GPT-4 Turbo Batch
Neural Network
OpenAI's GPT-4 Turbo: a model with image support and function calling, gpt-4 turbo api for batch request processing.
Specifications
- Input
- text, images, documents
- Output
- text
- Context size
- 128 000 tokens
- Max answer length
- 4 096 tokens
- Capabilities
- function calling
Prompt cost
(per 1M tokens)
Answer cost
(per 1M tokens)
How it works GPT-4 Turbo Batch?
OpenAI's GPT-4 Turbo works with more than just text: the model understands images, and visual queries are compatible with JSON mode and function calling. Its training data covers the period up to December 2023. The batch variant is designed for batch processing where request volume matters more than individual response speed. Through BotHub, you can connect to the model from Russia without a VPN or foreign card, paying with a Russian card in rubles only for the tokens you use: no subscription is required, and unused tokens do not expire. In the same window, you have access to over 250 models with quick switching, a unified OpenAI-compatible API, AES-GCM encryption, and corporate terms with contracts, invoices, electronic document management, and an admin panel with limits. Simple scenarios include: code analysis and refactoring, bug hunting, mass classification and labeling of large text datasets, recognition of screenshot, diagram, and photo content, assembling structured JSON responses for your backend, and agents that call external functions and services.Frequently asked questions about GPT-4 Turbo 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.
This is a model with a 128,000 token context and an output of up to 4,096 tokens per response. It accepts text, images, and documents, supports function calling, and structured JSON output. It is suitable for document parsing, working with code, image analysis, and batch data processing via the unified BotHub API.
The volume of a single response is limited to 4,096 output tokens, while the input context holds up to 128,000 tokens. This means you can input a large document or long correspondence, but the response will be compact. For voluminous materials, it is more convenient to break the task into several requests.
A separate step-by-step reasoning mode is not noted in our data for gpt-4-turbo:batch. However, the model confidently handles multi-step tasks: parsing conditions, logical deductions, and programming tasks. If you specifically need a step-by-step thought process, you can request it directly in the prompt text.
Yes, function calling and structured JSON output for gpt-4-turbo:batch are confirmed. This allows you to embed the model into applications: it returns a response in a predefined schema, and external tools are called upon its request. Convenient for integrations, agents, and data processing without manual text parsing.
gpt-4-turbo:batch accepts text, images, and documents as input — for example, you can upload a scan or screenshot and ask a question about it. Audio and video are not noted in the model data, so you should not count on them being processed. For audio and video clips, other models in BotHub are suitable.
There is no separate assessment of Russian language quality in our data for gpt-4-turbo:batch, so we will not promise a specific level. The model works with multilingual texts and usually correctly understands Russian queries. If phrasing is critical, test it with your own examples — it will take a couple of requests.