Gemini Embedding 2: A Native Multimodal Embedding Model from Gemini
Abstract
We introduce Gemini Embedding 2, a native multimodal embedding model that allows embedding video, audio, image, and text modalities in a unified representation space. We leverage the multimodal capabilities of Gemini to produce embeddings for arbitrary combinations of interleaved inputs across all these modalities that generalize well across a wide variety of tasks. Applying large-scale contrastive learning in a multi-task multi-stage training setup, we achieve state-of-the-art performance on key embedding benchmarks including unimodal, cross-modal, and multimodal retrieval spanning a diverse set of tasks. We show that our embedding model demonstrates strong performance (with a score of 62.9 R@1 on MSCOCO, 68.8 NDCG@10 on Vatex, 69.9 on MTEB multilingual and 84.0 on MTEB Code) across a variety of tasks surpassing the performance of specialized models. These unified capabilities make Gemini Embedding 2 a promising candidate for downstream use cases such as RAG, recommendation and search. Furthermore, its robust zero-shot performance across distinct fields - from astronomy and bioscience to fine arts and the culinary arts - establishes it as a highly reliable, out-of-the-box representation even for specialized domains.
Keywords
Cite
@article{arxiv.2605.27295,
title = {Gemini Embedding 2: A Native Multimodal Embedding Model from Gemini},
author = {Madhuri Shanbhogue and Zhe Li and Shanfeng Zhang and Gustavo Hernández Ábrego and Shih-Cheng Huang and Aashi Jain and Daniel Salz and Sonam Goenka and Chaitra Hegde and Ji Ma and Feiyang Chen and Jiaxing Wu and Tanmaya Dabral and Babak Samari and Kevin Poulet and Daniel Cer and Kaifeng Chen and Paul Suganathan and Hui Hui and Jovan Andonov and Philippe Schlattner and Jay Han and Iftekhar Naim and Wing Lowe and Vladimir Pchelin and Albert Yang and Yi-Ting Chen and Zhongli Ding and Grace Zhang and Georg Heigold and Yichang Chen and Antoine Reveillon and Brendan Mccloskey and Wenlei Zhou and Dahun Kim and Rui Meng and Emma Wang and Jack Zheng and Halley Fede and Zhen Yang and Keegan Mosley and Brian Potetz and Sahil Dua and Henrique Schechter Vera and Shen Gao and Hesen Zhang and Andreas Hess and Hengxuan Ying and Alberto Montes and Karan Gill and Min Choi and Sebastian Russo and Anja Hauth and Jinhyuk Lee and Michael Boratko and Megan Barnes and Vikram Rao and Claudiu Musat and Cyril Allauzen and Ehsan Variani and Shankar Kumar and Tom Bagby and Junyi Jiao and Yang Gu and Tengxin Li and Ayush Agrawal and Roberto Santana and Dev Nath and Stephen Karukas and Shuoxuan Han and Lucia Loher and Alice Twu and Nidhi Vyas and Siddharth Bhai and Frank Palma Gomez and Wangyuan Zhang and Chaoren Liu and Jizheng Yang and Steve Qiu and Shijie Zhang and Sujay Kulkarni and Sascha Rothe and Sean Nakamoto and Raphael Hoffmann and Zach Gleicher and Yunhsuan Sung and Qin Yin and Tom Duerig and Mojtaba Seyedhosseini},
journal= {arXiv preprint arXiv:2605.27295},
year = {2026}
}