https://store-images.s-microsoft.com/image/apps.10812.b34a6897-0e93-4e91-a8cd-29119cacd90c.6d235a0b-d497-432a-9b86-899feef2f0e1.3897df56-f7a4-4e44-8133-e016b709cad4
Jina Embeddings v4
Jina AI
Jina Embeddings v4
Jina AI
Jina Embeddings v4
Jina AI
Universal embedding model for multimodal and multilingual retrieval that supports both single-vector and multi-vector embedding outputs.
Introducing Jina Embeddings v4, a cutting-edge 3.8 billion parameter multilingual and multimodal embedding model, now available on Azure. This model aligns text and image inputs in a shared embedding space and achieves state-of-the-art retrieval performance on multimodal and multilingual tasks across MTEB, MMTEB, CoIR, LongEmbed, STS, Jina-VDR, CLIP, and ViDoRe benchmarks. Deploy seamlessly within your Azure infrastructure to support scalable inference and simplified MLOps workflows.
Jina Embeddings v4 features a novel architecture that supports both single-vector and multi-vector embeddings through late interaction, offering greater flexibility than traditional dual-encoder approaches. It includes a frozen backbone and multiple task-specific LoRA adapters, enabling optimized performance for use cases including query-document retrieval, semantic matching, and code search. The model processes visually rich inputs such as tables, charts, diagrams, and images through a unified embedding pathway, allowing for consistent cross-modal retrieval. With support for inputs up to 32,768 tokens and training data spanning 29 languages, it is well-suited for long-form document and multilingual search.