What should I use for semantic search: OpenAI, Cohere, or Voyage AI?

If your goal is semantic search, all three can work well—but the best choice depends on what you care about most:

Quick recommendation

• Best overall default: OpenAI
• Best if you want strong retrieval-focused embeddings: Voyage AI
• Best if you want a solid search/rerank stack with easy enterprise-ish search workflows: Cohere

How they compare

OpenAI

Good for:

• General-purpose embeddings
• Easy integration if you already use OpenAI
• Strong quality across many domains

Pros:

• Very easy to use
• Good documentation and ecosystem
• Often the simplest “just works” option

Cons:

• Not always the most retrieval-specialized choice
• If you want the absolute best semantic search performance, another provider may edge it out depending on dataset/task

Cohere

Good for:

• Search systems that benefit from both embeddings and reranking
• Enterprise/search applications
• Multistage retrieval pipelines

Pros:

• Strong rerank models, which can materially improve search quality
• Good for production search stacks
• Well-suited to “retrieve then rerank” architectures

Cons:

• If you only want embeddings and nothing else, it may not be the simplest choice
• Quality can depend heavily on whether you use reranking properly

Voyage AI

Good for:

• High-quality retrieval embeddings
• Semantic search, RAG, and document retrieval
• Users who care a lot about search relevance

Pros:

• Often regarded as very strong for retrieval-specific embeddings
• Frequently a top choice for semantic search benchmarks/use cases
• Good fit for RAG pipelines

Cons:

• Smaller ecosystem than OpenAI
• Fewer “general AI platform” extras compared with OpenAI

Practical guidance

Choose based on your stack:

• If you want simplest integration and broad utility: OpenAI
• If search quality is the main concern and you’re optimizing retrieval: Voyage AI
• If you want to improve ranking quality with reranking: Cohere
• If you have a bigger budget for best relevance: try Voyage AI embeddings + Cohere reranker
• If you want one vendor for many AI tasks: OpenAI

My default suggestion

For a new semantic search project:

1. Start with Voyage AI or OpenAI embeddings
2. Add a reranker if relevance matters a lot
3. Benchmark on your own data before deciding

Because semantic search quality is highly dataset-dependent, the “best” provider is usually the one that performs best on your queries and documents, not just public benchmarks.

If you want, I can also give you:

• a decision matrix
• a cost comparison
• or a recommended stack for RAG/search based on your use case.