Perplexity vs Semantic Scholar

Perplexity and Semantic Scholar are two of the most popular tools in the search category, but they take different approaches to solving the same problems. Perplexity, developed by Perplexity (founded 2022), is described as "ai-powered search engine that provides answers with sources.". Meanwhile, Semantic Scholar by Allen Institute for AI (founded 2015) "ai-powered academic search engine from allen ai with citation analysis and paper recommendations.". In terms of overall user satisfaction, Perplexity edges ahead with a rating of 4.4/5.0, compared to Semantic Scholar's 4.2/5.0 — a difference of 0.2 points. Perplexity's strongest advantages include shows sources, good for research, while Semantic Scholar is praised for free. On the pricing front, Semantic Scholar offers a free model at Free, making it the more budget-friendly option for teams watching their spend. Neither tool is perfect: Perplexity's main drawbacks include can hallucinate sources, limited free queries, while Semantic Scholar users typically cite no generation features as its biggest limitation. Both tools excel at research, so either choice will serve you well for these core use cases. However, Perplexity has an edge in fact-checking, which might be the tiebreaker if that's important to you. In terms of target audience, Perplexity is particularly popular among researchers and students, while Semantic Scholar tends to attract researchers and students. Our verdict: Perplexity holds a slight edge, but the gap is narrow enough that both tools are worth trying. Start with the free tier of each and see which fits your workflow better.