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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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Frequently Asked Questions

  • What is instruction-following in Voyage Rerank 2.5?

    Instruction-following lets you steer relevance scores with natural language. You pass an instruction along with the query and candidate documents, and Voyage Rerank 2.5 adjusts scoring to reflect the instruction. On the MAIR benchmark, this capability lifts Voyage Rerank 2.5 12.70% above Cohere Rerank v3.5.

  • What is the difference between Voyage Rerank 2.5 and rerank-2.5-lite?

    Voyage Rerank 2.5 is the quality-optimized tier and rerank-2.5-lite is the latency-and-quality balanced tier. Both support instruction-following and the same context window of 32K tokens. Choose Voyage Rerank 2.5 when top-result accuracy is the priority; choose rerank-2.5-lite when per-query cost or latency matters more.

  • How does Voyage Rerank 2.5 compare to Cohere Rerank v3.5?

    Across 93 retrieval datasets, Voyage Rerank 2.5 improves accuracy by 7.94% over Cohere Rerank v3.5. Averaged across four first-stage retrieval methods, it outperforms Cohere Rerank v3.5 by 3.26% on NDCG@10 and by 12.70% on the MAIR instruction-following benchmark.

  • What is the context window for Voyage Rerank 2.5?

    32K tokens. That is double the window of rerank-2 and eight times that of Cohere Rerank v3.5, so longer query-document pairs fit a single rerank call without truncation.

  • Which first-stage retrievers does Voyage Rerank 2.5 work with?

    Voyage Rerank 2.5 reorders candidates from any first-stage method. Voyage AI benchmarks it on BM25 lexical search, OpenAI text-embedding-3-large, voyage-3-large, and voyage-3.5. You can keep your existing embedding model and add Voyage Rerank 2.5 as a second-stage reranker.

  • Does Voyage Rerank 2.5 support multilingual retrieval?

    Yes. Voyage Rerank 2.5 reranks across many languages without separate per-language models. Voyage AI reports consistent improvement across all evaluated languages and first-stage retrieval methods.

  • How do I access Voyage Rerank 2.5 through Vercel AI Gateway?

    Add your Voyage AI API key in AI Gateway settings, then send rerank requests through AI Gateway. AI Gateway authenticates requests and records usage. You can call Voyage Rerank 2.5 through the AI SDK alongside Chat Completions, Responses, and Messages API formats.

  • Is Zero Data Retention available for Voyage Rerank 2.5?

    Zero Data Retention is not currently available for this model. Zero Data Retention is offered on a per-provider basis. See https://vercel.com/docs/ai-gateway/capabilities/zdr for details.