Sold by: Jina AI
Deployed on AWS
New state-of-the-art multimodal reranking model for 29 languages.
Overview
Jina Reranker m0 is a neural multimodal reranking model, designed to enhance the relevance of search results. It complements text, image or multimodal embedding models and refines search results by prioritizing documents relevant to a query. This state-of-the-art reranker model enables a variety of applications that rely on precise search results across different languages, improved information retrieval, and high document throughput. Use-cases: Deep search, AI agents orchestration, vector search, retrieval augmented generation (RAG).
Highlights
- High multilingual performance across the board: This reranker model ranks at the top compared to its competitors, in terms of NDCG and recall, according to MBEIR, MKQA, ViDoRe and others.
- Extended context length: This reranker model is capable of handling queries up to 10,240 tokens.
- Novel support for image documents: One of the first reranker models to support text, images and complex documents with negligible modality gap.
Details
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Deployed on AWS
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Pricing
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.g4dn.xlarge Inference (Batch) Recommended | Model inference on the ml.g4dn.xlarge instance type, batch mode | $1.50 |
ml.g5.xlarge Inference (Real-Time) Recommended | Model inference on the ml.g5.xlarge instance type, real-time mode | $2.50 |
ml.g4dn.12xlarge Inference (Batch) | Model inference on the ml.g4dn.12xlarge instance type, batch mode | $11.25 |
ml.g4dn.12xlarge Inference (Real-Time) | Model inference on the ml.g4dn.12xlarge instance type, real-time mode | $11.25 |
ml.g4dn.16xlarge Inference (Batch) | Model inference on the ml.g4dn.16xlarge instance type, batch mode | $14.50 |
ml.g4dn.16xlarge Inference (Real-Time) | Model inference on the ml.g4dn.16xlarge instance type, real-time mode | $14.50 |
ml.g4dn.2xlarge Inference (Batch) | Model inference on the ml.g4dn.2xlarge instance type, batch mode | $2.20 |
ml.g4dn.2xlarge Inference (Real-Time) | Model inference on the ml.g4dn.2xlarge instance type, real-time mode | $2.20 |
ml.g4dn.4xlarge Inference (Batch) | Model inference on the ml.g4dn.4xlarge instance type, batch mode | $4.00 |
ml.g4dn.4xlarge Inference (Real-Time) | Model inference on the ml.g4dn.4xlarge instance type, real-time mode | $4.00 |
Dimensions summary
You pay by the hour for the compute instance that runs the reranker model. Pricing is organized by AWS instance type, spanning the g4dn, g5, p2, and p3 families. Larger instances within each family carry a higher hourly rate to reflect added compute capacity. You also choose an inference mode. Real-time mode is offered across all instance types for immediate ranking responses. Batch mode is offered for a subset of instances to process grouped workloads. Your total cost depends on the instance size, the mode you pick, and the number of host hours you run.
Top-of-mind questions for buyers
You pay for each hour the compute instance runs the reranker model, billed per host hour. The charge reflects the AWS instance you select, not the number of documents or tokens you rank. Metering counts running time, so the instance size and hours you keep it active drive the cost.
Both modes bill by host hours on the instance you choose. Real-time mode is offered on all instance types for immediate ranking responses. Batch mode is offered on a subset of instances to process grouped workloads. You pick the mode that fits your workload, and each carries its own hourly rate.
There is no hard limit on documents per request; inputs are batched internally by token count for GPU use. Your host-hour charge stays the same regardless of document volume, since billing meters running time, not documents. Latency grows with longer documents and queries, so larger jobs may hold the instance longer.
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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:
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Additional details
Inputs
- Summary
The model accepts JSON inputs. Texts must be passed in the following format.
{ "data": { "documents": [{"text": "the dog is in my house"}, {"image": "https://cdn.britannica.com/68/170868-050-8DDE8263/Golden-Gate-Bridge-San-Francisco.jpg "}, {"bytes": "xxx"}], "query": "where is the dog", "top_n": 2 } }
- Input MIME type
- text/csv
https://github.com/jina-ai/jina-sagemaker/blob/main/examples/sample-reranking-m0-inference-input.json
https://github.com/jina-ai/jina-sagemaker/blob/main/examples/sample-reranking-m0-batch-input.csv
Resources
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Support
Vendor support
We provide support for this model package through our enterprise support channel:
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