GPT 6 Luna Pro Batch
Neural Network
GPT-6 Luna Pro is an OpenAI model with pro reasoning mode, GPT 6 Luna Pro API for complex tasks and batch request 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 6 Luna Pro Batch?
GPT-6 Luna Pro is the same base model as OpenAI's GPT-6 Luna, but served with the reasoning.mode parameter set to pro: reasoning is more detailed, and answers to complex tasks are higher quality. The batch variant is designed for batch processing, where requests are sent as an array and results are retrieved when ready. Through BotHub, the model is accessible without a VPN or foreign card; you can pay with Russian cards in rubles based on actual usage, without a subscription, and unused tokens do not expire. Over 250 models are available in one window: compare answers and switch via a single OpenAI-compatible API without rewriting your integration. Dialogues are encrypted with AES-GCM, and companies have access to contracts, invoices, electronic document management, and an admin panel with limits. Simple scenarios: analyze complex code and suggest refactoring, find the cause of a bug, solve multi-step math and engineering problems, compile an analytical summary of a long document, or run thousands of similar requests like classification, labeling, and description generation in a batch.Frequently asked questions about GPT 6 Luna 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.
gpt-6-luna-pro:batch is a text model with a reasoning mode, image and document input, and function calling. It is suitable for analyzing large documents and codebases, parsing reports, diagrams, and screenshots, STEM tasks, and for embedding into agent scenarios via the unified BotHub API.
The output limit is 128,000 tokens per response, so the model can output an entire chapter, a detailed technical document, or a large code module without splitting it into parts. And the 1,050,000 token context allows you to keep source materials nearby without losing the thread of the task.
Yes, the reasoning mode is confirmed: before answering, the model builds a chain of steps. This noticeably helps with math, logic, code debugging, and multi-step document analysis. On simple requests, the difference is barely noticeable, but on complex ones, you will get a more accurate and verifiable result.
Yes. Both function calling and structured JSON output are supported, so the model can be connected to external services, databases, and your own tools. In BotHub, this works via a unified OpenAI-compatible API: you can switch to another model without rewriting the integration.
According to our data, images and documents are accepted as input, and files are processed natively: send a screenshot, diagram, scan, or contract and ask for an analysis. We do not have confirmation for audio and video, so for such formats, it is more convenient to use a specialized model in BotHub.
There is no separate evaluation of Russian language performance in our data, so we will not promise accuracy — test the model with your typical requests, that is the most honest test. On the BotHub side, everything is in Russian: the website and Telegram bot, payment by Russian card, and access from Russia without a VPN.