OpenAI Embeddings Pricing 2026
Complete pricing guide with plans, and cost analysis
OpenAI Embeddings pricing ranges from $0.02 to $0.13/per million tokens.
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All OpenAI Embeddings Plans & Pricing
| Plan | Monthly | Annual | Best For |
|---|---|---|---|
| text-embedding-3-small | Custom | — | — |
| What's included at text-embedding-3-small
| |||
| text-embedding-3-large | Custom | — | — |
| What's included at text-embedding-3-large
| |||
| text-embedding-ada-002 (Legacy) | Custom | — | — |
| What's included at text-embedding-ada-002 (Legacy)
| |||
View all features by plan (compare side-by-side)
text-embedding-3-small
- $0.02 per 1 million input tokens
- 1,536 dimensions (configurable)
- Best price-to-performance for most use cases
- Batch API available at $0.01/M tokens (50% off)
text-embedding-3-large
- $0.13 per 1 million input tokens
- 3,072 dimensions (configurable)
- Highest accuracy embedding model
- Batch API available at $0.065/M tokens (50% off)
text-embedding-ada-002 (Legacy)
- $0.10 per 1 million input tokens
- 1,536 dimensions (fixed)
- Legacy model, still supported
- Batch API available at $0.05/M tokens (50% off)
OpenAI Embeddings costs $0.02 to $0.13 per per million tokens as of June 2026, with 3 plans available. Pricing depends on your chosen tier, contract length, and negotiated discounts.
Use the interactive pricing calculator to estimate your exact cost based on team size and requirements.
- Free tier: No free tier available
OpenAI Embeddings offers 3 pricing tiers: text-embedding-3-small, text-embedding-3-large, text-embedding-ada-002 (Legacy).
Compared to other embedding apis software, OpenAI Embeddings is positioned at the budget-friendly price point.
How much does OpenAI Embeddings cost?
OpenAI Embeddings Pricing Overview
OpenAI Embeddings has 3 pricing plans ranging from $0.02 to $0.13/per million tokens. The text-embedding-3-small plan requires contacting sales for a custom quote. The text-embedding-3-large plan requires contacting sales for a custom quote. The text-embedding-ada-002 (Legacy) plan requires contacting sales for a custom quote.
This pricing was last verified in May 30, 2026.
OpenAI Embeddings is a pay-per-use API that converts text into dense vector representations, enabling semantic search, clustering, classification, and retrieval-augmented generation (RAG). The current generation offers two models: text-embedding-3-small ($0.02/M tokens) for cost-efficient workloads and text-embedding-3-large ($0.13/M tokens) for maximum accuracy. All models support dimensionality reduction and a Batch API for 50% off on asynchronous workloads.
How OpenAI Embeddings Pricing Compares
Compare OpenAI Embeddings pricing against top alternatives in Embedding APIs.
Compare OpenAI Embeddings vs Alternatives
Before committing to OpenAI Embeddings, compare pricing with these 3 alternatives in the same category.
How OpenAI Embeddings Pricing Compares
| Software | Starting Price | Top Price |
|---|---|---|
| OpenAI Embeddings | $0.02/per million tokens | $0.13/per million tokens |
| Jina Embeddings | Free | $500/per million tokens |
| Mixedbread | Free | $20/month |
| Nomic Embed | Custom | Custom |
| Voyage AI | Free | $0.18/per million tokens |
| Cohere Embed | $0.1/month | $0.12/month |
OpenAI Embeddings Pricing FAQ
01 How much does OpenAI Embeddings cost?
OpenAI Embeddings are priced per million input tokens. text-embedding-3-small costs $0.02/M tokens, text-embedding-3-large costs $0.13/M tokens, and the legacy text-embedding-ada-002 costs $0.10/M tokens. The Batch API offers a 50% discount on all models.
02 Does OpenAI charge for output tokens on the Embeddings API?
No. The OpenAI Embeddings API only charges for input tokens. There are no output token charges since the API returns vector representations rather than generated text.
03 What is the difference between the standard API and the Batch API for embeddings?
The standard Embeddings API processes requests synchronously and is best for real-time use cases. The Batch API accepts asynchronous jobs and returns results within 24 hours, offering a 50% cost reduction — making it ideal for large-scale offline embedding tasks like indexing document libraries.
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