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Elastic Introduces Jina v5 Omni Family: Two Models to Power Text, Image, Video, and Audio Search

What happened
Based on Elastic Press · May 11, 2026

Elastic launched two multimodal embedding models, jina-embeddings-v5-omni-small and -nano, enabling unified search across text, images, video, and audio without rebuilding existing systems.

Key points
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Developers can now perform search, classification, clustering, and deduplication across different media types, giving users powerful new ways to understand and organize multimodal data.
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Available in two sizes, small and nano, the new omni models share the exact same text embedding space as jina-embeddings-v5-text, so v5-text users can keep their existing index, swap in an omni model, and immediately index multimedia into the same vectors.
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Key highlights include: Both jina-embeddings-v5-omni-small and jina-embeddings-v5-omni-nano models are available on Elastic Inference Service, via the Jina API, and for local installation via download.
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Model weights are distributed freely for non-commercial license use.
Key numbers
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Elastic's solutions are used by over 50% of the Fortune 500, supporting search, observability, and security applications.

Elastic introduced the Jina v5 Omni family, comprising two new multimodal embedding models designed to represent text, images, video, and audio as vectors. The models, available in small and nano sizes, allow developers to perform search, classification, clustering, and deduplication across different media types while maintaining compatibility with existing text-based systems. Users can integrate multimedia into existing vector indexes without additional infrastructure changes, streamlining adoption for teams already using Elastic's search solutions.

The v5-omni models share the same text embedding space as jina-embeddings-v5-text, enabling seamless swapping between text and multimodal models. This compatibility allows organizations to expand their search capabilities to include images, audio, and video without rebuilding their indexing or retrieval pipelines. Ken Exner, Elastic's chief product officer, emphasized the goal of making multimodal search as scalable and straightforward as text search.

Powered by a single universal language model that aligns all modalities, the v5-omni models feature a modular design. Users can enable or disable text, image, and audio processing features based on their requirements, optimizing performance and resource usage for specific use cases. The models are accessible via Elastic Inference Service, the Jina API, and local installation, with non-commercial licenses available at no cost.

Elastic offers the new models under a non-commercial license for free download, while commercial use requires contacting Elastic sales. The company, known for its Search AI Platform, integrates AI with search technology to help organizations transform data into actionable insights. Elastic's solutions are used by over 50% of the Fortune 500, supporting search, observability, and security applications.

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