Jina Embeddings v4: Universal Embeddings for Multimodal Multilingual Retrieval

Intended Usage & Model Info

jina-embeddings-v4 is a universal embedding model for multimodal and multilingual retrieval. The model is specially designed for complex document retrieval, including visually rich documents with charts, tables, and illustrations.

Built on Qwen/Qwen2.5-VL-3B-Instruct, jina-embeddings-v4 features:

Unified embeddings for text, images, and visual documents, supporting both dense (single-vector) and late-interaction (multi-vector) retrieval.
Multilingual support (30+ languages) and compatibility with a wide range of domains, including technical and visually complex documents.
Task-specific adapters for retrieval, text matching, and code-related tasks, which can be selected at inference time.
Flexible embedding size: dense embeddings are 2048 dimensions by default but can be truncated to as low as 128 with minimal performance loss.

Summary of features:

Feature	Jina Embeddings V4
Base Model	Qwen2.5-VL-3B-Instruct
Supported Tasks	`retrieval`, `text-matching`, `code`
Model DType	BFloat 16
Max Sequence Length	32768
Single-Vector Dimension	2048
Multi-Vector Dimension	128
Matryoshka dimensions	128, 256, 512, 1024, 2048
Pooling Strategy	Mean pooling
Attention Mechanism	FlashAttention2

Training & Evaluation

Please refer to our technical report of jina-embeddings-v4 for training details and benchmarks.

Usage

Jina-VDR

Alongside jina-embeddings-v4, we’re releasing Jina VDR, a multilingual, multi-domain benchmark for visual document retrieval. The task collection can be viewed here, and evaluation instructions can be found here.

License

This model was initially released under cc-by-nc-4.0 due to an error. The correct license is the Qwen Research License, as this model is derived from Qwen-2.5-VL-3B which is governed by that license.

Contact

Join our Discord community and chat with other community members about ideas.

Citation

If you find jina-embeddings-v4 useful in your research, please cite the following paper:

@misc{günther2025jinaembeddingsv4universalembeddingsmultimodal,
 title={jina-embeddings-v4: Universal Embeddings for Multimodal Multilingual Retrieval}, 
 author={Michael Günther and Saba Sturua and Mohammad Kalim Akram and Isabelle Mohr and Andrei Ungureanu and Sedigheh Eslami and Scott Martens and Bo Wang and Nan Wang and Han Xiao},
 year={2025},
 eprint={2506.18902},
 archivePrefix={arXiv},
 primaryClass={cs.AI},
 url={https://arxiv.org/abs/2506.18902}, 
}