GPT 5.6 Sol Batch
Neural Network
GPT-5.6 Sol is OpenAI's flagship model for complex reasoning, coding, and agentic tasks, available via API in BotHub.
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 Sol Batch?
GPT-5.6 Sol is OpenAI's flagship GPT-5.6 series model designed for complex reasoning, programming, and agentic scenarios, performing exceptionally well in multi-step development tasks and command-line operations. The batch variant is designed for bulk request processing. You can connect it in BotHub without a VPN or foreign card: pay with a Russian card in rubles for tokens used, with no expiration. Access over 250 neural networks in one window to switch between them and compare responses on the same prompt. A unified OpenAI-compatible API helps integrate the model into your product and switch later without rewriting integration. Chats are encrypted with AES-GCM, and data is not saved. Companies get access to contracts, invoices, EDI, an admin panel, and employee limits. Useful for analyzing and refactoring large repositories, debugging via terminal steps, building agents that plan and execute action chains, solving STEM problems with long logic, or reviewing code against technical specifications.Frequently asked questions about GPT 5.6 Sol 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 that also accepts images and documents as input. A context window of up to 1,050,000 tokens allows you to upload a voluminous report, codebase, or batch of files and work with them entirely: analysis, refactoring, debugging, summaries, and answers based on content.
Up to 128,000 tokens per response, which is a very voluminous text: full documentation, a large code module, or a detailed analysis with calculations. If the material is even longer, ask it to continue in the next message: there is enough context for the model to remember the beginning of the work.
Yes, reasoning before answering is confirmed in our data. The model first works through the task internally and only then provides the result. This is noticeable in math, logic, STEM, and code analysis: the response is more accurate, and there are fewer errors in long chains.
Yes, both. The model calls functions and returns structured JSON according to your schema, making it convenient to integrate into agents and pipelines. In BotHub, this works via a unified OpenAI-compatible API, so you can connect another model without rewriting the integration.
Images and documents — yes. Send a screenshot, diagram, chart, or file and ask to analyze the content: documents are read natively, without an external parser. There is no note in our data about audio and video input, so we won't promise them — check it with your specific task.
Our data does not specify supported languages, so we won't promise anything in advance — it's fairer to test it with your real text. In BotHub, this is easy: pay with Russian cards in rubles, access without VPN or foreign cards, and over 250 models nearby for comparison.