Claude Opus 4.7 Batch
Neural Network
Claude Opus 4.7 Batch is an Anthropic model for code and long autonomous agents, Claude Opus 4.7 API for batch tasks.
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, extra high, max
- Capabilities
- reasoning, function calling
Prompt cost
(per 1M tokens)
Answer cost
(per 1M tokens)
How it works Claude Opus 4.7 Batch?
Claude Opus 4.7 is the next generation of the Opus family from Anthropic: the model builds on the strengths of Opus 4.6 in programming and agent scenarios and is designed for long asynchronous agents. The batch variant processes requests in batches and is suitable where an immediate response isn't required, but the workload is large. In BotHub, you run the model without a VPN or foreign card: pay with Russian cards in rubles, only for tokens used, which do not expire. Over 250 models are available in one window—compare responses and switch without rewriting integration: access is via a unified API, traffic is encrypted with AES-GCM, and chats are not saved. For teams, we offer contracts, invoices, EDI, and an admin panel with limits. Useful for refactoring and reviewing large repositories, autonomous agents managing tasks from ticket to pull request, log analysis and debugging, batch processing of documents and datasets, and prototypes for multimodal services.Frequently asked questions about Claude Opus 4.7 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 a one-million token context. It analyzes large codebases, writes and refactors code, debugs errors, analyzes documents and images, solves STEM problems, and conducts long work dialogues. Suitable for analytics, development, and working with large materials.
The output limit is up to 128,000 tokens per response. This is enough for a large code module, detailed technical analysis, a large report, or translating a voluminous document in its entirety, without manually splitting the task into dozens of small requests.
Yes, reasoning mode is supported: the model runs through a chain of steps before the final answer. This is noticeable in math, logic, code debugging, and tasks where many conditions from a long context need to be reconciled. As a result, answers are more accurate but take more time.
Yes. The model works with tools: it calls your functions, passes arguments, and returns a structured response in JSON according to a specified schema. Convenient for agents, document parsing, and integrations. In BotHub, this is available via a unified OpenAI-compatible API.
According to our data, the model accepts text, images, and documents as input, including native file processing. You can send an interface screenshot, diagram, chart, or PDF and ask it to analyze the content. For audio and video, it is more logical to choose a specialized model from the catalog.
Anthropic models work confidently with Russian-language queries: you formulate the task in your usual language, clarify details, and receive a coherent, detailed response. In BotHub, access is open from Russia without a VPN or foreign card, and payment is made with Russian cards in rubles.