multimodal-embeddings

This paper compares natively multimodal embedding models (Gemini Embedding 2, Amazon Nova 2) against frontier LLMs (GPT-4.1, Claude Sonnet 4.6) for hard-negative text-to-image retrieval, finding comparable accuracy but much lower latency for embedding-based ranking.

This paper introduces jina-embeddings-v5-omni, a suite of multimodal embedding models that extend text embeddings to image, audio, and video using frozen-tower composition. The method trains only 0.35% of the total weights, maintaining text geometry while achieving competitive state-of-the-art performance with significantly lower computational cost.