Models that convert text, images, and other data into dense vector representations for similarity search, clustering, and retrieval. The quality of your embeddings determines the quality of your RAG and recommendation systems.
| Tool | Best For | Pricing |
|---|---|---|
| OpenAI text-embedding-3 | Best general-purpose embeddings with flexible dimension tuning | $0.02-0.13 per 1M tokens |
| Cohere embed-v4 | Multilingual applications and cross-language search | Free trial, then $0.10 per 1M tokens |
| BGE-M3 | Teams wanting full control and no API dependency | Free (open-source, self-hosted compute costs) |
| Voyage-3 | Code search, technical documentation, and developer tools | Free tier, then $0.06 per 1M tokens |
OpenAI's latest embedding models with flexible dimensionality (256-3072). Available in large and small variants, balancing quality and cost for different use cases.
$0.02-0.13 per 1M tokens
Best general-purpose embeddings with flexible dimension tuning
State-of-the-art multilingual embedding model supporting 100+ languages with leading performance on cross-lingual retrieval benchmarks.
Free trial, then $0.10 per 1M tokens
Multilingual applications and cross-language search
Open-source embedding model from BAAI supporting multi-lingual, multi-granularity, and multi-function capabilities. Self-hostable with strong benchmark scores.
Free (open-source, self-hosted compute costs)
Teams wanting full control and no API dependency
Specialized embedding model with state-of-the-art performance on code retrieval benchmarks. Optimized for technical documentation and code search.
Free tier, then $0.06 per 1M tokens
Code search, technical documentation, and developer tools
A comprehensive comparison of embedding models for semantic search, RAG, and similarity tasks.
Tested 12 embedding models on real production workloads. Here's what actually performs for RAG, semantic search, and clustering—with cost breakdowns and migration guides.
Retrieval-Augmented Generation is powerful, but these common pitfalls can tank your accuracy. Here's what to watch for.