Embedding Model Cost Comparison
Rank embedding models by price and dimensions.
With the default inputs, Embedding models compared — 10,000,000 tokens · ranked cheapest first. Enter your own numbers below to recompute instantly; the full step-by-step math is shown under the worked example.
Results update automatically as you type.
| text-embedding-3-small | OpenAI | $0.02/M | 1,536 | $0.2000 |
| Embed v3 (English) | Cohere | $0.1/M | 1,024 | $1.00 |
| text-embedding-3-large | OpenAI | $0.13/M | 3,072 | $1.30 |
Compare every embedding model by price per million tokens, vector dimensions and the total cost to embed your corpus — ranked cheapest first.
How this is calculated
For each embedding model we show price per million tokens, vector dimensions, and the total cost to embed your corpus (tokens ÷ 1M × price), ranked cheapest first. Dimensions don't change embedding price but drive downstream storage.
Is this a good result? What to do next
The cheapest embedding model is often good enough — retrieval quality doesn't scale linearly with price or dimensions. Balance price against dimensions (storage) and, above all, test recall on your own data.
Typical planning ranges
- Dimensions
- affect storage, not embedding price
- Smaller models
- often good enough
- Total cost
- tokens ÷ 1M × price
Ranges are typical planning figures to sanity-check your result, not authoritative benchmarks. Your numbers will vary with use case, volume, and vendor.
How to improve this number
- Prefer smaller/cheaper models when recall holds.
- Weigh dimensions for downstream storage cost.
- Test retrieval quality on your queries.
Common mistakes
- Assuming more dimensions/price means better retrieval.
- Ignoring storage implications of high dimensions.
When to use a different approach
For the full pipeline, use the RAG cost calculator. For storage by precision, use the embedding dimension storage calculator.
Worked example (defaults)
With the default inputs above, here is the result:
| text-embedding-3-small | OpenAI | $0.02/M | 1,536 | $0.2000 |
| Embed v3 (English) | Cohere | $0.1/M | 1,024 | $1.00 |
| text-embedding-3-large | OpenAI | $0.13/M | 3,072 | $1.30 |
Sources & references
Frequently asked questions
Do more dimensions cost more?+
Larger dimensions cost the same to generate but need more storage in your vector database.
Which embedding model should I pick?+
Balance retrieval quality (test on your data) against price and storage. Smaller models are often good enough.
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