Qwen has released the Qwen3-VL-Embedding and Qwen3-VL-Reranker models, designed for advanced multimodal information retrieval and cross-modal understanding. These models support various inputs, including text and images, and enhance retrieval accuracy through a two-stage process of initial recall and precise re-ranking.

• Qwen3-VL-Embedding and Qwen3-VL-Reranker pair up for a two-stage retrieval pipeline (recall then rerank) covering text, images, screenshots, and video across 30+ languages.
• Embedding model uses a dual-tower design for independent encoding, while the reranker uses a single-tower architecture with cross-attention for deep query-document interaction.
• Achieves state-of-the-art results on image, visual document, and video retrieval benchmarks.
• Trails the text-only Qwen3-Embedding model on pure text retrieval, showing a tradeoff for its multimodal gains.