📣 Announcing rerank-2.5 and 2.5-lite: our latest generation of rerankers!
• First reranker with instruction-following capabilities
• rerank-2.5 and 2.5-lite are 7.94% and 7.16% more accurate than Cohere Reranker v3.5
• Additional 8.13% and 7.55% performance gain with instruction
📈 The rerank-2.5 series sets a new price-performance frontier even w/o instruction-following. rerank-2.5 improves retrieval quality over rerank-2 by 1.85%, & 2.5-lite improves over 2-lite by 3.40%, while increasing to a 32K context length & maintaining their respective token price.
⬆️ When applied as a second-stage retrieval method atop the strongest embedding model (voyage-3-large), both rerank-2.5 and rerank-2.5-lite are the only rerankers to consistently improve performance across all domains.
🎉 Both models are available today! The first 200 million tokens are free. Get all the details in our latest blog:
https://t.co/XoZRrsbFL0
reranking — A second pass that re-scores retrieved candidates by reading each one against the query, fixing the ordering that fast vector search got approximately right.
context window — The maximum amount of text a model can consider at once — its working memory for the current conversation or task.
token — The chunk of text a model reads and writes in — roughly three-quarters of a word — and the unit AI usage is billed in.
Why it matters
You can steer rerankingA second pass that re-scores retrieved candidates by reading each one against the query, fixing the ordering that fast vector search got approximately right.Full definition → with a natural language instruction instead of retraining or post-filtering, with a reported gain of roughly 8% on top of the base improvement.
