Jina Embeddings v5 Omni Small by Jina AI — 1024D
1.74B-parameter multimodal omni embedding model accepting text, images, video, and audio with shared vector space aligned to text-only embeddings. Supports 4 tasks (retrieval, classification, clustering, text-matching) with task-specific adapters. 1024-dim embeddings with Matryoshka truncation down to 32 dims. Built on Qwen3 architecture with frozen-tower composition.
At a glance
Modalities
Dimensions
1K
Max tokens
33K
Parameters
1.74B
Price / 1M tokens
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Type
Matryoshka dimensions
Output types
Language support
Details
| Release date | 2026-05-01 |
| License | CC BY-NC 4.0 |
| Model ID | jina-embeddings-v5-omni-small |
| Provider | Jina AI |
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What You Need to Know About Jina Embeddings v5 Omni Small
Complete Specifications for Jina Embeddings v5 Omni Small by Jina AI
Get detailed specifications for Jina Embeddings v5 Omni Small, including output dimensionality of 1024 dimensions, maximum input token length, supported input modalities, and pricing per million tokens. This embedding model is designed for semantic search, text classification, clustering, and retrieval-augmented generation applications where understanding the relationship between texts is essential.
Pricing and Cost Efficiency for Jina Embeddings v5 Omni Small
Review the pricing structure for Jina Embeddings v5 Omni Small and compare it against other embedding models from Jina AI and competitors. Understanding embedding model costs is essential when scaling vector search and RAG applications to millions of documents. We provide transparent pricing to help you budget effectively.
Use Cases and Applications for Jina Embeddings v5 Omni Small
Explore the ideal use cases for Jina Embeddings v5 Omni Small. Whether you are building a semantic search engine, recommendation system, document classification pipeline, or multilingual retrieval system, understanding this model's capabilities and dimensionality will help you choose the right embedding strategy for your project.