Claude Opus 4.5 Batch
Neural Network
Claude Opus 4.5 is an Anthropic model with strong reasoning: Claude Opus 4.5 for code, agentic scenarios, and image processing.
Specifications
- Input
- text, images, documents
- Output
- text
- Context size
- 200 000 tokens
- Max answer length
- 64 000 tokens
- Reasoning effort
- low, medium, high
- Capabilities
- reasoning, function calling
Prompt cost
(per 1M tokens)
Answer cost
(per 1M tokens)
How it works Claude Opus 4.5 Batch?
Claude Opus 4.5 is an Anthropic model for complex engineering work: long reasoning chains, real-world programming tasks, agentic scenarios, and extended computer use for multi-step tasks. Multimodal input allows analyzing not just text, but also images: diagrams, interface screenshots, and charts. The batch processing option is convenient when requests are queued and results are needed in bulk rather than in a dialogue. In BotHub, the model is available from Russia without a VPN or foreign card; payment is made with Russian cards in rubles, based on actual usage, for tokens that do not expire. Over 250 models are available in one window: you can switch and compare answers, and a unified OpenAI-compatible API allows switching models without rewriting integrations; data is encrypted, and for companies, there is a contract, invoice, electronic document management, and an admin panel with limits. Developers will find Claude Opus 4.5 useful for refactoring large repositories, analyzing unfamiliar code, and debugging; analysts for multi-step calculations and STEM tasks; and product teams for agents with function calling and routine automation.Frequently asked questions about Claude Opus 4.5 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.
This is an Anthropic text model with a 200,000 token context. It analyzes long documents, writes and refactors code, helps with analytics and STEM tasks, and works with tools. Batch mode is convenient when you need to run a large volume of similar requests and get results without haste.
Up to 64,000 tokens in a single response — enough for voluminous technical text, a large code module, or a detailed document analysis. If the material doesn't fit entirely, break the task into parts: the 200,000 token context allows keeping all source content in front of the model.
Yes, reasoning before answering is supported: the model can break down the task step-by-step before providing a result. This significantly helps in mathematics, logic, code debugging, and analyzing long documents, where it is important not to guess the answer but to arrive at it sequentially.
Yes. The model supports function calling and structured JSON output, making it easy to integrate into agents, pipelines, and internal services. In BotHub, this works via a unified OpenAI-compatible API — you can switch models without rewriting the integration.
According to our data, images and documents are accepted as input, including in their original file format. You can send an interface screenshot, diagram, contract scan, or report and ask to analyze the content. There is no mention of audio or video in our data.
Our data does not specify supported languages, so we cannot promise anything specific — it is easier to test with your own task in the chat or via API. On the BotHub side, everything is in Russian: interface, support, payment with Russian cards, and access without VPN.