semantic-search

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.

Microsoft released the Harrier-OSS embedding model family, comprising three variants with 270M, 600M, and 27B parameters. The largest model achieves 74.3 on the Multilingual MTEB v2 benchmark. All models support 32,768 max tokens and multilingual inputs across 40+ languages.

Microsoft has released Harrier-OSS-v1, a family of multilingual text embedding models trained with contrastive learning and knowledge distillation. The 0.6B parameter variant achieves a 69.0 score on the Multilingual MTEB v2 benchmark with support for 32,768 token context windows and 45+ languages.

Perplexity has open-sourced two text embedding models designed to match or exceed the performance of Google's and Alibaba's embeddings while requiring significantly less memory. The move brings competitive embedding technology into the open-source ecosystem.