GPT-6 Sol
Neural Network
GPT-6 Sol is an OpenAI model from the GPT-6 series for demanding professional tasks; access GPT-6 Sol online and 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-6 Sol?
GPT-6 Sol sits in OpenAI's GPT-6 lineup between the flagship GPT-6 Astra and the fast GPT-6 Luna: it's a high-tier model designed for cost-effective performance. It is tailored for demanding professional tasks requiring deep insights while keeping costs under control for high-volume requests. In BotHub, GPT-6 Sol works without a VPN or foreign card: pay with a Russian card in rubles only for what you use, with no subscription, and unused tokens do not expire. Over 250 models are available in one window, making it easy to compare answers and switch via a unified OpenAI-compatible API without rewriting integrations. Chats are protected by AES-GCM encryption, and companies get access to contracts, invoices, EDI, an admin panel, and employee limits. It is useful for summarizing bulky documents, preparing business or technical documentation, coding and debugging, reasoning through complex analytical tasks, or connecting the model to internal services and company chatbots.Frequently asked questions about GPT-6 Sol
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-6 Sol is a reasoning text model: it analyzes long documents, writes and refactors code, and helps with debugging, analytics, and STEM tasks. It accepts both text and images/documents as input, making it suitable for working with reports, diagrams, and screenshots.
The single response limit is 128,000 tokens, which is dozens of pages at once. Enough for a bulky technical document, a detailed code base analysis, or a long translation without manual splitting. The context window is 1,050,000 tokens.
Yes, the reasoning mode is confirmed: the model thinks through the solution process first and then provides the result. The difference is noticeable in math, logic, multi-step data analysis, and debugging code—where it is important to reach the answer sequentially rather than guessing.
Yes, it supports both function calling and structured JSON output, so the model can be integrated into agents, pipelines, and internal services. In BotHub, this is available via a unified OpenAI-compatible API: you can connect another model without rewriting the integration.
According to our data, images and documents are accepted as input: send a screenshot, diagram, table, or PDF and ask to analyze the content. Audio and video are not listed as input formats, so it is more convenient to choose a specialized model for such tasks in the same window.
We do not have specific data on language proficiency, so we cannot promise specific accuracy—it is easier to test it with a couple of requests. The service itself is fully Russian-friendly: access from Russia without a VPN or foreign card, and payment with Russian cards as you go.