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Models

Cohere’s Rerank Model (Details and Application)

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Rerank models sort text inputs by semantic relevance to a specified query. They are often used to sort search results returned from an existing search solution. Learn more about using Rerank in the best practices guide.

Latest ModelDescriptionModalityEndpoints
rerank-v4.0-proA multilingual model that allows for re-ranking English and non-english documents and semi-structured data (JSON). This is better suited for state-of-the-art quality and complex use-cases than its fast variant.TextRerank
rerank-v4.0-fastA light version of rerank-v4.0-pro, this is a multilingual model that allows for re-ranking English and non-english documents and semi-structured data (JSON). This model is better suited for low latency and high throughput use-cases than its pro variant.TextRerank
rerank-v3.5A model for documents and semi-structured data (JSON). Performs well in English and non-English languages; supports the same languages as embed-multilingual-v3.0. This model has a context length of 4096 tokensTextRerank
rerank-english-v3.0A model that allows for re-ranking English Language documents and semi-structured data (JSON). This model has a context length of 4096 tokens.TextRerank
rerank-multilingual-v3.0A model for documents and semi-structure data (JSON) that are not in English. Supports the same languages as embed-multilingual-v3.0. This model has a context length of 4096 tokens.TextRerank

For each document included in a request, Rerank combines the tokens from the query with the tokens from the document and the combined total counts toward the context limit for a single document. If the combined number of tokens from the query and a given document exceeds the model’s context length for a single document, the document will automatically get chunked and processed in multiple inferences. See our best practice guide for more info about formatting documents for the Rerank endpoint.