How to Choose the Best Embedding Model for RAG in 2026: 10 Models Benchmarked

This post benchmarks 10 embedding models across cross-modal retrieval, cross-lingual retrieval, key information retrieval, and dimension compression scenarios. It highlights Gemini Embedding 2 as an all-rounder, Qwen3-VL-2B for cross-modal tasks, and Voyage Multimodal 3.5 or Jina Embeddings v4 for dimension compression. The post details the CCKM benchmark methodology, including hard negative generation for cross-modal and cross-lingual tasks, and the impact of modality gap and semantic understanding on retrieval accuracy. It also discusses the trade-offs of dimension compression for storage costs in vector databases like Milvus.