Tencent releases WeMM-Embedding multimodal embedding models (2B/4B/9B) — new MMEB-v2/v3 SOTA

Tencent published the WeMM-Embedding family — a universal multimodal embedding model in 2B/4B/9B parameter sizes, finetuned from Qwen3.5-2B/4B/9B-Base — on 25 Aug 2026. The technical report (arXiv:2608.24053) describes a two-stage training pipeline (large-scale multimodal alignment, then refinement with curated data, fine-grained relevance supervision, and cross-scale knowledge transfer). Inputs cover text, images, videos, visual documents, and interleaved multimodal content (no audio). Outputs are L2-normalized embeddings with Matryoshka support (2B → 2,048-dim; 4B → 2,560-dim; 9B → 4,096-dim). WeMM-Embedding-9B sets a new state of the art on MMEB-v2 with an average score of 80.6 (Image 81.9 / Video 74.3 / VisDoc 83.3), and tops MMEB-v3 V3-All at 59.5 — beating Qwen3-VL-Embedding-8B (53.5) and Tianmu-Emb-Uni-8B (53.3). The 2B variant already surpasses the prior 8B open-source baseline on MMEB-v2. Deployment is supported in vLLM 0.27.0 (pooling runner) and SGLang 0.5.9. The report claims deployment at scale across WeChat Channels, Official Accounts, Moments, and e-commerce services, with 14 online A/B tests and a 26-task in-house benchmark.