Embedding API Pricing in 2026

If you're building anything with embeddings — RAG, semantic search, recommendations — cost matters at scale. A small difference in price per million tokens adds up fast when you're processing millions of documents.

Here's the complete pricing breakdown for every major embedding provider.

Quick Comparison Table

Provider	Model	Dimensions	Price/1M Tokens	Free Tier
OpenAI	text-embedding-3-small	1536	$0.02	None
OpenAI	text-embedding-3-large	3072	$0.13	None
OpenAI	text-embedding-ada-002	1536	$0.10	None
Voyage	voyage-3	1024	$0.06	50M tokens/mo
Voyage	voyage-3-lite	512	$0.02	50M tokens/mo
Voyage	voyage-code-3	1024	$0.06	50M tokens/mo
Cohere	embed-english-v3.0	1024	$0.10	~1000 calls/mo
Cohere	embed-multilingual-v3.0	1024	$0.10	~1000 calls/mo
Mistral	mistral-embed	1024	$0.10	Limited

Cost at Scale

Let's compare what it actually costs to embed a realistic dataset:

10 million tokens per month (roughly 7,500 pages of text):

OpenAI small: $0.20/month
Voyage lite: $0.20/month (or free under free tier)
OpenAI large: $1.30/month
Voyage standard: $0.60/month
Cohere: $1.00/month
Mistral: $1.00/month

100 million tokens per month (roughly 75,000 pages):

OpenAI small: $2.00/month
Voyage lite: $2.00/month ($1.00 after free tier)
OpenAI large: $13.00/month
Voyage standard: $6.00/month ($3.00 after free tier)
Cohere: $10.00/month
Mistral: $10.00/month

1 billion tokens per month (enterprise scale):

OpenAI small: $20/month
Voyage lite: $20/month
OpenAI large: $130/month
Voyage standard: $60/month
Cohere: $100/month
Mistral: $100/month

The Cheapest Options

For cost-sensitive applications:

Cheapest overall: OpenAI text-embedding-3-small and Voyage voyage-3-lite are tied at $0.02/1M tokens. Voyage has the added benefit of a 50M token/month free tier. Best free tier: Voyage AI gives you 50 million tokens per month for free across all their models. That's enough for most startups and side projects. Best value for quality: Voyage voyage-3 at $0.06/1M tokens scores higher than OpenAI's large model on retrieval benchmarks while costing less than half.

Hidden Costs to Consider

1. Token counting differences. Providers use different tokenizers. The same text might be 100 tokens with OpenAI but 120 with Cohere. Always benchmark with your actual data. 2. Rate limits. Free tiers often come with strict rate limits. If you need to embed a large corpus quickly, you might hit limits that slow your pipeline. 3. Latency. Cheaper models aren't always slower, but some providers have higher latency. For real-time applications, test latency alongside cost. 4. Dimension storage. Higher-dimension embeddings (3072 for OpenAI large) use more vector database storage. At scale, this increases your database costs.

Using EmbedRoute to Optimize Cost

EmbedRoute adds a 20% routing fee on top of provider costs. But it can actually save you money by:

1. Testing without commitment. Try all providers through one API before committing to a long-term contract 2. Switching easily. If a provider raises prices, switch models with one line change 3. Using the right model per task. Use the cheap model for bulk indexing and the expensive model for user-facing queries

Recommendations by Use Case

Startup / Side project: Voyage voyage-3-lite (free tier covers most needs) Production RAG: OpenAI text-embedding-3-small (cheapest with great quality) Code search: Voyage voyage-code-3 (worth the premium for code-specific performance) Multilingual: Cohere embed-multilingual-v3.0 (best cross-language support) Maximum quality: Voyage voyage-3 (highest benchmark scores at a reasonable price)

Conclusion

Embedding costs are low enough that they rarely bottleneck a project. The real cost savings come from choosing the right model for your use case — not just the cheapest one. A model that retrieves better results means fewer wasted LLM tokens on bad context.

Test multiple models on your actual data. EmbedRoute makes this easy with a single API key.

Embedding Models Pricing Comparison (2026): OpenAI, Voyage, Cohere, Mistral