Semantic search, retrieval-augmented generation (RAG), classification, clustering
Cohere Embed Multilingual 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 across multiple languages.
By converting text into 1024-dimensional embeddings, Cohere Embed Multilingual 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.
Cohere Embed Multilingual is designed to provide robust performance and flexibility for diverse AI applications. Its ability to handle multiple languages 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.
Below you will find all supported platforms and the related CogniTech AI Credits costs.
| Region | Context | TPM | RPM | Input Credits | Output Credits |
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| Region | Context | TPM | RPM | Input Credits | Output Credits |