The Embedding Model Comparison lets you compare 20+ text embedding models by dimensions, cost, MTEB benchmark score, and use case. Filter by provider or open-source, sort by any column, and find the right embedding model for your RAG or semantic search project.
| Model | Provider | Dims | Max Tokens | Cost/1M | MTEB | Best For | Multi |
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How to Choose an Embedding Model
Choosing the right embedding model for your application significantly affects both the quality of results and your operating costs. For most RAG (Retrieval-Augmented Generation) pipelines, the embedding model is the biggest factor in retrieval accuracy.
Understanding the Key Metrics
Dimensions: The size of the embedding vector. More dimensions capture more semantic nuance but increase storage and compute costs. 384–768 dimensions cover most use cases; 1536+ is for maximum quality on complex tasks.
Max Tokens: The maximum input length the model can process in one call. For long documents, you'll need to chunk at this boundary. Most models support 512–8,192 tokens; some like BAAI/bge-m3 support up to 8,192.
MTEB Score: The Massive Text Embedding Benchmark measures performance across retrieval, clustering, reranking, and classification tasks. Higher is better; competitive models score 60–70.
Choosing by Use Case
General-purpose RAG: OpenAI text-embedding-3-small ($0.02/1M tokens) or all-MiniLM-L6-v2 (free, self-hosted). Both offer excellent retrieval quality with minimal setup.
Code search: Voyage AI voyage-code-3 or OpenAI text-embedding-3-large. Purpose-built for code retrieval with strong performance on mixed code/comment content.
Multilingual applications: BAAI/bge-m3 (free, 100 languages) or Cohere embed-multilingual-v3.0. Both handle cross-lingual semantic similarity well.
High-volume self-hosted: all-MiniLM-L6-v2 or GTE-large. Run locally with Sentence Transformers library, zero per-token cost at any scale.
FAQ
What are text embeddings and why do they matter?
Text embeddings convert words, sentences, or documents into numerical vectors that capture semantic meaning. Two pieces of similar text have vectors that are close together in the vector space. Embeddings power semantic search, RAG pipelines, recommendation systems, and clustering — they're the core of most modern AI applications.
What is MTEB and how do I read the scores?
MTEB (Massive Text Embedding Benchmark) is the standard benchmark for embedding models, covering 58 tasks across retrieval, clustering, classification, and more. Higher scores indicate better semantic understanding. A score of 65+ is excellent; most competitive models score 60–70. Top models like BGE-large and text-embedding-3-large score 62–65 on the English leaderboard.
OpenAI vs open-source embeddings — which should I choose?
OpenAI text-embedding-3-small offers excellent quality at $0.02/1M tokens with no infrastructure setup — ideal for getting started and for most RAG applications. Open-source models like all-MiniLM-L6-v2 (free, self-hosted) have lower MTEB scores but zero API cost. For high-volume applications (>100M tokens/month), self-hosted open-source models become significantly cheaper.
What embedding dimension should I use?
Higher dimensions generally capture more semantic nuance but use more vector DB storage and slower similarity search. 384 dimensions (all-MiniLM-L6-v2) is sufficient for most applications. 768 dimensions balances quality and efficiency. 1536+ dimensions (OpenAI ada-002, text-embedding-3-large) provide maximum quality. Choose based on your quality requirements and index size.
Which embedding model is best for code search?
For code search, use models specifically trained on code: Voyage AI's voyage-code-3 achieves top performance on code retrieval. OpenAI's text-embedding-3-large also handles code reasonably well due to diverse training data. BAAI/bge-m3 is a strong free option for multilingual code.
Is this embedding model comparison tool free?
Yes, completely free with no signup required. All data is static and runs in your browser.