Claude Fable 5 Batch
Neural Network
Claude Fable 5 is an Anthropic model for autonomous work with knowledge and code; Claude Fable 5 API accepts text, images, and files.
Specifications
- Input
- text, images, documents
- Output
- text
- Context size
- 1 000 000 tokens
- Max answer length
- 128 000 tokens
- Reasoning effort
- low, medium, high, extra high, max
- Capabilities
- reasoning, function calling
Prompt cost
(per 1M tokens)
Answer cost
(per 1M tokens)
How it works Claude Fable 5 Batch?
Claude Fable 5 is an Anthropic Mythos-class model for autonomous work with knowledge and programming. It accepts text, images, and files, responds with text, and supports reasoning, making it suitable for tasks requiring a chain of steps: analyzing materials, comparing them, and completing the result. The batch variant is designed for batch processing, where requests are sent as an array and results are retrieved upon completion. Via BotHub, the model works from Russia without a VPN or foreign card; payment is in rubles based on usage, and unused tokens do not expire. Over 250 models are available in one window with a unified OpenAI-compatible API, allowing you to switch models in a multi-model system without rewriting integrations. Companies have access to contracts, invoices, electronic document management, and an admin panel with limits. Engineers will find it useful for refactoring and analyzing unfamiliar code, analysts for processing sets of documents and reports, product teams for mass classification of requests, and researchers for long reasoning on STEM tasks.Frequently asked questions about Claude Fable 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 that accepts not only text but also images and documents. It is suitable for analyzing large materials, working with code and refactoring, reasoning and STEM tasks, as well as scenarios involving external tool calls.
The output limit is up to 128,000 tokens per response, which is enough for a voluminous document, detailed analysis, or a large code module in its entirety. The context window reaches 1,000,000 tokens, so the response can be built based on a large array of input data.
Yes, the reasoning mode is confirmed: the model can break down a task into steps before the final answer. This is useful in mathematics, logic, code debugging, and analyzing contradictory documents, where not only the result but also a reproducible chain of reasoning is important.
Yes, both function calling and structured JSON output are supported. The model can be connected to your tools, databases, and services, and responses can be provided in a strict schema for automated processing. In BotHub, this is done via a unified OpenAI-compatible API.
According to our data, images and documents are accepted as input, including natively: you can send a screenshot, diagram, table, or PDF and get a text analysis. Audio and video support is not noted in the model's specifications.
There are no separate measurements by language in our data, so it is more reliable to test the model on your own prompts and documents. In BotHub, this is convenient: all models are in one window, switching takes a second, and payment is based on actual token usage.