검색 및 RAG를 위한 최고의 리랭크 모델
모델 순위가 실제 사용 데이터를 기반으로 업데이트되었습니다.
모델 순위는 최근 7일 동안 OpenRouter API를 통해 처리된 프롬프트 및 완성 토큰의 총수에 따라 결정됩니다. 상위 10개 모델을 표시합니다. 순위는 OpenRouter에서의 사용량을 측정하고 도입 현황을 반영하며, 모델 품질이나 벤치마크 성능을 나타내지 않습니다.
리랭크 모델은 후보 문서, 구절 또는 검색 결과를 관련성에 따라 재정렬하여 시맨틱 검색, 검색 증강 생성(RAG), 추천 파이프라인의 검색 단계를 개선합니다. 이 컬렉션은 지난 일주일간 OpenRouter 사용량을 기준으로 리랭크 모델의 순위를 매깁니다. 현재 최상위 모델은 rerank-2.5-lite, rerank-2.5 및 rerank-3-lite입니다. 가격, 지연 시간 및 품질을 비교하여 검색 또는 RAG 파이프라인에 가장 적합한 모델을 찾으십시오.
OpenRouter의 상위 리랭크 모델
VoyageAI by MongoDB: rerank-2.5-lite
64.7B개 토큰
rerank-2.5-lite is a reranker optimized for both latency and quality, delivering a 7.16% improvement in retrieval accuracy over Cohere Rerank v3.5 across 93 datasets. It also outperformed Cohere Rerank v3.5 by 10.36% on the Massive Instructed Retrieval Benchmark (MAIR). The model supports a combined context length of 32K tokens per query–document pair, including up to 8K tokens for the query, enabling more accurate retrieval over longer documents. Additionally, rerank-2.5-lite supports instruction following, allowrerank-2.5-lite here: mongodb.com/products/platform/ai-search-and-retrieval/models
rerank-2.5 is a cutting-edge reranker optimized for quality, delivering a 7.94% improvement in retrieval accuracy over Cohere Rerank v3.5 across 93 datasets. It also outperformed Cohere Rerank v3.5 by 12.70% on the Massive Instructed Retrieval Benchmark (MAIR). The model supports a combined context length of 32K tokens per query–document pair, including up to 8K tokens for the query, enabling more accurate retrieval over longer documents. Additionally, rerank-2.5 supports instruction following, allowingbout rerank-2.5 here: mongodb.com/products/platform/ai-search-and-retrieval/models
VoyageAI by MongoDB: rerank-3-lite
16.9B개 토큰
rerank-3-lite is a reranker optimized for both latency and quality and a drop-in upgrade to rerank-2.5-lite, improving on it by 0.94% NDCG@10 on average across domain evaluations and by 1.86% on long-document evaluations, with code retrieval gains of 2.77% atop voyage-3-large and 2.59% atop voyage-4-large. It matches the retrieval quality of rerank-2.5, and across 93 retrieval datasets it outperforms Cohere Rerank v4.0 Pro by 1.44% and Qwen3-Reranker-8B by 2.61%. The model supports a combined context length of 32K tokens per query-document pair, including up to 8K tokens for the query, enabling more accurate retrieval over longer documents. Additionally, rerank-3-lite supports instruction following, allowing users to guide relevance scoring t rerank-3-lite here: mongodb.com/products/platform/ai-search-and-retrieval/models
Qwen3 Reranker 8B
9.37B개 토큰
Qwen3 Reranker 8B is a text reranking model from Alibaba Cloud built on the Qwen3 architecture. It evaluates query-document pairs to produce relevance scores for use in retrieval and RAG pipelines. Supports 100+ languages and programming languages, with instruction-aware reranking that allows customizing scoring criteria per task. Offers strong performance on multilingual benchmarks including MTEB, CMTEB, and MMTEB.
VoyageAI by MongoDB: rerank-3
8.61B개 토큰
rerank-3 is a reranker optimized for quality and a drop-in upgrade to rerank-2.5, improving on it by 0.80% NDCG@10 on average across domain evaluations and by 3.35% on long-document evaluations, with code retrieval gains of 2.01% atop voyage-3-large and 1.96% atop voyage-4-large. Across 93 retrieval datasets it outperforms Cohere Rerank v4.0 Pro by 2.14% and Qwen3-Reranker-8B by 3.31%. The model supports a combined context length of 32K tokens per query-document pair, including up to 8K tokens for the query, enabling more accurate retrieval over longer documents. Additionally, rerank-3 supports instruction following, allowing users to guide relevance sco about rerank-3 here: mongodb.com/products/platform/ai-search-and-retrieval/models
Llama Nemotron Rerank VL 1B V2 is a 1.7B multimodal reranking model from NVIDIA. It evaluates the relevance of document images and text against user queries, designed for vision RAG pipelines handling charts, tables, infographics, and mixed-media documents. Functions as a cross-encoder that accepts text queries paired with image, text, or combined document inputs, delivering approximately 6-7% recall improvements over embedding-only baselines on visual document retrieval benchmarks.
Cohere's AI search foundation model for enhancing the relevance of information surfaced within search and RAG systems. Features a 32K context window, multilingual support across 100+ languages, no data pre-processing required, and state of the art performance with low latency.
Cohere's AI search foundation model for enhancing the relevance of information surfaced within search and RAG systems. Features a 32K context window, multilingual support across 100+ languages, no data pre-processing required, and high performance with lowest latency.
Rerank v3.5 is designed to reorder search results for improved relevance. It supports multi-aspect and semi-structured data reranking over 100+ languages. Ideal for refining results from semantic or keyword search pipelines.
자주 묻는 질문
Send POST /api/v1/rerank with the model slug, your query, a documents array, and optionally top_n. The response returns results with a relevance_score and index for each document. The RAG cookbook walks through the request.
Listings with published pricing bill per search unit or per input token, depending on the model, so compare prices in the same unit before you choose. You pay only for the requests you make, with no separate subscription per provider.
Retrieve candidate chunks with an embedding model, rerank the top candidates to sharpen precision, then pass the best documents to an LLM as context. All three steps run through the same OpenRouter API key.
Yes. One OpenRouter API key works for every rerank model on this page and for every other model on OpenRouter. Create a key on your API keys page.