Sold by: MongoDB, Inc
Deployed on AWS
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General-purpose reranker with instruction following optimized for quality, with improved long-context and code retrieval. Context length: 32K for queries & documents (8K for queries).
Overview
Rerankers are neural networks that predict the relevancy scores between a query and documents and rank them based on the scores. They are used to refine search results in semantic search/retrieval systems and retrieval-augmented generation (RAG).
rerank-3 is the next generation of Voyage AI's reranker optimized for quality, and a drop-in upgrade to rerank-2.5 that requires no code changes. Trained with an updated backbone and an improved mixture of training data, rerank-3 improves on rerank-2.5 across nearly all domains, with the largest gains on long documents and code. Relevance scores are calibrated to match the score distribution of rerank-2.5, so score thresholds tuned on rerank-2.5 continue to work.
Averaged across 95 retrieval datasets in 9 domains and four first-stage retrieval methods, rerank-3 outperforms Cohere Rerank v4.0 Pro, Qwen3-Reranker-8B, and rerank-2.5 by 2.72%, 3.02%, and 0.96% NDCG@10, respectively. On long-document retrieval, rerank-3 outperforms rerank-2.5 by 3.35% and Cohere Rerank v4.0 Pro by 13.84%. On code retrieval, it outperforms rerank-2.5 by 2.17% atop voyage-3-large and 2.11% atop voyage-4-large.
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. rerank-3 also retains the instruction-following capability introduced in the rerank-2.5 series, allowing users to guide relevance scoring through natural language instructions, and outperforms Cohere Rerank v4.0 Pro by 3.58% on the MAIR instruction-following benchmark.
Learn more about rerank-3 here: https://blog.voyageai.com/2026/09/30/rerank-3
Highlights
- Drop-in upgrade to rerank-2.5 with no code changes: same API, 32K-token combined context length per query-document pair (up to 8K for the query), and instruction following, with relevance scores calibrated so existing rerank-2.5 score thresholds continue to work.
- Outperforms Cohere Rerank v4.0 Pro by 2.72%, Qwen3-Reranker-8B by 3.02%, and rerank-2.5 by 0.96% NDCG@10 on average across 95 retrieval datasets spanning 9 domains.
- Largest gains on long documents and code: outperforms rerank-2.5 by 3.35% and Cohere Rerank v4.0 Pro by 13.84% on long-document retrieval, and rerank-2.5 by 2.17% and 2.11% on code retrieval atop voyage-3-large and voyage-4-large, respectively.
Details
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Pricing
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Try this product free for 7 days according to the free trial terms set by the vendor.
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.g6.xlarge Inference (Real-Time) Recommended | Model inference on the ml.g6.xlarge instance type, real-time mode | $2.25 |
ml.g5.xlarge Inference (Batch) Recommended | Model inference on the ml.g5.xlarge instance type, batch mode | $35.92 |
ml.g5.xlarge Inference (Real-Time) | Model inference on the ml.g5.xlarge instance type, real-time mode | $2.82 |
ml.g5.2xlarge Inference (Real-Time) | Model inference on the ml.g5.2xlarge instance type, real-time mode | $3.03 |
ml.g5.4xlarge Inference (Real-Time) | Model inference on the ml.g5.4xlarge instance type, real-time mode | $4.06 |
ml.g5.8xlarge Inference (Real-Time) | Model inference on the ml.g5.8xlarge instance type, real-time mode | $6.12 |
ml.g6.2xlarge Inference (Real-Time) | Model inference on the ml.g6.2xlarge instance type, real-time mode | $2.44 |
ml.g6.4xlarge Inference (Real-Time) | Model inference on the ml.g6.4xlarge instance type, real-time mode | $3.31 |
ml.g6.8xlarge Inference (Real-Time) | Model inference on the ml.g6.8xlarge instance type, real-time mode | $5.04 |
ml.g7e.2xlarge Inference (Real-Time) | Model inference on the ml.g7e.2xlarge instance type, real-time mode | $4.49 |
Dimensions summary
You pay an hourly rate for running this reranker model on a GPU instance you deploy in your own account. Pricing splits into two parts: software usage and the AWS infrastructure cost. Rates vary by instance type and by deployment mode. Seventeen options cover real-time inference across g5, g6, g7e, p4d, p4de, and p5 instance families, sized from xlarge up to 48xlarge. Real-time mode gives you a persistent API endpoint billed per host hour. One batch option, ml.g5.xlarge, runs bulk inference jobs. Larger instances carry higher hourly rates. You choose the size that fits your workload.
Top-of-mind questions for buyers
Your total hourly cost combines two parts: the software rate for model usage and the underlying AWS infrastructure rate for the instance. Both bill hourly. The software rate varies by deployment mode, instance type, and region. The infrastructure charge is the AWS compute cost for the instance you run.
Canceling your subscription does not terminate running real-time endpoints or batch jobs. Charges keep accruing while an endpoint stays active. You must delete endpoints and their configurations separately through the console to stop costs. Do not leave real-time endpoints running longer than needed.
Real-time mode runs a persistent, always-on API endpoint that bills per host hour for as long as it stays active, suiting request-by-request inference. Batch mode runs a finite job that processes a dataset in bulk, writes results to a file, and stops when the job completes.
docs.voyageai.com
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
MongoDB is excited to announce the initial release of rerank-3.
Additional details
Inputs
- Summary
Supply a query and a list of documents to score for relevance.
Note: Does NOT support batch transform.
- Limitations for input type
- Total request size, calculated as the query tokens multiplied by the number of documents plus the tokens of all documents, cannot exceed 600,000 tokens.
- Input MIME type
- application/json
https://github.com/voyage-ai/voyageai-aws/blob/main/sample_reranker_input.json
rerank-3 reranker model does NOT support batch transform.
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
query | A query string (string). | Max 8,000 tokens. | Yes |
documents | Documents to rerank (List[string]). | Maximum of 1,000 documents; max 32,000 total tokens for each document. | Yes |
top_k | Number of most relevant documents to return (int). | Default: null (all documents are returned). | No |
truncation | Whether to truncate inputs to fit context limits (boolean). | Default: true. | No |
return_documents | Whether to return the documents in the response (boolean). | Default: false. | No |
Resources
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Support
Vendor support
Please email us at aws-marketplace@mongodb.com for inquiries and customer support.
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