Best Rerank Models for Search and RAG
Model rankings updated April 2026 based on real usage data.
Rerank models improve retrieval systems by reordering candidate documents, passages, or search results according to relevance. They are commonly used in semantic search, retrieval-augmented generation (RAG), recommendations, and knowledge-base applications where the first retrieval step returns too many possible matches. Compare top reranking models on OpenRouter to find the best fit for your search or RAG pipeline.
Top Rerank Models on OpenRouter
Cohere's AI search foundation model for enhancing the relevance of information surfaced within search and RAG systems. Features a 32K context window, multilingual support across 100+ languages, no data pre-processing required, and state of the art performance with low latency.
Cohere's AI search foundation model for enhancing the relevance of information surfaced within search and RAG systems. Features a 32K context window, multilingual support across 100+ languages, no data pre-processing required, and high performance with lowest latency.
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.