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rerank-4-pro, rerank-4-fast, and rerank-v3.5.Standard embedding-based search finds relevant chunks, but rerankers determine which ones are most relevant to a query. These models act as a high-accuracy filter, identifying the best information before it reaches the model. Integrating these tools removes the need for separate infrastructure to handle the precision layer of a production RAG stack.
You can now implement reranking via the POST /api/v1/rerank endpoint by passing a query and document chunks. The available models support 100+ languages with no pre-processing. rerank-4-pro offers a 32K context window, while rerank-4-fast is optimized for applications requiring the lowest possible latency.
New on OpenRouter: Reranker Models 🔍 Why add a reranker to your RAG pipeline? Embedding search finds relevant chunks, but rerankers tell you which ones are MOST relevant and result in significantly better answers. Now live via a single API endpoint, starting with @cohere! https://t.co/ZE0vrLOHHl
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