Voyage Rerank 2.5 Lite
Voyage Rerank 2.5 Lite is Voyage AI's generalist reranker balanced for latency and quality. It supports a context window of 32K tokens, instruction-following, and multilingual reranking. It improves accuracy by 7.16% over Cohere Rerank v3.5 across 93 retrieval datasets. Your use subject to Voyage AI's Terms & Privacy Policies.
import { rerank } from 'ai';
const result = await rerank({ model: 'voyage/rerank-2.5-lite', 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.', ],})Providers
Route requests across multiple providers. Copy a provider slug to set your preference. Visit the docs for more info. Using a provider means you agree to their terms, listed under Legal.
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$0.12/M | — | | 04/15/2024 |
About Voyage Rerank 2.5 Lite
Voyage Rerank 2.5 Lite is Voyage AI's generalist reranker released August 11, 2025, optimized for both latency and quality. It reorders candidate documents returned by a first-stage retriever, with a context window of 32K tokens, multilingual support, and the same instruction-following capability as rerank-2.5.
Across 93 retrieval datasets, Voyage Rerank 2.5 Lite improves accuracy by 7.16% over Cohere Rerank v3.5 when paired with four first-stage retrieval methods: BM25 lexical search, OpenAI text-embedding-3-large, voyage-3-large, and voyage-3.5. Averaged across those first-stage methods, Voyage Rerank 2.5 Lite outperforms Cohere Rerank v3.5, Qwen3-Reranker-8B, and rerank-2-lite by 1.93%, 1.01%, and 2.70% respectively on NDCG@10. It performs better than Qwen3-Reranker-8B, the strongest open-source reranker in the comparison, despite being over an order of magnitude smaller.
On the Massive Instructed Retrieval (MAIR) benchmark, Voyage Rerank 2.5 Lite outperforms Cohere Rerank v3.5 by 10.36%. Instruction-following lets you steer relevance scores using natural language without changing your retrieval index. The context window of 32K tokens matches rerank-2.5, so long query-document pairs fit a single rerank call.
What To Consider When Choosing a Provider
- Configuration: Voyage Rerank 2.5 Lite sits in the latency-and-quality balanced tier of the Voyage AI 2.5 reranker series. Pick it when per-query cost and response time matter and you can accept a small accuracy tradeoff versus
rerank-2.5. Most production RAG pipelines fall in this category. - Configuration: If retrieval quality is the dominant constraint,
rerank-2.5is the quality-optimized tier of the same series. The two models share the same context window, instruction-following capability, and multilingual coverage. - Configuration: Voyage Rerank 2.5 Lite pairs with any first-stage retriever. Keep your existing embedding stack and add Voyage Rerank 2.5 Lite as a second-stage reranker to lift top-k quality without changing how documents are indexed.
- Zero Data Retention: AI Gateway does not currently support Zero Data Retention for this model. See the documentation for models that support ZDR.
- Authentication: AI Gateway authenticates requests using an API key or OIDC token. You do not need to manage provider credentials directly.
When to Use Voyage Rerank 2.5 Lite
Best for
- High-volume RAG pipelines: Per-query reranking cost scales with traffic, and Voyage Rerank 2.5 Lite balances accuracy with throughput
- Latency-sensitive search: Customer-facing search where end-to-end response time matters and a reranker stage must stay fast
- Instruction-driven relevance: Encode preferences such as recency or source authority in natural language without retraining
- Multilingual reranking: One reranker covers retrieval across many languages
- Cost-sensitive production: Strong reranking quality without the full price tier of
rerank-2.5
Consider alternatives when
- Top-tier accuracy is the priority:
rerank-2.5is the quality-optimized tier and improves on Cohere Rerank v3.5 by 7.94% versus 7.16% for Voyage Rerank 2.5 Lite - Open-source-only constraint: Qwen3-Reranker-8B is available for self-hosting, though Voyage Rerank 2.5 Lite outperforms it on Voyage's published benchmarks
- Single-language English-only retrieval: A monolingual reranker may suffice when other languages are out of scope
- No reranker stage needed: Strong first-stage retrievers like
voyage-3.5may meet your accuracy targets without a second pass
Conclusion
Voyage Rerank 2.5 Lite balances reranking quality and per-query cost in Voyage AI's 2.5 reranker series. Instruction-following, multilingual coverage, and a context window of 32K tokens let you upgrade RAG accuracy without retraining your retrieval stack. Route requests through AI Gateway to swap between Voyage Rerank 2.5 Lite and rerank-2.5 as your accuracy and cost targets evolve.