Overview
Embed v3 is tracked in LLMWIKI as part of Cohere's Embedding lineup. Rather than repeating marketing copy, this page is built to answer the question someone actually has when they land here: what category this model belongs to, what it's realistically good at, and where it fits against the other options tracked in this index.
Embed v3 is one of 3 Cohere releases tracked in this index, alongside 2 sibling models. Use the related models section further down this page to compare Embed v3 directly against its closest siblings.
What Embed v3 Is Built For
Embed v3 doesn't generate text at all — it converts text into a numerical vector that captures its meaning, so pieces of text with similar meaning end up positioned close together in that vector space. That single capability is the backbone of modern semantic search, recommendation systems, and retrieval-augmented generation pipelines, where a query gets embedded and compared against a database of pre-embedded documents to find the most relevant matches before an LLM ever sees them.
Where It Fits in Practice
- Powering semantic search across documents, support tickets, or product catalogs
- Feeding a retrieval-augmented generation (RAG) pipeline with relevant context
- Clustering or de-duplicating large sets of text automatically
- Recommendation systems based on textual similarity rather than manual tagging
- Classifying support tickets or reviews by topic without hand-labeled training data
Pricing & Access
Embed v3 is typically available through api only, billed per token processed. Pricing for models in the Embedding category is usually usage-based — per token, per generation, or per minute of output depending on the modality — and providers adjust rates as new versions ship, so treat any number you see quoted elsewhere as a starting point to confirm on Cohere's official pricing page.
Considerations
Embedding quality depends heavily on how well the model matches your domain — a model tuned on general web text may under-perform on specialized jargon compared to a domain-tuned alternative. Evaluate retrieval quality on your own data before committing at scale.
Related Models
Frequently Asked
Who develops Embed v3?
Embed v3 is developed by Cohere.
What type of model is Embed v3?
It's tracked as a Embedding model, with text embeddings as its primary modality.
How is Embed v3 typically accessed?
Most people reach it through api only, billed per token processed, though availability can vary by region and plan.
How does Embed v3 compare to its siblings?
See the related models below for the closest comparisons, or use the comparison hub to put it side by side with any other tracked model.
How much does Embed v3 cost to use?
Pricing for embedding models is typically usage-based and changes as new versions ship — check Cohere's official pricing page for current rates rather than relying on a cached figure.
Is Embed v3 suitable for production use?
That depends on your specific requirements around latency, cost, and reliability at your expected volume — the considerations above cover what's generally worth testing before committing to it for a production workload.