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Cohere Command R+

  • Context Window: 128,000
  • TPM: 300,000
  • RPM: 400
  • Embedding Size: NA
Attributes

Natural language processing, Text generation, Text summarization

Cohere Command R+

Cohere Embed English translates text into numerical vectors that models can understand. It plays a critical role in enabling advanced generative AI applications to comprehend the nuances of user inputs, search results, and documents. This model is essential for creating high-performing AI systems that require deep and precise text understanding in English.

By converting text into 1024-dimensional embeddings, Cohere Embed English enhances the ability of AI models to perform various tasks more effectively. These embeddings serve as a foundational element for many sophisticated AI processes, ensuring that models can accurately interpret and respond to complex queries and data.

Supported Use Cases:

  • Semantic search: Improve search accuracy and relevance
  • Retrieval-augmented generation (RAG): Enhance the generation of text based on retrieved information
  • Classification: Categorize data accurately for various applications
  • Clustering: Group similar items to uncover patterns and insights

Cohere Embed English is designed to provide robust performance and flexibility for diverse AI applications. Its ability to handle English text makes it a versatile tool for global AI solutions, ensuring that models can effectively interpret and utilize textual information from a wide range of sources.

CogniTech AI Credits

Below you will find all supported platforms and the related CogniTech AI Credits costs.

AWS Bedrock Credits

Region Context TPM RPM Input Credits Output Credits
Region Context TPM RPM Input Credits Output Credits
us-west-2 128,000 300,000 400 0.39 / 1000 tokens 1.95 / 1000 tokens
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