Building a RAG pipeline or semantic search system? The embedding model you choose has a massive impact on your costs — and the difference between the cheapest and most expensive option is 6.5x.
We ranked every embedding API by cost and built a free tool to estimate your exact spend. Here's what we found.
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Find the Cheapest Model →Embedding API Cost Ranking (2026)
| # | Model | Provider | Price/1M Tokens | Dimensions | Languages | Cost per 1M Docs |
|---|---|---|---|---|---|---|
| 1 | text-embedding-3-small | OpenAI | $0.02 | 1,536 | English+ | $10 |
| 2 | text-embedding-004 | Free* | 768 | Multilingual | Free* | |
| 3 | embed-v3 | Cohere | $0.10 | 1,024 | 100+ | $50 |
| 4 | text-embedding-ada-002 | OpenAI | $0.10 | 1,536 | English+ | $50 |
| 5 | text-embedding-3-large | OpenAI | $0.13 | 3,072 | English+ | $65 |
*Google free tier for low-volume. Costs $0.50 for 1M docs at scale. Assumes 500 tokens per document.
The Winner: OpenAI text-embedding-3-small
At $0.02 per 1M tokens, OpenAI's text-embedding-3-small is the cheapest embedding API by a wide margin. It's:
- 5x cheaper than Cohere embed-v3 ($0.10/1M)
- 6.5x cheaper than OpenAI's own large model ($0.13/1M)
- Good enough for 90% of English RAG applications
The quality trade-off is minimal. text-embedding-3-small delivers about 90% of large's retrieval accuracy at 85% less cost. For most use cases, the savings far outweigh the marginal quality difference.
When to Choose Something Else
Cohere embed-v3: For Multilingual
If you need to embed documents in 100+ languages, Cohere is the only serious option. At $0.10/1M, it's 5x more expensive than OpenAI small, but the multilingual support is unmatched. For global applications, the premium is justified.
OpenAI text-embedding-3-large: For High-Stakes Search
Legal, medical, and financial applications where retrieval accuracy directly impacts outcomes should use the large model. At $0.13/1M with 3,072 dimensions, it delivers the best retrieval quality available. The 6.5x cost premium over small is worth it when accuracy matters.
Google text-embedding-004: For Prototyping
Free tier makes it unbeatable for prototyping. Build your RAG pipeline, validate the approach, then switch to OpenAI small for production. The free tier handles thousands of documents with no credit card required.
Real Cost Comparison: 100K Documents
| Model | Indexing Cost | Monthly Query Cost (1K/day) | Total Year 1 |
|---|---|---|---|
| OpenAI small | $1.00 | $0.06 | $1.72 |
| Cohere v3 | $5.00 | $0.30 | $8.60 |
| OpenAI large | $6.50 | $0.39 | $11.18 |
The cost difference becomes dramatic at scale. For 10M documents, OpenAI small costs $10 to index while OpenAI large costs $65 — a $55 difference on a one-time operation.
How to Reduce Embedding Costs Further
- Reduce dimensions: Use 1024d instead of 3072d for 67% storage savings
- Optimize chunks: 256-512 tokens balances quality and cost
- Batch API calls: Embed 2,048 inputs per request for 10-20x speedup
- Cache embeddings: Don't re-embed unchanged documents
- Use incremental indexing: Only embed new/changed documents
The Bottom Line
OpenAI text-embedding-3-small at $0.02/1M tokens is the cheapest embedding API for most use cases. Use our Cheapest Embedding API Finder to estimate your exact costs, and the Embedding Cost Calculator for full RAG pipeline cost estimation.
Estimate your embedding costs — all 6 models ranked by your usage.
Find Cheapest Embedding API →Related Tools
- Cheapest Embedding API Finder — All models ranked by cost
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