rerank-3 and rerank-3-lite: the next generation of Voyage rerankers

Voyage AI released the Rerank 3 series, comprising rerank-3 and rerank-3-lite, which outperform Cohere Rerank v4.0 Pro by 2.72% and 2.23% respectively across 95 retrieval datasets at the same price as the prior Rerank 2.5 models. On long-document retrieval, rerank-3 beats rerank-2.5 by 3.35% and Cohere Rerank v4.0 Pro by 13.84%, while rerank-3-lite matches rerank-2.5 retrieval quality at 40% of the price. Both models retain the 32K token context length and instruction-following capability of the Rerank 2.5 series, with relevance scores calibrated so existing thresholds continue to work.

TL;DR – We are excited to introduce the Rerank 3 series, the next generation of Voyage rerankers. rerank-3 and rerank-3-lite improve upon rerank-2.5 and rerank-2.5-lite across nearly all domains at the same price, with the largest gains on long documents and code. On our standard suite of 95 retrieval datasets, rerank-3 and rerank-3-lite outperform Cohere Rerank v4.0 Pro by 2.72% and 2.23%, and Qwen3-Reranker-8B by 3.02% and 2.53%. On long-document retrieval, rerank-3 outperforms rerank-2.5 by 3.35% and Cohere Rerank v4.0 Pro by 13.84%. Both models retain the 32K token context length and the instruction-following capability of the Rerank 2.5 series, and rerank-3-lite now matches the retrieval quality of rerank-2.5 at 40% of the price. <sup Quality: 95 datasets with voyage-4-large top-100 retrieval. Qwen3-Reranker-8B: assumed $0.10 / 1M tokens.</sup Rerankers are the second stage of a retrieval pipeline. The first stage, usually embeddings and vector search, selects a list of candidates from a large corpus. The reranker then scores each candidate against the query and reorders the list so that the most relevant documents land at the top. Because the reranker only sees a short list, it can afford to be more accurate per document than an embedding model, and it can be added to an existing retrieval system with a single API call and no re-indexing. For an introduction to rerankers, see our earlier post https://blog.voyageai.com/2024/03/15/boosting-your-search-and-rag-with-voyages-rerankers/ . For a comparison against LLMs used as rerankers, see this post https://blog.voyageai.com/2025/10/22/the-case-against-llms-as-rerankers/ . Since its release in August 2025, rerank-2.5 has been one of our most popular models. Over the same period, the workloads we serve have changed. Coding agents now issue many of the queries that reach our rerankers, and the documents being reranked are getting longer. The Rerank 3 series brings its largest improvements in these two areas. Today, we are excited to announce rerank-3 and rerank-3-lite . Both models are trained with an updated backbone and an improved mixture of training data. Improvements on long documents and code The Rerank 3 series is designed as an upgrade to Rerank 2.5 that requires no changes to your code. The API, the 32K token context length, instruction-following, and pricing are the same. Relevance scores are calibrated to match the score distributions of the corresponding Rerank 2.5 models, so score thresholds tuned on Rerank 2.5 continue to work. Long documents. rerank-3 and rerank-3-lite score candidate documents of up to 32K tokens, and the largest improvement over the Rerank 2.5 series comes on long documents. On LongEmbed, which consists of NarrativeQA, SummScreenFD, and QMSum, rerank-3 outperforms rerank-2.5 by 3.35% and rerank-3-lite outperforms rerank-2.5-lite by 1.86%. Both models outperform Cohere Rerank v4.0 Pro on long documents by more than 13%. Code. As we noted in the voyage-code-4 release, coding agents now issue many of the code retrieval queries we serve. On our code retrieval datasets, rerank-3 outperforms rerank-2.5 by 2.17% when evaluated atop voyage-3-large and by 2.11% atop voyage-4-large . rerank-3-lite outperforms rerank-2.5-lite by 3.58% and 3.72% on the same two retrievers. Evaluation Details Datasets. We evaluate across 9 domains: technical documentation, code, law, finance, web reviews, multilingual, long documents, medical, and conversations, 95 datasets in total. The multilingual domain is composed of 51 datasets from 31 languages. Detailed information about each of the domains and languages can be found in the rerank-2 release blog https://blog.voyageai.com/2024/09/30/rerank-2/ . To evaluate instruction-following, we use the MAIR benchmark https://arxiv.org/abs/2410.10127 , which consists of 123 tasks with task-specific instructions. Examples of instruction-following can be found in the Rerank 2.5 release blog https://blog.voyageai.com/2025/08/11/rerank-2-5/ . Method and Metrics. We evaluate the retrieval quality of various rerankers on top of four first-stage search methods: 1 lexical search with BM25, 2 OpenAI v3 large text-embedding-3-large , 3 voyage-3-large , and 4 voyage-4-large . For each query, the first-stage method retrieves up to 100 candidate documents. The reranker then re-orders these documents, and we retrieve the top 10. We report the normalized discounted cumulative gain NDCG@10 , the standard metric for retrieval quality. Baselines: We compare our models against rerank-2.5 , rerank-2.5-lite , Cohere Rerank v4.0 Pro, Cohere Rerank v4.0 Fast, Qwen3-Reranker-8B, Jina Reranker v3.5, mxbai-rerank-large-v2, and NVIDIA Llama-Nemotron Rerank 1B v2. Results rerank-3 is the top-performing reranker on every first-stage retrieval method we evaluated, and rerank-3-lite is roughly on par with rerank-2.5 at 40% of the price. Both models improve on their predecessors at the same price per token. Results across domains. The first bar chart below shows the average accuracy of each reranker across the 9 domains. Specifically: - Averaged across the four first-stage retrieval methods, rerank-3 outperforms Cohere Rerank v4.0 Pro, Cohere Rerank v4.0 Fast, Qwen3-Reranker-8B, and rerank-2.5 by 2.72%, 5.74%, 3.02%, and 0.96%, respectively. - rerank-3-lite , while optimized for latency, outperforms Cohere Rerank v4.0 Pro, Cohere Rerank v4.0 Fast, Qwen3-Reranker-8B, and rerank-2.5-lite by 2.23%, 5.25%, 2.53%, and 1.10%, respectively. - rerank-3-lite outperforms Qwen3-Reranker-8B, the leading open-weights reranker, despite being over an order of magnitude smaller. - Both models provide a significant quality improvement on top of all first-stage retrieval results. rerank-3 improves NDCG@10 by 7.65% atop voyage-3-large , 6.38% atop voyage-4-large , 16.67% atop OpenAI v3 large, and 20.33% atop BM25. Long documents. On LongEmbed, rerank-3 outperforms rerank-2.5 , Qwen3-Reranker-8B, and Cohere Rerank v4.0 Pro by 3.35%, 4.95%, and 13.84%, respectively. rerank-3-lite outperforms rerank-2.5-lite , Qwen3-Reranker-8B, and Cohere Rerank v4.0 Pro by 1.86%, 4.92%, and 13.80%. The gains over rerank-2.5 come from NarrativeQA and QMSum, where rerank-3 improves by 3.77% and 6.20%; all Voyage rerankers score above 99% on SummScreenFD. Code. On our code datasets DS-1000, APPS, CodeChef C++, RepoBench Java, and WikiSQL , rerank-3 outperforms rerank-2.5 by 2.17% atop voyage-3-large and 2.11% atop voyage-4-large , and rerank-3-lite outperforms rerank-2.5-lite by 3.72% and 3.58%. On CodeChef, rerank-3 and rerank-3-lite improve by 2.45% and 2.63% averaged across retrieval methods. Multilingual. Averaged across the four first-stage retrieval methods, rerank-3 outperforms rerank-2.5 and Cohere Rerank v4.0 Pro on the 51 multilingual datasets by 0.52% and 1.19%, and rerank-3-lite outperforms rerank-2.5-lite by 0.80%. Instruction-following. Both models retain the instruction-following capability introduced in Rerank 2.5. On MAIR, rerank-3 performs on par with rerank-2.5 , and rerank-3-lite outperforms rerank-2.5-lite by 0.91%. rerank-3 and rerank-3-lite outperform Cohere Rerank v4.0 Pro on MAIR by 3.58% and 2.34%, respectively. Comparison with Jev. We also evaluate the very recent Jev model in domains requiring strong expertise. Across 26 datasets, rerank-3 leads Jev 1.13 by 1.77, 5.69, and 5.87 NDCG@10 points in code, law, and finance, respectively. We use the score mode for Jev and did not apply any specific prompt tuning. We did not perform a full eval due to rate limits. Detailed results. Numeric results for all evaluations are available in this spreadsheet https://docs.google.com/spreadsheets/d/1rGzFkGERw6WDAWRuLrtG6AWlejn0T9Bb vHPJA9 4cA/edit?gid=1684416114 gid=1684416114 . Try rerank-3 and rerank-3-lite today As our results show, combining Voyage embedding models with Voyage rerankers delivers the highest possible retrieval accuracy. Both rerank-3 and rerank-3-lite are available today with flexible, token-based pricing, at the same price as rerank-2.5 and rerank-2.5-lite : $0.05 and $0.02 per million tokens. For existing rerank-2.5 and rerank-2.5-lite users, we recommend upgrading to rerank-3 and rerank-3-lite , respectively. This upgrade provides better quality at the same cost and requires no code changes. We will continue to offer the rerank-2.5 series for existing users who do not wish to upgrade. For new users, head over to our docs https://docs.voyageai.com/ to get started and learn more; the first 200M tokens are free. rerank-3 and rerank-3-lite are also available to MongoDB Atlas customers through the Atlas Embedding and Reranking API and Native Reranking in Atlas Vector Search. Learn more in our docs https://www.mongodb.com/docs/voyageai/quickstart/?llm-provider=anthropic here. Follow us on Twitter https://x.com/VoyageAI and LinkedIn https://www.linkedin.com/company/voyageai/ to stay up-to-date with our latest releases. Contributors Tengyu Ma, Project Advisor Minghan Li, Project Lead In collaboration with the Voyage team at MongoDB.