embeddings

IBM released two new multilingual embedding models under Apache 2.0: a 97M-parameter compact model scoring 60.3 on MTEB Multilingual Retrieval (highest in its size class) and a 311M full-size model scoring 65.2. Both support 200+ languages with enhanced retrieval for 52 languages, handle 32K-token context (64x increase over predecessors), and include code retrieval across 9 programming languages.

AWS released multimodal embedding capabilities for Amazon Nova on Bedrock, allowing manufacturing organizations to retrieve information from technical documents that combine text, engineering diagrams, and images. The model supports configurable dimensions from 256 to 3072 and processes text, images, and multi-page documents into a shared vector space.

IBM released Granite Embedding 311M Multilingual R2, a 311-million parameter dense embedding model with 32,768-token context length and support for 200+ languages. The model scores 64.0 on Multilingual MTEB Retrieval (18 tasks), an 11.8-point improvement over its predecessor, and ships with ONNX and OpenVINO models for production deployment.

Amazon Nova Multimodal Embeddings, announced October 28, 2025, now supports audio content for semantic search alongside text, images, and video. The model offers four embedding dimension options (3,072, 1,024, 384, 256) and uses Matryoshka Representation Learning to balance accuracy with storage efficiency.

Google has released Gemini Embedding 2, its first native multimodal embedding model that represents text, images, video, audio, and documents in a unified vector space. The model eliminates the need for separate embedding models across different modalities in AI pipelines.