Sold by: Jina AI
Deployed on AWS
Jina Embeddings v5 Text Small is a 677M-parameter multilingual text embedding model with a 32K-token context window, Matryoshka dimensions from 32 to 1024, and task-specific LoRA adapters for retrieval, classification, separation, and text matching across 30+ languages.
Overview
Jina Embeddings v5 Text Small is the latest generation of Jina AI's open-weight text embedding family, distilling the quality of 4B-parameter teacher models into a sub-1B footprint. With 677M parameters and a 32,768-token context window, it embeds full research papers, legal contracts, and long documents in a single pass, with no chunking required.
The model supports 30+ languages with state-of-the-art retrieval quality (MTEB English average 67.1, multilingual average 65.8). Matryoshka Representation Learning lets you truncate embeddings from 1024 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
- Long-context multilingual embeddings: 32,768-token window and coverage for 30+ languages let you embed entire documents without chunking and search across language boundaries with state-of-the-art quality.
- Matryoshka dimensions from 32 to 1024: 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, separation, classification, and text-matching per request. Replace a stack of single-purpose embedders with one endpoint.
Details
Sold by
Categories
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
Trust Center
Access real-time vendor security and compliance information through their Trust Center powered by Drata or Vanta. Review certifications and security standards before purchase.
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
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 the GPU instance that runs the text embedding model. Two inference modes are available: batch mode for processing large volumes at once, and real-time mode for live requests. Batch mode runs on g4dn instances plus two g5/g6 options. Real-time mode runs on a range of g5 and g6 instances. Within each mode, larger instance sizes carry more compute, so the hourly rate rises as you scale up. You choose the mode and instance size that match your workload; there is no upfront commitment.
Top-of-mind questions for buyers
You pay for one running GPU instance per hour. Each instance type maps to fixed AWS hardware, so larger sizes give more GPU memory and compute. The hourly software rate covers the model running on that instance; underlying AWS infrastructure fees apply separately.
Charges accrue per hour while the instance runs. Stopping the instance ends the hourly software charge. Batch mode meters processing time for the job, while real-time mode meters the endpoint's running hours whether or not requests arrive. Stopped endpoints do not accrue software charges.
Batch mode runs an instance to process a fixed set of inputs, then stops, so you pay for the job duration. Real-time mode keeps an endpoint running to answer live requests, so you pay for all hours the endpoint stays active. Batch suits bulk jobs; real-time suits continuous serving.
jina.ai
Helpful?
Vendor refund policy
For support, please visit https://jina.ai/contact-sales or https://www.elastic.co/support .
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) .
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
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-small-input.json
https://github.com/jina-ai/jina-sagemaker/blob/main/examples/sample-batch-v5-text-small-input.csv
Support
Vendor support
For support, please visit https://jina.ai/contact-sales or
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
Text embedding model (small) for input of size up to 8192 tokens
Jina Embeddings v5 Omni Small is a 1.56B-parameter multimodal embedding model that maps text, images, video, audio, and PDFs into a single shared vector space, with a 32K-token text context, Matryoshka dimensions from 32 to 1024, and task-specific LoRA adapters for retrieval, classification, clustering, and text matching across 30+ languages.
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.
Jina Embeddings v5 Omni Nano is a 1.04B-parameter multimodal embedding model that maps text, images, video, audio, and PDFs into a single shared vector space, with an 8K-token text context, Matryoshka dimensions from 32 to 768, and task-specific LoRA adapters for retrieval, classification, clustering, and text matching, with multilingual support across dozens of languages.
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.