Exa Beats Google Scholar by 3x Finding Academic Papers Researchers Half-Remember
Exa launches semantic search over 350M scientific papers, outperforming Google Scholar with 86.4% recall at sub-400ms latency
- Exa launches semantic search over 350M+ scientific publications, queryable via a new
category="publication"API parameter. - Benchmarks show 86.4% recall vs Google Scholar's 28%, with sub-400ms latency -- more than 3x more accurate and faster.
- Two new retrieval benchmarks released publicly: known-item and tip-of-the-tongue retrieval for research papers.
- Ingestion pipeline uses OCR + document parsing to index PDFs, tables, appendices, and scanned documents, combined with author/citation metadata.
- Harvard Medical School professors and biopharma companies like Elicit are already using it for scientific AI agents.
- Available now through the Exa API; free tier includes 1,000 queries/month with no credit card required.
Exa launched a dedicated academic search product with benchmark numbers worth examining. The company built an index of roughly 350 million publications and added around 30 million authors to its people index, all queryable through its existing Search API. The goal: scientific search that retrieves papers the way researchers think about them, not the way databases catalog them.
Why academic search keeps failing researchers
Traditional tools like Google Scholar rely on exact keyword matching against titles, author names, and abstracts. That works when you already know what you're looking for. It breaks down when all you have is a vague memory of a result or a question like: "wasn't there a study from the 70s showing dietary cholesterol raised LDL in primates even when total cholesterol looked normal?"
Exa searches over the meaning of a query and the full contents of a paper, so you can retrieve a specific publication from factual clues or an incomplete recollection without translating your question into academic keywords. The shift is from keyword lookup to semantic retrieval.
The benchmark numbers
Exa created two benchmarks to measure this. The first tests known-item retrieval, where a searcher provides precise factual clues and the system must identify the exact paper. The second tests tip-of-the-tongue retrieval