Jina Embeddings v5 Omni Small

embedjina-ai

Jina Embeddings v5 Omni Small is a multilingual multimodal embedding model with 1.66B parameters built on the jina-embeddings-v5-text-small text backbone, extended with a fine-tuned SigLIP2 So400m vision encoder (from Qwen3.5-2B) for image and video and a Whisper-large-v3 audio encoder (from Qwen2.5-Omni-7B), connected via trained cross-modal projectors. It supports 32K context length and produces 1024-dimensional embeddings with Matryoshka representation learning (down to 32 dimensions). It uses last-token pooling and supports retrieval, text-matching, clustering, and classification tasks across text, image, video, audio, and PDF inputs.

Context
33K
Input price
$0.05/1M tokens
Output price
—

Capabilities

No supported capabilities recorded.

Details

Model IDjina-embeddings-v5-omni-small
Provider Jina AI
Creator jina
Licensejina-ai
Statusactive
Typeembed
Input modalitiestext, image, video, audio
Output modalitiesembedding
Open weightYes
Parameters—
Architecture—
Context window33K tokens
Max input—
Max output—
Reasoning tokens—
Knowledge cutoff—
Release date
Sourceofficial

Endpoints

EmbeddingsPOST
Generate vector embeddings for texthttps://api.jina.ai/v1/embeddings

Pricing

Input
$0.05
Output
—
Cache write
—
Cache read
—
Batch in
—
Batch out
—

Family Comparison: jina-embeddings

ModelContextMax outPricing
Jina Embeddings v5 Omni Nano8K—$0.02—
Jina Embeddings v5 Text Nano8K—$0.02—
Jina Embeddings v2 Base Code8K—$0.05—
Jina Embeddings v2 Base DE8K—$0.05—
Jina Embeddings v2 Base EN8K—$0.05—
Jina Embeddings v2 Base ES8K—$0.05—
Jina Embeddings v2 Base ZH8K—$0.05—
Jina Embeddings v38K—$0.05—
Jina Embeddings v433K—$0.05—
Jina Embeddings v5 Omni Small33K—$0.05—
Jina Embeddings v5 Text Small33K—$0.05—

API

GET/v1/models/jina/jina-embeddings-v5-omni-small

Jina Embeddings v5 Omni Small

jina-embeddings-v5-omni-small

Jina Embeddings v5 Omni Small is a multilingual multimodal embedding model with 1.66B parameters built on the jina-embeddings-v5-text-small text backbone, extended with a fine-tuned SigLIP2 So400m vision encoder (from Qwen3.5-2B) for image and video and a Whisper-large-v3 audio encoder (from Qwen2.5-Omni-7B), connected via trained cross-modal projectors. It supports 32K context length and produces 1024-dimensional embeddings with Matryoshka representation learning (down to 32 dimensions). It uses last-token pooling and supports retrieval, text-matching, clustering, and classification tasks across text, image, video, audio, and PDF inputs.

Changes · 2 entries
jina-embeddings-v5-omni-smallcreateca7a400Nov 2, 2026, 03:02 AM
jina-embeddings-v5-omni-smallcreate80c574aJun 3, 2026, 07:05 AM