Cohere Rerank 5 mejora la precisión de búsqueda al devolver pasajes relevantes específicos y puntuaciones calibradas, lo que permite a los desarrolladores enviar menos tokens de mayor calidad a los LLMs para un mejor rendimiento de RAG.
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Cohere AI has released Rerank 5, a new reranker family in Pro and Fast tiers. Cohere Rerank 5 is a retrieval model that reorders search results before they reach an LLM. It adds 2 developer features: Context Focus, which returns relevant passages inside each document, and calibrated relevance scores. For RAG and agent builders, the message is simple. Send fewer, better tokens to the LLM. TL;DR Size: Parameter count not disclosed. 32K token context window. Two tiers: rerank-v5.0-pro and rerank-v5.0-fast. Runs on: Hosted API, Model Vault, Microsoft Foundry, Amazon SageMaker, or private VPC and on-prem. Cohere benchmarks speed on a single NVIDIA A100. No open weights or quantized builds. Performance: Pro ranks first on 39 of 46 composite datasets in Cohere’s own evaluation. Best: Pro scores 82.2 on BRIGHT (RCP-nDCG@10), 5.8 points above Voyage AI Rerank 3. Worst: Pro median latency is 11.7 s per 96-document request, versus 4.1 s for Fast. What is new in Cohere Rerank 5? Rerank 5 ships as two models. Pro targets ranking quality on reasoning-heavy queries and long, domain-specific documents. Fast targets low latency and high throughput for agents running many searches. Both models support over 100 languages and a 32K context window. They also rerank semi-structured data such as JSON, tables and YAML records. Upgrading needs no re-indexing. Developers change the model parameter and keep their existing keyword, vector or hybrid search. How does Context Focus work? Context Focus is an optional feature, off by default. A single Rerank call returns document scores plus scored character spans. Each span carries start and end offsets into the original text. Developers set it through the relevant_spans field, with a threshold and max_spans value. Cohere tested it on 30 QA datasets. Removing half the retrieved text kept 96% of answer quality. Removing three quarters kept 87%. Cohere says teams can push to 10x less context at some quality cost. In a search-agent test
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