Cohere Embed v4

Pocket-sized multimodal AI for content understanding across multilingual texts, images, and video. Up to 5x faster than OpenAI CLIP with quantization-aware embeddings and support for 20+ languages.

Retrieval-Augmented Generation extended to handle multiple modalities including text, images, video, and audio. Uses multimodal embeddings like Gemini Embedding 2 or CLIP to enable cross-modal search and generation.

Multilingual embedding model supporting over 100 languages and ranking #1 on MTEB multilingual leaderboard. Offers flexible model sizes from 0.6B to 8B parameters with user-defined instructions.

Massive Text Embedding Benchmark (MTEB) - a comprehensive benchmark for evaluating text embedding models across 8 embedding tasks and 58 datasets in 112 languages. Provides a standardized leaderboard for comparing embedding quality across classification, clustering, retrieval, reranking, semantic textual similarity, and summarization tasks.

Multimodal embedding model with 137M parameters that outperforms OpenAI text-embedding-3-small on both short and long context tasks. Features Matryoshka Representation Learning for flexible embedding dimensions.

High-performance multilingual embedding model from Cohere supporting 100+ languages with 1024 dimensions, optimized for semantic search, RAG, and cross-lingual retrieval tasks.