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The MTEB leaderboard says one model wins, industry blogs repeat the same recommendation, and none of that tells me whether it performs well on my own private documents.
What you'll have at the end
A recall score for two or three candidate models, measured on a small labeled set from your own data.
You need
A folder or export of your own documents, plus a way to call at least two different embedding models from code, whether through an API or a locally installed library.
Not covered
Running a formal statistical significance test on the recall difference, or building the retrieval pipeline the winning model will eventually serve.
Leans on
Verify your first embedding works
go there first if you're not yet sure your embedding calls are producing usable vectors at all
Why did swapping your embedding model wreck search quality?
go there once you've picked a winner here and need to replace the model in a live index without mixing vector spaces
Checked 15 Aug 2026
Part of the Embeddings cookbook