OpenAI vs Voyage vs Cohere: Choosing the Right Embedding Model in 2025

Introduction

Choosing the right embedding model can make or break your AI application. Whether you're building a RAG system, semantic search, or recommendation engine, the quality of your embeddings directly impacts your results.

In this guide, we compare the top embedding providers: OpenAI, Voyage AI, and Cohere. We'll cover performance, pricing, and when to use each.

Quick Comparison

Provider	Best Model	Dimensions	Price (per 1M tokens)	Best For
OpenAI	text-embedding-3-large	3072	$0.13	General purpose
OpenAI	text-embedding-3-small	1536	$0.02	Cost-effective
Voyage	voyage-3	1024	$0.06	Code & technical
Voyage	voyage-3-lite	512	$0.02	Fast & cheap
Cohere	embed-english-v3.0	1024	$0.10	Multilingual

OpenAI Embeddings

OpenAI's text-embedding-3 family is the most widely used. It's reliable, well-documented, and integrates easily with existing OpenAI workflows.

Pros:

• Excellent general-purpose performance
• Great documentation and SDK support
• Consistent and reliable
• Variable dimensions (can reduce for cost savings)

Cons:

• Not the best for specialized domains
• Slightly higher latency than competitors

Best for: General RAG applications, chatbots, document search.

Voyage AI Embeddings

Voyage AI was founded by Stanford researchers and has quickly become a favorite for technical content. Their models excel at understanding code, technical documentation, and scientific text.

Pros:

• Best-in-class for code and technical content
• Lower latency than OpenAI
• Strong performance on retrieval benchmarks
• voyage-3-lite is extremely cost-effective

Cons:

• Smaller ecosystem
• Less documentation

Best for: Code search, technical documentation, scientific papers.

Cohere Embeddings

Cohere's embed-v3 models are optimized for multilingual use cases. If your application needs to work across languages, Cohere is often the best choice.

Pros:

• Excellent multilingual support (100+ languages)
• Strong reranking capabilities
• Good general performance

Cons:

• Slightly higher cost
• Fewer model options

Best for: Multilingual applications, global products, cross-language search.

Performance Benchmarks

Based on MTEB (Massive Text Embedding Benchmark) and real-world testing:

Retrieval Quality (higher is better):

• OpenAI text-embedding-3-large: 64.6
• Voyage voyage-3: 67.2
• Cohere embed-english-v3.0: 64.5

Code Search (higher is better):

• OpenAI text-embedding-3-large: 62.1
• Voyage voyage-code-3: 71.8
• Cohere embed-english-v3.0: 58.3

Voyage clearly wins for code-related tasks, while all three perform similarly for general text.

Pricing Comparison

For 10 million tokens per month:

• OpenAI small: $0.20/month
• OpenAI large: $1.30/month
• Voyage lite: $0.20/month
• Voyage standard: $0.60/month
• Cohere: $1.00/month

The cost differences are minimal at low volumes but add up at scale.

Our Recommendation

Start with OpenAI text-embedding-3-small if you're unsure. It's cost-effective, reliable, and good enough for most use cases. Switch to Voyage if you're working with code or technical content. The performance difference is significant. Choose Cohere if multilingual support is critical to your application. Or use EmbedRoute to access all of them through a single API. Test different models on your actual data and switch without changing your code.

Conclusion

There's no single "best" embedding model. The right choice depends on your use case, budget, and performance requirements.

The embedding landscape is evolving quickly. New models are released regularly, and benchmarks change. The best strategy is to test multiple options on your specific data and be ready to switch when better options emerge.

That's exactly why we built EmbedRoute—one API to access all embedding providers, so you can experiment freely and optimize as the landscape evolves.