best embedding model for pdf search and retrieval

For PDF search and retrieval, the “best” embedding model depends on your priorities, but these are the strongest practical choices:

Best overall

OpenAI text-embedding-3-large

• Very strong retrieval quality
• Works well for semantic search over documents
• Good general default for PDFs
• Especially solid if you want a managed API and high accuracy

Best cost/performance

OpenAI text-embedding-3-small

• Cheaper and faster
• Still very good for most PDF search tasks
• Often the best choice if you have lots of documents and want lower cost

Best open-source options

BAAI bge-large-en-v1.5 or bge-m3

• bge-large-en-v1.5: strong English retrieval
• bge-m3: better if you need multilingual or mixed retrieval use cases
• Good if you want to self-host

nomic-embed-text-v1.5

• Also a strong open-source option
• Good quality and easy to use

If PDFs are technical / enterprise / multilingual

bge-m3

• Handles multilingual content well
• Useful for mixed-language PDFs and heterogeneous corpora

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My practical recommendation

If you want the simplest answer:

• Use text-embedding-3-large for best quality
• Use text-embedding-3-small if cost matters
• Use bge-m3 if you need open-source + multilingual

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Important note for PDFs

Embedding model quality matters, but PDF retrieval usually depends just as much on:

• chunking strategy
• OCR quality for scanned PDFs
• metadata extraction
• hybrid search (BM25 + embeddings)
• reranking after retrieval

For best results, use:

1. good PDF text extraction
2. chunking by semantic sections
3. embedding model
4. reranker

If you want, I can also give you:

• the best model for your budget
• a top 5 comparison table
• or a recommended PDF search stack for production