Claude Opus 5 Batch
Neural Network
Claude Opus 5 by Anthropic: a model for complex reasoning, development, and code review; Claude for code in batch mode.
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 5 Batch?
Claude Opus 5 is Anthropic's flagship model designed for complex reasoning, development, and long-running agentic tasks. It excels at end-to-end software project work, code review, debugging, and visual analysis. The batch processing variant is suitable for scenarios where requests can be queued and results retrieved asynchronously without maintaining a connection. In BotHub, the model is accessible from Russia without a VPN or foreign card, with payment in rubles based on actual token usage. A unified OpenAI-compatible API allows switching between models without rewriting integrations. Traffic is encrypted, and companies have access to contracts, invoices, and an admin panel with limits. Claude's batch mode is convenient for regular repository reviews, pull request analysis, mass refactoring, test coverage, log analysis, bug reproduction, interface screenshot and diagram analysis, and long research tasks requiring structured reasoning chains.Frequently asked questions about Claude Opus 5 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.
claude-opus-5:batch works with text, code, and documents: it analyzes large repositories, writes and refactors functions, finds bugs, and prepares analytics on attached files. It accepts images and documents as input, can reason before answering, and call external functions. The context is one million tokens, so an entire project fits into a single request.
In a single response, the model outputs up to 128,000 tokens — that's dozens of pages of coherent text or a large code module in its entirety. It's enough to get ready-made documentation, a long analysis, or a translation of a large file without manually stitching parts together. If you need more, break the task into sequential requests.
Yes, the reasoning mode is confirmed: before the final answer, the model builds a chain of thought. This is noticeable in tasks where step-by-step analysis is more important than a quick response — mathematics, logic, finding the cause of a code error, or comparing architectural options. For simple questions, this mode is usually redundant.
Yes, both. The model calls external functions and returns structured JSON — convenient for agents, connecting to databases and services, and automated pipelines. In BotHub, this works via a unified OpenAI-compatible API, so you won't have to rewrite the call schema when switching to another model.
Images and documents — yes: you can send an interface screenshot, diagram, chart, PDF, or contract and ask to analyze the content, extract data, or explain an error on the screen. For audio and video, it is more logical to use specialized models from the BotHub catalog — they open in the same window without separate integration.
We do not have fixed language characteristics for the model in our data, so we won't promise anything specific — it's easier to test it with your own task: send a sample text and compare the result with another model in the same window. Access from Russia is available without a VPN or foreign card, with payment in rubles.