How it works Rerank 5 Fast?
Rerank 5 Fast is a reranking model from Cohere, a low-latency version of the Rerank 5 family. It receives a query and a set of retrieved documents, evaluates each for relevance, and reorders the output, keeping truly relevant snippets at the top. The first search stage retrieves candidates quickly but roughly, while the reranker refines the order and reduces the proportion of irrelevant snippets in the context. Through BotHub, the model connects alongside others—without VPN or foreign cards, with pay-as-you-go in rubles, and access via a single OpenAI-compatible API. Use cases: refining corporate search, selecting snippets for RAG, sorting knowledge base results, and filtering candidates in high-load services.Frequently asked questions about Rerank 5 Fast
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.
rerank-5-fast is a reranker from Cohere. It receives a query and a list of documents, evaluates the relevance of each, and arranges them in the correct order. It is useful for knowledge base search, RAG systems, and result selection where it is important to bring the most suitable snippets to the top.
It does not write its own text: the output is not a coherent response, but relevance scores and the order of documents. You can input up to 32,768 tokens of context at a time, so both the query and significant document snippets fit into this volume.
It does not have a separate reasoning mode before answering—this is not noted in our data. The model does not reason aloud but immediately compares the query with each document and provides a score. For long chains of logic, it is better to use a generative model and leave the final sorting to rerank-5-fast.
Function calling and JSON output are not noted in our data. However, the result is an ordered list of documents with scores, which is easy to parse programmatically to integrate reranking into your search pipeline or RAG pipeline.
In our data, the acceptance of images, audio, and video is not noted: the model is described as working with 'query-document' text pairs. If you need to extract meaning from images or transcribe recordings, other models in BotHub are suitable for this, while reranking will remain with rerank-5-fast.
There is no quality assessment for Russian in our data, so we will not promise a specific level. The model compares text pairs, so you should run it on your own queries and documents and check the output order—this is more accurate than any general assurances.