Tencent has released Qwen3.5 Embedding ahead of Alibaba’s Qianwen (Qwen) team, with its 2B parameter variant outperforming Qwen3-VL 8B.
53 minutes ago
Beating AI Express (from Dongcha Insight): Tencent WeChat’s Computer Vision team has open-sourced WeMM-Embedding, releasing three multimodal embedding models in one go: 2B, 4B, and 9B parameters. Built on Qwen3.5, these models can convert text, images, videos, and visual documents into unified vectors for applications including search, recommendation, and content matching. All models are open-sourced under the Apache 2.0 license. Notably, Alibaba’s Qwen has not yet launched its own Qwen3.5-Embedding; its official existing offerings remain Qwen3-Embedding and Qwen3-VL-Embedding, while Tencent has taken the lead in developing a new generation of embeddings based on Qwen3.5. The performance is quite impressive: WeMM-Embedding 2B scores 77.9 on MMEB-v2, slightly outperforming Qwen3-VL-Embedding 8B’s 77.8 points, while the 9B version further reaches 80.6 points. Both models support cross-modal retrieval such as text-to-video search and image-to-text search, but WeMM, built on the Qwen3.5 base, achieves higher performance with a smaller model size. These models have already been deployed for internal use within WeChat. Tencent confirmed that they are currently used in WeChat Official Accounts, Video Channels, e-commerce recommendations, and WeChat Search, with the search scope extending to Moments.
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