Embed v4.0 is a multimodal embedding model from Cohere that converts text, images, or mixed content into vector representations for classification and semantic search.
import { embed } from 'ai';
const result = await embed({ model: 'cohere/embed-v4.0', value: 'Sunny day at the beach',})Embed v4.0 is Cohere's fourth-generation embedding model, released April 15, 2025. It reaches a 65.2 MTEB score, ahead of OpenAI's text-embedding-3-large (64.6). Beyond text-only retrieval, it embeds interleaved text and images in the same vector space. You can index screenshots of PDFs, slides, figures, and tables directly alongside text documents without converting visual content to text first. That removes a common preprocessing step in document-heavy RAG pipelines.
The architecture supports four output dimensions: 256, 512, 1,024, and 1,536. It also supports Matryoshka-style nested representations, so you can truncate a full-resolution embedding to a smaller size with limited quality loss. You can tune the cost-versus-accuracy tradeoff at query time or build tiered retrieval systems. Use compressed vectors for lightweight candidate retrieval and full-resolution embeddings for re-ranking. The default dimension is 1,536.
Cohere describes the model as helping organizations "securely retrieve their multimodal data to build agentic AI applications." Its multimodal input coverage (text, images, and interleaved combinations) fits knowledge bases with mixed-format assets: technical documentation with embedded diagrams, investor presentations, and research reports with figures. You don't need separate embedding models per content type.
Embedding input is billed at $0.12 per million tokens at listed AI Gateway rates. See https://docs.cohere.com/docs/cohere-embed for request formats and limits.