{"slug": "rerank-3-and-rerank-3-lite-the-next-generation-of-voyage-rerankers", "title": "rerank-3 and rerank-3-lite: the next generation of Voyage rerankers", "summary": "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.", "body_md": "**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.\n\n<sup>Quality: 95 datasets with voyage-4-large top-100 retrieval. Qwen3-Reranker-8B: assumed $0.10 / 1M tokens.</sup>\n\nRerankers 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/).\n\nSince 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.\n\nToday, 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.\n\n## Improvements on long documents and code\n\nThe 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.\n\n**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%.\n\n**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.\n\n## Evaluation Details\n\n**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/).\n\n**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.\n\n**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.\n\n## Results\n\n`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.\n\n**Results across domains.** The first bar chart below shows the average accuracy of each reranker across the 9 domains. Specifically:\n\n- 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.\n- `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.\n- `rerank-3-lite` outperforms Qwen3-Reranker-8B, the leading open-weights reranker, despite being over an order of magnitude smaller.\n- 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.\n\n**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.\n\n**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.\n\n**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%.\n\n**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.\n\n**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.\n\n**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).\n\n## Try rerank-3 and rerank-3-lite today!\n\nAs our results show, combining Voyage embedding models with Voyage rerankers delivers the highest possible retrieval accuracy.\n\nBoth `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.\n\nFor new users, head over to our [docs](https://docs.voyageai.com/) to get started and learn more; the first 200M tokens are free. \n\n`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. \n\nFollow us on [Twitter](https://x.com/VoyageAI) and [LinkedIn](https://www.linkedin.com/company/voyageai/) to stay up-to-date with our latest releases.\n\n### Contributors\n\nTengyu Ma, Project Advisor\n\nMinghan Li, Project Lead\n\nIn collaboration with the Voyage team at MongoDB.", "url": "https://wpnews.pro/news/rerank-3-and-rerank-3-lite-the-next-generation-of-voyage-rerankers", "canonical_source": "https://blog.voyageai.com/2026/09/30/rerank-3/", "published_at": "2026-09-30 13:00:00+00:00", "updated_at": "2026-09-30 13:18:12.123431+00:00", "lang": "en", "topics": ["ai-search", "large-language-models", "ai-infrastructure", "developer-tools"], "entities": ["Voyage AI", "rerank-3", "rerank-3-lite", "rerank-2.5", "Cohere Rerank v4.0 Pro", "Qwen3-Reranker-8B", "voyage-4-large", "LongEmbed"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/rerank-3-and-rerank-3-lite-the-next-generation-of-voyage-rerankers", "markdown": "https://wpnews.pro/news/rerank-3-and-rerank-3-lite-the-next-generation-of-voyage-rerankers.md", "text": "https://wpnews.pro/news/rerank-3-and-rerank-3-lite-the-next-generation-of-voyage-rerankers.txt", "jsonld": "https://wpnews.pro/news/rerank-3-and-rerank-3-lite-the-next-generation-of-voyage-rerankers.jsonld"}}