ask the model to write out the solution step-by-step directly in the prompt — this is usually sufficient for simple logic. If you need deep analysis, reasoning models are available nearby in BotHub, and switching takes just one action.
Yes. The model supports function calling and structured JSON output, making it easy to integrate into pipelines: describe the response schema and get predictable fields without post-processing. In BotHub, this works via a unified OpenAI-compatible API, so you won't have to rewrite your integration.
Images and documents are accepted as input — you can send a scan, PDF, or image along with a text request to get an analysis, description, or extracted data. Audio and video, as well as generation other than text, are not specified in our data. The context window is 1,047,576 tokens.
There is no information in our data about the language of prompts and responses, so we make no promises in advance. The easiest way to check is with your own tasks: send a typical request and compare the response with another model in the same BotHub window. Payment in rubles, without VPN or foreign card.