Cohere: Rerank v3.5

cohere/rerank-v3.5

Rerank v3.5 is designed to reorder search results for improved relevance. It supports multi-aspect and semi-structured data reranking over 100+ languages. Ideal for refining results from semantic or keyword search pipelines.

Modalities

Price

$0.001per search

Context

Released

Apr 5, 2026

Sample code and API for Rerank v3.5

OpenRouter normalizes requests and responses across providers for you.

Get your API key

Create an API key from your OpenRouter dashboard and set it as an environment variable:

Make your first request

Use cohere/rerank-v3.5 with the OpenRouter API:

OpenRouter provides a rerank API that reorders documents by relevance to a query. Pass a query and a list of documents, and the model returns them ranked by relevance score.

In the examples below, the OpenRouter-specific headers are optional. Setting them allows your app to appear on the OpenRouter leaderboards.

Using third-party SDKs

For information about using third-party SDKs and frameworks with OpenRouter, please see our frameworks documentation.

Endpoint

POSThttps://openrouter.ai/api/v1/rerank

AuthorizationBearer $OPENROUTER_API_KEY

Content-Typeapplication/json

HTTP-Refereroptional — your site URL, for rankings

X-Titleoptional — your site name, for rankings

Modelcohere/rerank-v3.5

Parameters

Name	Type	Default	Description
`max_tokens`	integer	—	This sets the upper limit for the number of tokens the model can generate in response.
`temperature`	float	`1`	This setting influences the variety in the model's responses.
`top_p`	float	`1`	This setting limits the model's choices to a percentage of likely tokens: only the top tokens whose probabilities add up to P.
`stop`	array	—	Stop generation immediately if the model encounter any token specified in the stop array.
`frequency_penalty`	float	`0`	This setting aims to control the repetition of tokens based on how often they appear in the input.
`presence_penalty`	float	`0`	Adjusts how often the model repeats specific tokens already used in the input.
`top_k`	integer	`0`	This limits the model's choice of tokens at each step, making it choose from a smaller set.
`seed`	integer	—	If specified, the inferencing will sample deterministically, such that repeated requests with the same seed and parameters should return the same result.
`response_format`	map	—	Forces the model to produce specific output format.
`structured_outputs`	boolean	—	If the model can return structured outputs using response_format json_schema.