Best Reranking Models
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Compare the Top Reranking Models as of November 2025
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What are Reranking Models?
Reranking models are AI models in information retrieval systems that refine the order of retrieved documents to better match user queries. Typically employed in two-stage retrieval pipelines, these models first generate a broad set of candidate documents and then reorder them based on relevance. They utilize sophisticated techniques, such as deep learning models like BERT, T5, and their multilingual variants, to capture complex semantic relationships between queries and documents. The primary advantage of reranking models lies in their ability to improve the precision of search results, ensuring that the most pertinent documents are presented to the user. However, this enhanced accuracy often comes at the cost of increased computational resources and potential latency. Despite these challenges, rerankers are integral to applications requiring high-quality information retrieval, such as question answering, semantic search, and recommendation systems. Compare and read user reviews of the best Reranking Models currently available using the table below. This list is updated regularly.
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1
Ragie
Ragie
Ragie streamlines data ingestion, chunking, and multimodal indexing of structured and unstructured data. Connect directly to your own data sources, ensuring your data pipeline is always up-to-date. Built-in advanced features like LLM re-ranking, summary index, entity extraction, flexible filtering, and hybrid semantic and keyword search help you deliver state-of-the-art generative AI. Connect directly to popular data sources like Google Drive, Notion, Confluence, and more. Automatic syncing keeps your data up-to-date, ensuring your application delivers accurate and reliable information. With Ragie connectors, getting your data into your AI application has never been simpler. With just a few clicks, you can access your data where it already lives. Automatic syncing keeps your data up-to-date ensuring your application delivers accurate and reliable information. The first step in a RAG pipeline is to ingest the relevant data. Use Ragie’s simple APIs to upload files directly.Starting Price: $500 per month -
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BGE
BGE
BGE (BAAI General Embedding) is a comprehensive retrieval toolkit designed for search and Retrieval-Augmented Generation (RAG) applications. It offers inference, evaluation, and fine-tuning capabilities for embedding models and rerankers, facilitating the development of advanced information retrieval systems. The toolkit includes components such as embedders and rerankers, which can be integrated into RAG pipelines to enhance search relevance and accuracy. BGE supports various retrieval methods, including dense retrieval, multi-vector retrieval, and sparse retrieval, providing flexibility to handle different data types and retrieval scenarios. The models are available through platforms like Hugging Face, and the toolkit provides tutorials and APIs to assist users in implementing and customizing their retrieval systems. By leveraging BGE, developers can build robust and efficient search solutions tailored to their specific needs.Starting Price: Free -
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Mixedbread
Mixedbread
Mixedbread is a fully-managed AI search engine that allows users to build production-ready AI search and Retrieval-Augmented Generation (RAG) applications. It offers a complete AI search stack, including vector stores, embedding and reranking models, and document parsing. Users can transform raw data into intelligent search experiences that power AI agents, chatbots, and knowledge systems without the complexity. It integrates with tools like Google Drive, SharePoint, Notion, and Slack. Its vector stores enable users to build production search engines in minutes, supporting over 100 languages. Mixedbread's embedding and reranking models have achieved over 50 million downloads and outperform OpenAI in semantic search and RAG tasks while remaining open-source and cost-effective. The document parser extracts text, tables, and layouts from PDFs, images, and complex documents, providing clean, AI-ready content without manual preprocessing. -
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NVIDIA NeMo Retriever
NVIDIA
NVIDIA NeMo Retriever is a collection of microservices for building multimodal extraction, reranking, and embedding pipelines with high accuracy and maximum data privacy. It delivers quick, context-aware responses for AI applications like advanced retrieval-augmented generation (RAG) and agentic AI workflows. As part of the NVIDIA NeMo platform and built with NVIDIA NIM, NeMo Retriever allows developers to flexibly leverage these microservices to connect AI applications to large enterprise datasets wherever they reside and fine-tune them to align with specific use cases. NeMo Retriever provides components for building data extraction and information retrieval pipelines. The pipeline extracts structured and unstructured data (e.g., text, charts, tables), converts it to text, and filters out duplicates. A NeMo Retriever embedding NIM converts the chunks into embeddings and stores them in a vector database, accelerated by NVIDIA cuVS, for enhanced performance and speed of indexing. -
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Cohere Rerank
Cohere
Cohere Rerank is a powerful semantic search tool that refines enterprise search and retrieval by precisely ranking results. It processes a query and a list of documents, ordering them from most to least semantically relevant, and assigns a relevance score between 0 and 1 to each document. This ensures that only the most pertinent documents are passed into your RAG pipeline and agentic workflows, reducing token use, minimizing latency, and boosting accuracy. The latest model, Rerank v3.5, supports English and multilingual documents, as well as semi-structured data like JSON, with a context length of 4096 tokens. Long documents are automatically chunked, and the highest relevance score among chunks is used for ranking. Rerank can be integrated into existing keyword or semantic search systems with minimal code changes, enhancing the relevance of search results. It is accessible via Cohere's API and is compatible with various platforms, including Amazon Bedrock and SageMaker. -
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MonoQwen-Vision
LightOn
MonoQwen2-VL-v0.1 is the first visual document reranker designed to enhance the quality of retrieved visual documents in Retrieval-Augmented Generation (RAG) pipelines. Traditional RAG approaches rely on converting documents into text using Optical Character Recognition (OCR), which can be time-consuming and may result in loss of information, especially for non-textual elements like graphs and tables. MonoQwen2-VL-v0.1 addresses these limitations by leveraging Visual Language Models (VLMs) that process images directly, eliminating the need for OCR and preserving the integrity of visual content. This reranker operates in a two-stage pipeline, initially, it uses separate encoding to generate a pool of candidate documents, followed by a cross-encoding model that reranks these candidates based on their relevance to the query. By training a Low-Rank Adaptation (LoRA) on top of the Qwen2-VL-2B-Instruct model, MonoQwen2-VL-v0.1 achieves high performance without significant memory overhead.
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