GPT 5.6 Terra Pro Batch
Neural Network
GPT-5.6 Terra Pro is an OpenAI model with pro reasoning mode: gpt 5.6 terra pro api for complex tasks and batch processing.
Specifications
- Input
- text, images, documents
- Output
- text
- Context size
- 1 050 000 tokens
- Max answer length
- 128 000 tokens
- Reasoning effort
- none, low, medium, high, extra high, max
- Capabilities
- reasoning, function calling
Prompt cost
(per 1M tokens)
Answer cost
(per 1M tokens)
How it works GPT 5.6 Terra Pro Batch?
GPT-5.6 Terra Pro is the same GPT-5.6 Terra model from OpenAI, running with reasoning.mode set to pro: it reasons more thoroughly and provides higher quality answers for complex tasks, while the batch suffix indicates queued batch request processing. In BotHub, it works from Russia without a VPN or foreign card; payment is made with Russian cards in rubles, paying only for tokens used, which do not expire. Alongside it, in one window, are over 250 other neural networks with quick switching, and a unified OpenAI-compatible API allows you to connect the model to your services and change it later without rewriting the integration; chats are encrypted via AES-GCM, and companies have access to contracts, invoices, electronic document management, an admin panel, and limits. Use pro-mode where the cost of error is high: to analyze a complex bug and refactor code, summarize multi-page analytics into conclusions, go through a long chain of engineering or mathematical calculations, or assemble a technical solution from conflicting requirements. Batch execution helps when there are hundreds of similar tasks — classification, labeling, document reconciliation.Frequently asked questions about GPT 5.6 Terra Pro 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 text model with reasoning: text analysis and writing, working with code — refactoring, debugging, review, parsing documents and images, STEM tasks. The 1,050,000 token context allows you to keep large codebases and voluminous reports in a single request, and function calling integrates it into workflows.
Up to 128,000 tokens in a single response. This is enough for a long technical document, a large code module, or a detailed analysis with calculations — without manually stitching parts together. If the answer is longer, just continue the dialogue: the 1,050,000 token context will retain everything written previously.
Yes, reasoning before answering is confirmed. The model first breaks down the task step-by-step and only then answers — this is noticeable in math, logic, code debugging, and multi-step instructions. For simple questions, the gain is smaller, but for complex ones, the answer is more accurate.
Yes, both. Function calling allows the model to access your functions and external services, and structured JSON output provides the answer according to a specified schema — convenient for parsing. In BotHub, this is available via a unified OpenAI-compatible API, so you won't have to rewrite the integration.
Images and documents are accepted as input — screenshots, diagrams, charts, PDFs, and entire files, without prior text extraction. Audio and video are not marked as input formats in our data. The model responds with text: generation of images, sound, or video is not marked in our data.
We do not have data on prompt and output languages, so we won't make any promises here — check it with your own tasks. It's fast: in BotHub, payment is based on actual token usage, without a subscription, and you can compare the answer with another model in the same window.