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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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 |
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Amazon SageMaker model
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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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