GPT 5.1 Batch
Neural Network
OpenAI's GPT 5.1 Batch is a GPT 5.1 API for batch processing tasks with adaptive reasoning and precise instruction following.
Specifications
- Input
- text, images, documents
- Output
- text
- Context size
- 400 000 tokens
- Max answer length
- 128 000 tokens
- Reasoning effort
- none, low, medium, high
- Capabilities
- reasoning, function calling
Prompt cost
(per 1M tokens)
Answer cost
(per 1M tokens)
How it works GPT 5.1 Batch?
GPT 5.1 Batch is the batch version of OpenAI's GPT-5.1. Compared to GPT-5, the model reasons more consistently, follows instructions more precisely, and sounds more natural in dialogue. Adaptive reasoning helps it allocate effort: answering briefly for simple tasks and thinking longer for complex ones. Via BotHub, it is accessible without a VPN or foreign card: pay with a Russian card in rubles, only for tokens used, which do not expire. Over 250 models are available in one window for easy comparison and switching, and a unified OpenAI-compatible API allows you to connect GPT 5.1 Batch to your services and switch models later without rewriting the integration. Companies have access to contracts, invoices, electronic document management (EDO), and an admin panel with limits. This format is used to process large volumes of text, prepare analytical summaries and reports, label and classify data, generate repetitive descriptions and drafts, and test complex reasoning scenarios before production deployment.Frequently asked questions about GPT 5.1 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.
The model works with text, analyzes images and documents, reasons before answering, and calls external functions. This is a gpt-5.1 variant for batch processing of large tasks: analyzing reports and contracts, working with code and refactoring, analytics, and extracting data from scans and tables.
The output limit is 128,000 tokens per response, so the model easily provides large results in full: documentation, large code modules, detailed reports. And the 400,000-token context window allows fitting source materials and the finished response into a single request.
Yes, reasoning before answering is confirmed. The model processes the task internally before outputting the result, making it more reliable for multi-step logic, mathematics, code analysis, and scenarios where the answer must be derived rather than guessed.
Yes, both. You provide function descriptions, and the model decides which one to call and with what arguments, and can return structured JSON according to a specified schema. Convenient for agents, integrations with your services, and cases where the response goes directly into code.
Images and documents — yes: screenshots, diagrams, scans, formatted files. There is no mention of audio or video in our data, so expect to use visual and text materials. The model returns the result as text: analysis, summary, extracted fields, or a table.
We don't have exact data on supported languages in our card, so we won't make promises — it's easier to test with your own task. In BotHub, the model is available without a VPN or foreign card, with payment via Russian cards, so a test run will take a couple of minutes.