Sold by: Jina AI
Deployed on AWS
Jina Embeddings v5 Text Nano is a 239M-parameter multilingual text embedding model with an 8K-token context window, Matryoshka dimensions from 32 to 768, and task-specific LoRA adapters for retrieval, classification, clustering, and text matching across 30+ languages.
Overview
Jina Embeddings v5 Text Nano is the compact member of the latest generation of Jina AI's open-weight text embedding family. Built on a EuroBERT-210M backbone with 239M parameters, it delivers multilingual embedding quality competitive with models more than twice its size, while keeping memory footprint and inference latency low for edge deployment, latency-sensitive search, and high-throughput pipelines.
The model supports 30+ languages with strong retrieval quality (MTEB English average 71.0, multilingual average 65.5). Matryoshka Representation Learning lets you truncate embeddings from 768 down to 32 dimensions without retraining, trading storage cost for marginal recall loss. Five task-specific LoRA adapters (retrieval.query, retrieval.passage, clustering, classification, and text-matching) let a single deployed model serve diverse downstream workloads.
Highlights
- Compact multilingual embeddings: a 239M-parameter model with an 8,192-token window and coverage for 30+ languages runs on cost-efficient GPUs with low latency, delivering quality competitive with models more than twice its size.
- Matryoshka dimensions from 32 to 768: truncate embeddings at inference time to match your storage and latency budget. One model, many deployment profiles; no separate training runs for small-vector use cases.
- Five task-specific LoRA adapters in one model: switch between retrieval.query, retrieval.passage, clustering, classification, and text-matching per request. Replace a stack of single-purpose embedders with one endpoint.
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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.g5.xlarge Inference (Batch) Recommended | Model inference on the ml.g5.xlarge instance type, batch mode | $2.50 |
ml.g5.xlarge Inference (Real-Time) Recommended | Model inference on the ml.g5.xlarge instance type, real-time mode | $2.50 |
ml.g4dn.xlarge Inference (Batch) | Model inference on the ml.g4dn.xlarge instance type, batch mode | $2.50 |
ml.g4dn.2xlarge Inference (Batch) | Model inference on the ml.g4dn.2xlarge instance type, batch mode | $2.50 |
ml.g4dn.4xlarge Inference (Batch) | Model inference on the ml.g4dn.4xlarge instance type, batch mode | $2.50 |
ml.g4dn.8xlarge Inference (Batch) | Model inference on the ml.g4dn.8xlarge instance type, batch mode | $2.50 |
ml.g4dn.12xlarge Inference (Batch) | Model inference on the ml.g4dn.12xlarge instance type, batch mode | $2.50 |
ml.g4dn.16xlarge Inference (Batch) | Model inference on the ml.g4dn.16xlarge instance type, batch mode | $2.50 |
ml.g6.xlarge Inference (Batch) | Model inference on the ml.g6.xlarge instance type, batch mode | $2.50 |
ml.g5.2xlarge Inference (Real-Time) | Model inference on the ml.g5.2xlarge instance type, real-time mode | $2.50 |
Dimensions summary
You pay by the hour for each running instance, billed on host hours. Pricing splits into two modes. Batch mode covers eight instance sizes for processing groups of inputs at once. Real-time mode covers sixteen instance sizes for on-demand, low-latency requests. Within each mode, larger instance sizes carry more compute power and cost more per hour. You choose the mode that fits your workload, then pick a size that matches your throughput needs. Cost scales with the size and number of instances you run and how long you keep them active.
Top-of-mind questions for buyers
You pay for the running instance by the hour, not by tokens or requests. Each host-hour reflects the GPU instance kept active to serve the model. Once you deploy on your own instance, you process any volume of text without separate per-token charges from this listing.
Charges accrue per running host-hour. A stopped or terminated instance stops the software charge for that hour. Underlying AWS infrastructure fees, such as storage, may still apply while resources remain provisioned. To halt software charges, shut the instance down.
Batch mode meters host-hours for processing grouped inputs at once, offered across eight instance sizes. Real-time mode meters host-hours for on-demand, low-latency requests across sixteen sizes. Both bill the same way, per running host-hour. You pick the mode matching your workload, then a size matching throughput.
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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
Initial release
Additional details
Inputs
- Summary
The model accepts JSON inputs. Texts must be passed in the following format.
- Input MIME type
- text/csv
https://github.com/jina-ai/jina-sagemaker/blob/main/examples/sample-inference-v5-text-nano-input.json
https://github.com/jina-ai/jina-sagemaker/blob/main/examples/sample-batch-v5-text-nano-input.csv
Support
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