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.
💡 What You Will Learn
Real per-million-token prices for embedding APIs in 2026, with cost-saving strategies.
📜 Table of Contents
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 |
| 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%
- Use small models for retrieval, large only for re-ranking
- Cache embeddings - never embed the same text twice
- Batch API calls (many texts per request)
- Go local (sentence-transformers) for high volume
- 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.
❓ 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.
Written by our editorial team; tools listed here are tested or verified against public sources. Links point to official sites or GitHub repos for reference only โ no paid placements.
