Sold by: MongoDB, IncÂ
Deployed on AWS
Free Trial
Reranker model for refining retrieval/search accuracy with instruction-following. 32K context length.
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-2.5 significantly improves upon the rerank-2 performance while also introducing instruction-following capabilities for the first time. On the Massive Instructed Retrieval Benchmark (MAIR), rerank-2.5 outperform Cohere Rerank v3.5 by 12.70%. The accuracy of rerank-2.5 is increased by an average of 8.13% on 24 domain-specific instruction-following datasets across 7 domains (web, tech, legal, finance, conversational, medical, and code). It supports a 32K-token context length, an 8x increase over Cohere Rerank v3.5. Latency is 1.5 s for 25K tokens, and throughput is 60M tokens per hour at $0.05 per 1M tokens on an ml.g6.xlarge. Learn more about rerank-2.5 here: https://blog.voyageai.com/2025/08/11/rerank-2-5/Â
Highlights
- First reranker with instruction-following capabilities, allowing users to dynamically steer the reranking process by providing explicit instructions alongside their query. These instructions can define the user notion of relevance or specify the desired characteristics of the documents to be retrieved.
- rerank-2.5 is 7.94% more accurate than Cohere Reranker v3.5, additional 8.13% performance gain if with instruction.
- rerank-2.5 improves retrieval quality over rerank-2 by 1.85% while increasing to a 32K context length. Latencies are 1.5 s (1 GPU), 415 ms (4 GPUs), and 245 ms (8 GPUs) for 25K tokens. We recommend using multiple GPUs to reduce latency. The supported 12xlarge and 24xlarge instances come with 4 GPUs each, while the 48xlarge instances are equipped with 8 GPUs. 60M tokens per hour at $0.05 per 1M tokens on an ml.g6.xlarge
Details
Sold by
Delivery method
Latest version
Deployed on AWS
New
Introducing multi-product solutions
You can now purchase comprehensive solutions tailored to use cases and industries.
Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Free trial
Try this product free for 5 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.g5.2xlarge Inference (Batch) Recommended | Model inference on the ml.g5.2xlarge instance type, batch mode | $0.00 |
ml.p4d.24xlarge Inference (Real-Time) Recommended | Model inference on the ml.p4d.24xlarge instance type, real-time mode | $35.92 |
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.g5.xlarge Inference (Real-Time) | Model inference on the ml.g5.xlarge instance type, real-time mode | $2.82 |
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.g6.xlarge Inference (Real-Time) | Model inference on the ml.g6.xlarge instance type, real-time mode | $2.25 |
Vendor refund policy
Refunds to be processed under the conditions specified in EULA. Please contact aws-marketplace@mongodb.com for further assistance.
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA)Â .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
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-2.5
Additional details
Inputs
- Summary
- query: str - The query as a string. Maximum of 4K tokens.
- documents: List[str] - The documents to be reranked as a list of strings. Maximum of 1K documents.
- top_k: int, optional (default=None) - The number of most relevant documents to return. If not specified, the reranking results of all documents will be returned.
- truncation: bool, optional (default=True) - True: Truncates. False: raises error if any given text exceeds the context length.
- Limitations for input type
- Voyage Rerank 2.5 has 32,000 token context length limit, which necessitates the truncation of documents that exceed this size.
- Input MIME type
- application/json
https://github.com/voyage-ai/voyageai-aws/blob/main/sample_reranker_input.json
https://github.com/voyage-ai/voyageai-aws/blob/main/sample_reranker_input.json
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
query | The query as a string. The query can contain a maximum of 8000 tokens. | Type: FreeText | Yes |
documents | The documents to be reranked as a list of strings. - The number of documents cannot exceed 1000. - The sum of the number of tokens in the query and the number of tokens in any single document cannot exceed 32000. - The total number of tokens, defined as "the number of query tokens Ă— the number of documents + sum of the number of tokens in all documents", # GPUs. The supported p4d and p4de instances that come with 8 NVIDIA A100 GPUs | Type: FreeText | Yes |
top_k | The number of most relevant documents to return. If not specified, the reranking results of all documents will be returned. | Default value: None Type: Integer | No |
truncation | Whether to truncate the input to satisfy the "context length limit" on the query and the documents. | Default value: True Type: Categorical Allowed values: True, False | No |
Support
Vendor support
Please email us at aws-marketplace@mongodb.com for inquiries and customer support. Join our Discord channel: https://discord.gg/zAU7GQEmvTÂ
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
Similar products
General-purpose reranker optimized for quality with multilingual support. Context length: 16K for queries & documents (4K for queries).
Rerank improves search systems by sorting documents based on their semantic similarity to a query.
Customer reviews
No customer reviews yet
Be the first to review this product . We've partnered with PeerSpot to gather customer feedback. You can share your experience by writing or recording a review, or scheduling a call with a PeerSpot analyst.