Cohere Embed v4vsAya Expanse

Cohere Embed v4, developed by Cohere, is the first multimodal embedding model in Cohere's lineup, processing both text and images into unified 128K-context vector representations. The model generates embeddings for semantic search, RAG pipelines, document retrieval, and visual search applications. Supporting 100+ languages, Embed v4 produces compact, efficient vectors optimized for modern vector databases. Its multimodal capability enables searching across mixed document types containing both text and visual elements. Priced at $0.12 per million tokens, it offers affordable embedding generation for production applications. The model represents a significant upgrade over text-only Embed v3, enabling unified search across document types. It is particularly valuable for enterprises with heterogeneous content including PDFs, presentations, and image-heavy documents that require combined text and visual understanding.

Aya Expanse, developed by Cohere through the Cohere For AI research initiative, is a multilingual open-source model with 32 billion parameters and a 128K token context window supporting 23 languages. Building on Aya 23, it substantially extends context length and improves quality across diverse languages. The model demonstrates strong cross-lingual transfer, enabling tasks like translation, summarization, and question answering across language pairs with limited parallel training data. Its 128K context window makes it suitable for processing long documents in languages where few other models offer extended context. Free and open-source, Aya Expanse aims to democratize access to capable multilingual AI. The model is particularly valuable for researchers and organizations working in lower-resource languages that receive minimal attention from major commercial AI providers.