Cohere Embed English v3 by Cohere — 1024D
English-optimized text and image embedding model. Fast and accurate for classification and retrieval tasks.
At a glance
Modalities
Dimensions
1K
Max tokens
512
Parameters
—
Price / 1M tokens
—
Type
Output types
Language support
Benchmarks
| MTEB AVG | 64.47 |
| MTEB RETRIEVAL | 55.00 |
Details
| Release date | 2024-01-01 |
| License | Proprietary |
| Model ID | cohere-embed-english-v3 |
| Provider | Cohere |
Tags
What You Need to Know About Cohere Embed English v3
Complete Specifications for Cohere Embed English v3 by Cohere
Get detailed specifications for Cohere Embed English v3, including output dimensionality of 1024 dimensions, maximum input token length, supported input modalities, and pricing per million tokens. This embedding model is designed for semantic search, text classification, clustering, and retrieval-augmented generation applications where understanding the relationship between texts is essential.
Pricing and Cost Efficiency for Cohere Embed English v3
Review the pricing structure for Cohere Embed English v3 and compare it against other embedding models from Cohere and competitors. Understanding embedding model costs is essential when scaling vector search and RAG applications to millions of documents. We provide transparent pricing to help you budget effectively.
Use Cases and Applications for Cohere Embed English v3
Explore the ideal use cases for Cohere Embed English v3. Whether you are building a semantic search engine, recommendation system, document classification pipeline, or multilingual retrieval system, understanding this model's capabilities and dimensionality will help you choose the right embedding strategy for your project.