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arXiv · 2506.18902

jina-embeddings-v4: Universal Embeddings for Multimodal Multilingual Retrieval

Michael Günther·Saba Sturua·Mohammad Kalim Akram·Isabelle Mohr·Andrei Ungureanu·Bo Wang·Sedigheh Eslami·Scott Martens·Maximilian Werk·Nan Wang·Han Xiao
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Abstract

We introduce jina-embeddings-v4, a 3.8 billion parameter multimodal embedding model that unifies text and image representations through a novel architecture supporting both single-vector and multi-vector embeddings in the late interaction style. The model incorporates task-specific Low-Rank Adaptation (LoRA) adapters to optimize performance across diverse retrieval scenarios, including query-document retrieval, semantic text similarity, and code search. Comprehensive evaluations demonstrate that jina-embeddings-v4 achieves state-of-the-art performance on both single-modal and cross-modal retrieval tasks, with particular strength in processing visually rich content such as tables, charts, diagrams, and mixed-media formats. To facilitate evaluation of this capability, we also introduce Jina-VDR, a novel benchmark specifically designed for visually rich image retrieval.

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Michael Günther, Saba Sturua, Mohammad Kalim Akram, Isabelle Mohr, Andrei Ungureanu, Bo Wang, Sedigheh Eslami, Scott Martens, Maximilian Werk, Nan Wang, Han Xiao. 2025-06-23. jina-embeddings-v4: Universal Embeddings for Multimodal Multilingual Retrieval. https://arxiv.org/abs/2506.18902

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