Embedding API Price Comparison 2026: OpenAI vs Cohere vs Gemini vs Mistral

Real per-million-token prices for embedding APIs in 2026, with cost-saving strategies.

## Embedding API Price Comparison 2026 Embeddings are cheap but they add up at scale - a 10M document corpus means 10M+ API calls. Prices below are the public rates as of mid-2026. ### Per 1M tokens (input) | Provider | Model | Price | Dimensions | |----------|-------|-------|-----------| | OpenAI | text-embedding-3-small | 0.02 USD | 1536 | | OpenAI | text-embedding-3-large | 0.13 USD | 3072 | | Cohere | embed-v4 | 0.12 USD | 1024 | | Google | text-embedding-004 | 0.10 USD (after free tier) | 768 | | Mistral | mistral-embed | 0.07 USD | 1024 | | Jina | jina-embeddings-v3 | 0.02 USD (10M free/mo) | 1024 | | Voyage | voyage-3 | 0.06 USD | 1024 | ### Cost math Embedding a 1M-document corpus at 1,000 tokens each = 1B tokens: - text-embedding-3-small: 20 USD one-time - text-embedding-3-large: 130 USD - Local model: electricity only (~1-5 USD) ### How to save 80% 1. Use small models for retrieval, large only for re-ranking 2. Cache embeddings - never embed the same text twice 3. Batch API calls (many texts per request) 4. Go local (sentence-transformers) for high volume 5. Use Matryoshka-style truncation to cut dimensions without quality loss ### FAQ **Are more dimensions better?** Not always - 768-1024 is enough for most RAG; larger costs more and is slower. **Can I mix providers?** Yes - vectors from different models are not comparable, so pick one and stay with it.