Rerank 4 Fast takes a search question and a list of candidate documents and returns them ordered by relevance, reading up to about 32 pages per document, question included, in more than 100 languages. It is the lighter of the two Rerank 4 models, suited to quick replies and heavy traffic, so use it where many searches arrive at once.
Rerank 5 Pro
Newby CohereReleased 3 November 2026
Rerank 5 Pro is the higher-accuracy tier of Cohere's Rerank 5 family of reranking models. It scores and reorders candidate documents against a query and is suited for retrieval pipelines...
About 32 pages per search
Questions about Rerank 5 Pro
Is Rerank 5 Pro still on sale?
No. Cohere has retired it. Other Cohere models are on sale.
What is Rerank 5 Pro best for?
The lab has not said.
Does Rerank 5 Pro work in Arabic, French or Darja?
Cohere names no languages for Rerank 5 Pro. Arabic, French and Darja: not stated.
How much text per document?
Up to 32 768 tokens for the query and one document together.
Who makes Rerank 5 Pro, and when was it released?
Cohere. Released 3 November 2026.
Other Cohere models, priced in dinars
See all Cohere modelsYou send Rerank 4 Pro a search question and a list of candidate documents, and it returns them ordered by relevance. Each document, question included, can run to about 32 pages, in more than 100 languages, and data records are accepted alongside text. Use it as a second pass after a keyword search or a search by meaning, for hard questions and long documents where result quality counts most. Rerank 4 Fast is the lighter version for quick replies; Rerank v3.5 reads only about 4 pages per document.
Cohere's earlier reranker, from before Rerank 4, reordering semantic or keyword search results by relevance and handling multi-aspect and semi-structured data across more than 100 languages
This model accepts text and pictures as input to generate numerical representations of the content. It can process up to 120 pages of material simultaneously. Its design focuses on being a fast and economical option for content representation tasks. The model is limited to processing and outputting these representations and cannot perform tasks requiring complex reasoning or generating new written material.