GPT 5.6 Luna Batch
Neural Network
GPT 5.6 Luna is a fast, cost-effective OpenAI model for chat, classification, and lightweight agent scenarios, available via BotHub API.
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 Luna Batch?
GPT 5.6 Luna is a model from OpenAI's GPT-5.6 line, designed for speed and cost-efficiency. It is built for high-load scenarios where requests flow continuously and low latency is critical. Its profile includes dialogues, classification, and simple agent chains requiring accurate reasoning without high costs. Through BotHub, you can access it from Russia without a VPN or foreign card, paying with Russian cards only for tokens used, not a subscription. All models are in one window for easy comparison, and the unified OpenAI-compatible API allows switching without rewriting integrations. Chats are encrypted, and companies get contracts, invoices, EDI, and an admin panel with limits. It is suitable for first-line support, high-volume chatbots, batch labeling/classification of reviews and tickets, building lightweight agents for routine tasks, mass text processing in batch mode, and offloading draft work from heavier models.Frequently asked questions about GPT 5.6 Luna 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-5.6-luna:batch works with text, images, and documents: it parses large files, answers content-related questions, writes and refactors code, and assists with analytics and STEM tasks. A 1,050,000 token context allows keeping entire codebases and large reports in a single dialogue.
The maximum length of a single response is 128,000 tokens. This is enough for a voluminous technical document, a detailed analysis of a large file, a long translation, or an entire code module without splitting. If the response cuts off, ask it to continue from where it stopped.
Yes, the reasoning mode is confirmed in our data: the model builds a chain of steps before providing the result. This is noticeable in math, logic, code debugging, and tasks requiring the synthesis of facts from large documents. Such responses take slightly longer than usual.
Yes, both function calling and structured JSON output based on your schema are available. The model can become part of an agent or service: it accesses your tools and returns a predictable format. Connection is via the unified OpenAI-compatible BotHub API, and switching models does not require rewriting the integration.
You can input images and documents, with documents read natively without separate recognition: screenshots, diagrams, charts, tables, and formatted files. There is no note in our data regarding audio and video, so it is safer to test such files directly in the interface.
There is no separate quality assessment for Russian in our data, so we cannot make precise promises—please test the model with your tasks in the web version or Telegram bot. The service itself is fully Russian-speaking: payment with Russian cards in rubles, access without VPN or foreign cards.