Context
512 tokens
Input
$0.10/1M tokens
Key Specifications
🏆
Arena Rank
Not disclosed
📐
Context Window
512 tokens
📥
Input Price
per 1M tokens
$0.10/1M tokens
📤
Output Price
per 1M tokens
N/A (embeddings)
🧠
Parameters
Undisclosed
🔒
Open Source
No
Best For
SearchRAGsemantic similarityclustering
About Cohere Embed v3
Cohere Embed v3 is one of the leading text embedding models, supporting over 100 languages with state-of-the-art retrieval performance. It produces dense vector representations of text that power semantic search, RAG pipelines, and classification systems. The model offers specialized embedding types for search queries vs documents, optimizing retrieval accuracy for enterprise applications.
Built byCohere↗
Pricing per 1M tokens
Input Tokens
$0.10/1M tokens
Output Tokens
N/A (embeddings)
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Frequently Asked Questions
What is Cohere Embed v3?▾
Cohere Embed v3 is one of the leading text embedding models, supporting over 100 languages with state-of-the-art retrieval performance. It produces dense vector representations of text that power semantic search, RAG pipelines, and classification systems. The model offers specialized embedding types for search queries vs documents, optimizing retrieval accuracy for enterprise applications.
How much does Cohere Embed v3 cost?▾
Cohere Embed v3 costs $0.10/1M tokens per 1 million input tokens and N/A (embeddings) per 1 million output tokens. Pricing is based on token usage, making it cost-effective for both small and large-scale applications.
What is Cohere Embed v3's context window?▾
Cohere Embed v3 has a context window of 512 tokens tokens. This determines how much text the model can process in a single request — larger context windows allow the model to handle longer documents, maintain more conversation history, and reason over bigger codebases.
Is Cohere Embed v3 open source?▾
No, Cohere Embed v3 is a proprietary model available through Cohere's API. Proprietary models are typically accessible via API endpoints and offer managed infrastructure, support, and regular updates from the provider.
What is Cohere Embed v3 best for?▾
Cohere Embed v3 is best suited for: Search, RAG, semantic similarity, clustering. These use cases leverage the model's specific strengths in terms of capability, speed, and cost-effectiveness within Cohere's model lineup.