Sold by: Jina AI
Deployed on AWS
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.
Overview
Jina Embeddings v5 Omni Small is the multimodal member of the latest generation of Jina AI's open-weight embedding family. Built on a Qwen3-0.6B text backbone with a SigLIP2 vision tower and a Whisper-large-v3 audio tower, its 1.56B parameters map text, images, video, audio, and PDF documents into a single shared embedding space, so you can index any modality and retrieve any other from one vector index. This enables cross-modal search, multimodal RAG, visual document retrieval over PDFs and scans, video moment retrieval, and audio semantic search without separate per-modality pipelines.
Text embeddings are identical to jina-embeddings-v5-text-small, so multimodal content drops into an existing v5-text index with no reindexing. The model handles a 32,768-token text context, supports 30+ languages, and matches the retrieval quality of omni models more than four times its size. Matryoshka Representation Learning lets you truncate embeddings from 1024 down to 32 dimensions without retraining, trading storage cost for marginal recall loss. Four task-specific LoRA adapters (retrieval, text-matching, clustering, and classification) tune the same base model for different downstream workloads at request time.
Highlights
- One shared embedding space for every modality: index text, images, video, audio, and PDFs together and query across them, so a text query can retrieve a video frame, a scanned page, or an audio clip from a single vector index.
- Drop-in compatible with jina-embeddings-v5-text-small: text embeddings are identical, so you can add multimodal content to an existing v5-text index with no reindexing. At 1.56B parameters it matches the quality of omni models more than four times its size.
- Matryoshka dimensions from 32 to 1024 with four task-specific LoRA adapters: truncate embeddings to fit your storage and latency budget, and switch between retrieval, text-matching, clustering, and classification per request from a single deployed model.
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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 instance you run the model on, billed per host-hour. Pricing splits into two modes. Batch mode covers eight instance sizes across two GPU families, used for processing large datasets at once. Real-time mode covers sixteen instance sizes across two GPU families, used for live, on-demand inference. Within each mode, cost scales with instance size: larger instances carry more compute and a higher hourly rate. You choose the mode and instance size that match your workload, and separate AWS infrastructure charges apply.
Top-of-mind questions for buyers
A host-hour is one running hour of the SageMaker instance you deploy the model on. Charges accrue only while the instance runs. Stopped or terminated instances stop the software charge, though AWS storage or other infrastructure fees may still apply separately.
Both bill per host-hour, but they fit different workloads. Batch mode processes large datasets in scheduled jobs across eight instance sizes. Real-time mode serves live, on-demand inference across sixteen instance sizes. You pick the mode matching your workload; each runs on its own hourly rate.
Larger instances carry more GPU compute, so they can handle more inputs per hour. You still pay per host-hour regardless of how many inputs you process. The model counts tokens from text and converts images, audio, video, and PDFs to tokens, but no per-token charge applies here.
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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-omni-small-input.json
https://github.com/jina-ai/jina-sagemaker/blob/main/examples/sample-batch-v5-omni-small-input.csv
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Text embedding model (small) for input of size up to 8192 tokens
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.
Text embedding model (base) for English and German input of size up to 8192 tokens
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