GPT 6.1 Terra Pro
Neural Network
GPT 6.1 Terra Pro by OpenAI — Terra version with pro mode: higher quality answers on complex tasks
Specifications
- Input
- text, images, documents
- Output
- text
- Context size
- 1 050 000 tokens
- Max answer length
- 128 000 tokens
- Reasoning effort
- low, medium, high, extra high, max
- Capabilities
- reasoning, function calling
Prompt cost
(per 1M tokens)
Answer cost
(per 1M tokens)
How it works GPT 6.1 Terra Pro?
GPT-6.1 Terra Pro is the same GPT-6.1 Terra model from OpenAI, but with pro reasoning mode enabled: answers to complex tasks become higher quality due to deeper processing. The basic capabilities are the same, so it is reasonable to keep pro mode for heavy requests and leave routine ones to the standard mode. The price for depth is token consumption — it is noticeably higher in pro, and it is convenient that BotHub charges per usage: no subscription, balance does not expire. Access is open from Russia — no VPN or foreign card needed, payment with Russian cards in rubles. Over 250 models live in one window: it is convenient to compare the Terra Pro answer with the standard mode or another model without rewriting the integration — BotHub provides a unified OpenAI-compatible API. Correspondence is encrypted via AES-GCM and not saved, and companies have access to contracts, invoices, EDI, admin panel, and limits. Pro mode is useful for architectural analysis and refactoring large modules, multi-step debugging via logs, analytical and scientific calculations, proofreading documents, and in multi-model systems where such a call is enabled only for difficult requests.Frequently asked questions about GPT 6.1 Terra Pro
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, accepts documents and images, can reason before answering, and call functions. The context is 1,050,000 tokens, so it can handle analysis of large documentation sets, long dialogues, code analysis, and multi-step tasks like data collection from various sources and automation via tools.
In one response, the model outputs up to 128,000 tokens — this is the volume of a large chapter, a detailed instruction, or a long report. Such capacity is convenient when you need a complete result without stitching pieces together, but you pay for the length with tokens, so it is better to keep the response size tailored to the task.
Yes, the model's reasoning mode is noted: according to our data, it thinks through steps before providing an answer. This helps where logic and order of actions are important — when analyzing code, checking conditions, planning multi-step tasks, and finding errors in a chain of reasoning.
Yes, both modes are supported. The model can call functions, i.e., access your tools and APIs, and return structured JSON. This is convenient for scenarios where the answer needs to be immediately put to work: extracting fields from documents, filling tables, connecting to internal services via a unified API.
The model accepts images and documents as input, so you can send a scan, document, or screenshot along with a question and ask it to analyze the content. Audio and video input is not noted in our data, so we will not promise it — if this is critical for your task, please clarify before payment.
There is no separate quality assessment for the Russian language in the model's data, so we will not name a specific level. This is a general-purpose language model, and the easiest way to test it with your own phrasing is through practice: ask a couple of typical work tasks in the chat and see if the result satisfies you.