Qwen3-VL-Embedding and Qwen3-VL-Reranker: A Unified Framework for State-of-the-Art Multimodal Retrieval and Ranking
In this report, we introduce the Qwen3-VL-Embedding and Qwen3-VL-Reranker model series, the latest extensions of the Qwen family built on the Qwen3-VL foundation model. Together, they provide an end-to-end pipeline for high-precision multimodal search by mapping diverse modalities, including text, images, document images, and video, into a unified representation space. The Qwen3-VL-Embedding model employs a multi-stage training paradigm, progressing from large-scale contrastive pre-training to reranking model distillation, to generate semantically rich high-dimensional vectors. It supports Matryoshka Representation Learning, enabling flexible embedding dimensions, and handles inputs up to 32k tokens. Complementing this, Qwen3-VL-Reranker performs fine-grained relevance estimation for query-document pairs using a cross-encoder architecture with cross-attention mechanisms. Both model series inherit the multilingual capabilities of Qwen3-VL, supporting more than 30 languages, and are released in 2B and 8B parameter sizes to accommodate diverse deployment requirements. Empirical evaluations demonstrate that the Qwen3-VL-Embedding series achieves state-of-the-art results across diverse multimodal embedding evaluation benchmarks. Specifically, Qwen3-VL-Embedding-8B attains an overall score of 77.8 on MMEB-V2, ranking first among all models (as of January 8, 2025). This report presents the architecture, training methodology, and practical capabilities of the series, demonstrating their effectiveness on various multimodal retrieval tasks, including image-text retrieval, visual question answering, and video-text matching.
69647212138cc47cbd765311
Mingxin Li
69647212138cc47cbd765312
Yanzhao Zhang
69647212138cc47cbd765313
Dingkun Long
claimed_verified
2026-01-12T10:33:42.987Z
616adb8578833ce5997e441a
https://cdn-avatars.huggingface.co/v1/production/uploads/616adb8578833ce5997e441a/Oy5YkTaCMq-FEc0k3FTMd.jpeg
Dingkun Long
thenlper
user
thenlper
69647212138cc47cbd765314
Keqin Chen
69647212138cc47cbd765315
Sibo Song
69647212138cc47cbd765316
Shuai Bai
69647212138cc47cbd765317
Zhibo Yang
69647212138cc47cbd765318
Pengjun Xie
69647212138cc47cbd765319
An Yang
claimed_verified
2026-02-02T17:01:36.346Z
62088594a5943c8a8fc94560
https://cdn-avatars.huggingface.co/v1/production/uploads/1644733028938-62088594a5943c8a8fc94560.png
An Yang
yangapku
user
yangapku
69647212138cc47cbd76531a
Dayiheng Liu
69647212138cc47cbd76531b
Jingren Zhou
69647212138cc47cbd76531c
Junyang Lin
Qwen3-VL-Embedding and Qwen3-VL-Reranker: A Unified Framework for State-of-the-Art Multimodal Retrieval and Ranking
In this report, we introduce the Qwen3-VL-Embedding and Qwen3-VL-Reranker model series, the latest extensions of the Qwen family built on the Qwen3-VL foundation model. Together, they provide an end-to-end pipeline for high-precision multimodal search by mapping diverse modalities, including text, images, document images, and video, into a unified representation space. The Qwen3-VL-Embedding model employs a multi-stage training paradigm, progressing from large-scale contrastive pre-training to reranking model distillation, to generate semantically rich high-dimensional vectors. It supports Matryoshka Representation Learning, enabling flexible embedding dimensions, and handles inputs up to 32k tokens. Complementing this, Qwen3-VL-Reranker performs fine-grained relevance estimation for query-document pairs using a cross-encoder architecture with cross-attention mechanisms. Both model series inherit the multilingual capabilities of Qwen3-VL, supporting more than 30 languages, and are released in 2B and 8B parameter sizes to accommodate diverse deployment requirements. Empirical evaluations demonstrate that the Qwen3-VL-Embedding series achieves state-of-the-art results across diverse multimodal embedding evaluation benchmarks. Specifically, Qwen3-VL-Embedding-8B attains an overall score of 77.8 on MMEB-V2, ranking first among all models (as of January 8, 2025). This report presents the architecture, training methodology, and practical capabilities of the series, demonstrating their effectiveness on various multimodal retrieval tasks, including image-text retrieval, visual question answering, and video-text matching.
The Qwen3-VL-Embedding and Qwen3-VL-Reranker models form an end-to-end multimodal search pipeline, leveraging multi-stage training and cross-attention mechanisms to achieve high-precision retrieval across diverse modalities.
Qwen3-VL-Embedding Qwen3-VL-Reranker multimodal search multi-stage training contrastive pre-training model distillation Matryoshka Representation Learning cross-encoder architecture cross-attention mechanisms multimodal embedding visual question answering video-text matching