# Cohere's Rerank Model (Details and Application)

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](/guides/embeddings-vectors-search-retrieval-text-embeddings-reranking-reranking-best-practices).

| Latest Model               | Description                                                                                                                                                                                                                                                        | Modality | Endpoints      |
| -------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | -------- | -------------- |
| `rerank-v4.0-pro`          | A 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.                                               | Text     | [Rerank](/api) |
| `rerank-v4.0-fast`         | A 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. | Text     | [Rerank](/api) |
| `rerank-v3.5`              | A 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 tokens                                                  | Text     | [Rerank](/api) |
| `rerank-english-v3.0`      | A model that allows for re-ranking English Language documents and semi-structured data (JSON). This model has a context length of 4096 tokens.                                                                                                                     | Text     | [Rerank](/api) |
| `rerank-multilingual-v3.0` | A 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.                                                                            | Text     | [Rerank](/api) |

:::callout{intent="note"}
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](/guides/embeddings-vectors-search-retrieval-text-embeddings-reranking-reranking-best-practices) for more info about formatting documents for the Rerank endpoint.
:::

## Related pages

- [Changelog](../changelog.md)
- [Cohere](../index.md)
- [Cohere API](./cohere-api-index.md)
- [Cohere Labs](./cohere-labs-index.md)
- [Cohere Platform](./cohere-platform-index.md)
- [Cookbooks](./cookbooks-index.md)
- [Deployment Options](./deployment-options-index.md)
- [Embeddings (Vectors, Search, Retrieval)](./embeddings-vectors-search-retrieval-index.md)
- [Get Started](./get-started-index.md)
- [Going to Production](./going-to-production-index.md)

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