Embeddings Cost Calculator
Compare cost across OpenAI, Voyage AI, and Cohere embedding APIs.
Quick Answer
text-embedding-3-small at $0.02/M is the cost-leader for most RAG workloads. text-embedding-3-large at $0.13/M trades 6.5x cost for ~5-10% recall gain. Voyage 3-large ($0.18/M) tops MTEB but costs the most. Embedding 1M docs at 500 tokens each costs $10 on small, $65 on large.
| Model | Dims | Per 1M | Initial | Year 1 |
|---|---|---|---|---|
text-embedding-3-small OpenAI | 1536 | $0.02 | $1.00 | $1.60 |
text-embedding-3-large OpenAI | 3072 | $0.13 | $6.50 | $10.40 |
text-embedding-ada-002 OpenAI · Legacy | 1536 | $0.1 | $5.00 | $8.00 |
voyage-3-large Voyage AI | 1024 | $0.18 | $9.00 | $14.40 |
voyage-3 Voyage AI | 1024 | $0.06 | $3.00 | $4.80 |
voyage-3-lite Voyage AI | 512 | $0.02 | $1.00 | $1.60 |
embed-english-v3 Cohere | 1024 | $0.1 | $5.00 | $8.00 |
embed-multilingual-v3 Cohere | 1024 | $0.1 | $5.00 | $8.00 |
About This Tool
The Embeddings Cost Calculator estimates indexing cost across OpenAI, Voyage AI, and Cohere embedding APIs. Enter average tokens per document, total document count, and the percentage of your corpus that updates each month. The tool computes initial indexing cost plus year-one update cost for each model.
Embedding pricing (April 2026)
OpenAI text-embedding-3-small: $0.02 per million tokens, 1536 dimensions, the cost-performance leader for general RAG. Text-embedding-3-large: $0.13/M, 3072 dims, marginally better recall. Voyage 3-large: $0.18/M, 1024 dims, the MTEB benchmark king for retrieval. Voyage 3-lite: $0.02/M, 512 dims, competitive with OpenAI's small at half the dimensions. Cohere Embed v3 (English and multilingual): $0.10/M, 1024 dims.
The hidden cost: re-embedding
Most teams budget for the initial index and forget update churn. If your corpus has 100K documents at 500 tokens each, the initial cost on text-embedding-3-small is $1. But if 10% of documents update monthly, that's another $1.20 per year on top. At enterprise scale (10M+ documents), the math reverses — update cost dominates the budget.
How to choose
Start with text-embedding-3-small. It's cheap, fast, and good enough for most retrieval tasks. Upgrade to large or Voyage 3-large only if your evals show meaningful recall improvement on your specific data. For multilingual corpora, Cohere's multilingual model is a strong default. For code retrieval, Voyage's code-tuned variants outperform general-purpose models.
Beyond embedding cost
Embedding is just the first line. You also need a vector database (storage + query cost), reranker (often Voyage Rerank or Cohere Rerank at $1-2/M tokens), and the LLM that consumes retrieved context. Use the vector database cost calculator for storage estimates. Compare against fine-tuning at RAG vs fine-tune calculator. For total stack budgeting, see AI monthly budget calculator. Estimate text size with token counter.
Frequently Asked Questions
What's the cheapest embedding model?
When should I pick text-embedding-3-large?
How is Voyage different from OpenAI embeddings?
Do embedding costs include re-embedding for updates?
How do dimensions affect storage and query cost?
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