embed-english-v3.0 generates semantic embeddings from English text, producing 1,024-dimensional vectors suitable for retrieval-augmented generation, classification, clustering, and semantic search tasks. It achieves state-of-the-art performance on MTEB and BEIR benchmarks and was trained on approximately 1 billion English training pairs. The model supports batches of up to 96 inputs with 512 tokens maximum per input, and supports both text and image embedding. Pricing is $0.10 per million tokens. A notable limitation is that it requires explicit input_type specification to differentiate between search documents, queries, classification, and clustering tasks.