When text, images, videos, charts, and even UI interfaces can be uniformly "understood" and precisely matched, the boundaries of multimodal information retrieval are being completely redefined. Today, Alibaba Tongyi Lab officially open-sources two models: Qwen3-VL-Embedding and Qwen3-VL-Reranker. Built upon the powerful Qwen3-VL multimodal foundation, these models are specifically designed for cross-modal understanding and efficient retrieval, marking a significant leap from the "keyword matching" era to a new epoch of "semantic alignment" in multimodal search.
These two models do not exist in isolation but form a collaborative intelligent retrieval engine. Qwen3-VL-Embedding uses an efficient dual-tower architecture to independently encode diverse content such as text, images, visual documents (e.g., code screenshots, data charts, app interfaces), and even videos into vector representations within a unified high-dimensional semantic space. This means that regardless of whether the user input is a textual description, a product image, or a short video, the system can map it into the same semantic coordinate system, enabling millisecond-level cross-modal similarity calculations and massive data recall.
Meanwhile, Qwen3-VL-Reranker acts as a "refiner." It employs a single-tower cross-attention architecture to perform deep re-ranking on the initial results from Embedding. When facing complex tasks such as "image-text query matching image-text documents" or "video segment retrieval of related articles," the Reranker will jointly encode the query and candidate documents, analyzing their deeper associations in semantics, details, and even contextual logic through the model's internal cross-attention mechanism, ultimately outputting a precise relevance score. This two-stage process of "fast embedding retrieval + precise reranking" significantly improves the accuracy and relevance of the final retrieval results.
Technical strength is ultimately proven by data. In authoritative multimodal benchmark tests such as MMEB-v2 and MMTEB, the Qwen3-VL series has shown outstanding performance. The 8B version of the Embedding model surpassed all known open-source models and mainstream closed-source commercial services on MMEB-v2; the Reranker model continues to lead in visual document retrieval tasks including JinaVDR and ViDoRe v3, with the 8B version taking first place in most subtasks. Particularly notable is that this series inherits the multilingual capabilities of Qwen3-VL, supporting over 30 languages, and offers flexible vector dimension options, instruction fine-tuning capabilities, and high-performance quantized versions, greatly reducing the integration barriers for developers.
This open-source release is not only a technical achievement but also marks the maturity of multimodal AI infrastructure. In the past, image-text retrieval, video understanding, and document analysis often required separate models and processes. Now, the Qwen3-VL twin models provide a unified, efficient, and open solution, allowing developers to handle almost all mixed modal content within a single framework. As real-world data increasingly emerges in multimodal forms, this toolset may accelerate the next generation evolution of search engines, content platforms, enterprise knowledge bases, and intelligent assistants—where machines truly "see" and "understand" everything we see, write, and take pictures of.
