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Voyage Rerank 2.5

Voyage Rerank 2.5 is Voyage AI's generalist reranker optimized for retrieval quality. It supports a context window of 32K tokens, instruction-following over natural language steering, and multilingual reranking. On 93 retrieval datasets, it improves accuracy by 7.94% over Cohere Rerank v3.5.

Rerank
index.ts
import { rerank } from 'ai';


const result = await rerank({
  model: 'voyage/rerank-2.5',
  query: 'What is the capital of France?',
  documents: [
    'Paris is the capital of France.',
    'Berlin is the capital of Germany.',
    'Madrid is the capital of Spain.',
  ],
})
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About Voyage Rerank 2.5

Voyage Rerank 2.5 is Voyage AI's generalist reranker released August 11, 2025. It reorders candidate documents returned by a first-stage retriever to lift top-k relevance, with a context window of 32K tokens, multilingual support, and instruction-following capabilities new to the rerank family.

Across 93 retrieval datasets, Voyage Rerank 2.5 improves accuracy by 7.94% over Cohere Rerank v3.5 when paired with four different first-stage methods: BM25 lexical search, OpenAI text-embedding-3-large, voyage-3-large, and voyage-3.5. Averaged across these first-stage retrieval methods, Voyage Rerank 2.5 outperforms Cohere Rerank v3.5, Qwen3-Reranker-8B, and rerank-2 by 3.26%, 2.34%, and 1.35% respectively on NDCG@10.

Instruction-following lets you steer relevance scores using natural language. You can describe the kind of relevance you want ('prefer documents that cite primary sources' or 'rank legal precedents above commentary') and Voyage Rerank 2.5 adjusts its scoring accordingly. On the Massive Instructed Retrieval (MAIR) benchmark, Voyage Rerank 2.5 outperforms Cohere Rerank v3.5 by 12.70%. The context window of 32K tokens is double that of rerank-2 and eight times that of Cohere Rerank v3.5, so longer query-document pairs fit a single rerank call without truncation.