Claude Opus 4.6 Batch
Neural Network
Claude Opus 4.6 Batch is an Anthropic model for code and agent tasks: Claude Opus 4.6 API for long-running workflows.
Specifications
- Input
- text, images, documents
- Output
- text
- Context size
- 1 000 000 tokens
- Max answer length
- 128 000 tokens
- Reasoning effort
- none, low, medium, high, max
- Capabilities
- reasoning, function calling
Prompt cost
(per 1M tokens)
Answer cost
(per 1M tokens)
How it works Claude Opus 4.6 Batch?
Opus 4.6 is an Anthropic model focused on programming and long professional tasks. It is designed not for single prompts, but for agents that manage the entire process: breaking down tasks, maintaining context, and driving work to completion. The Batch variant is for bulk request processing where volume matters more than instant response. In BotHub, you connect from Russia without a VPN or foreign card, pay with Russian cards per token without a subscription, and switch between Claude and other models in one window. A unified OpenAI-compatible API allows replacing the model in a multi-model system without rewriting integration. Data is encrypted via AES-GCM, and companies get contracts, invoices, EDI, and an admin panel with limits. Useful for writing and refactoring code, debugging large repositories, building agents for long workflows, batch processing documents/texts, and preparing analytics on accumulated data.Frequently asked questions about Claude Opus 4.6 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 an Anthropic text model with reasoning before answering. It analyzes and refactors code, helps with debugging, solves STEM tasks, analyzes documents and images, and writes/edits long materials. The million-token context allows loading an entire repository or a collection of reports at once.
The output limit is 128,000 tokens per response. This is enough for a large code module, detailed documentation, or a comprehensive analytical breakdown without manual assembly. If the response hits the limit, ask it to continue: the million-token context will retain everything started.
Yes, reasoning before answering is confirmed by our data: the model first builds a chain of steps, then formulates the result. This is noticeable in math, logic, debugging, and tasks requiring multiple conditions. Such depth is usually not needed for simple queries.
Yes, both function calling and structured output are supported. The model accesses your tools with the necessary arguments and returns JSON according to a specified schema — the foundation for agents, bots, and pipelines. In BotHub, this is available via a unified OpenAI-compatible API.
Images and documents — yes: send screenshots, diagrams, charts, PDFs, and contracts; the model reads them and responds with text. Audio and video are not supported as input formats — there are separate models for them in the BotHub catalog, which you can switch to in the same window.
We have no formal data on languages, so we won't make promises — evaluate the model on your tasks: editing, correspondence, document analysis. It's easy to check: pay per token, no subscription, with a Russian card and no VPN, and unused tokens do not expire.