I've been deep in a literature review for a project and decided to test-drive two AI tools that promise to help: Perplexity and Elicit. Both are positioned to help with research, but their approaches are fundamentally different. I've spent a week with each and have some thoughts on where they shine and where they fall short for academic work.
**Perplexity** feels like a supercharged, citation-aware search engine. Its strength is breadth and current awareness.
* You ask a question, and it scours the web (and its Pro sources) to give a summarized answer with inline citations.
* It's fantastic for getting up to speed on a new topic quickly or finding recent papers and commentary you might have missed.
* However, the control over the *type* of literature it finds is limited. It's pulling from a broad set of web-accessible sources.
**Elicit**, on the other hand, is built specifically for the academic workflow. Its core function is querying Semantic Scholar to find and summarize empirical papers.
* You pose a research question, and it returns a list of papers in a table, with columns for abstracts, key findings, methodology, and even lets you extract data like participant counts.
* It excels at the *screening* phase of a review—helping you quickly filter through many papers to find the most relevant ones based on your specific criteria.
My initial takeaway: they're complementary. Elicit is my starting point for a systematic, paper-focused search. I use it to build a core list of relevant studies. Then, I switch to Perplexity to check for very recent developments, find related blog posts or news articles from credible sources, or explore adjacent questions that come up during the process.
Has anyone else used both for a full project? I'm particularly curious about:
* How reliable you've found the citation accuracy in each tool.
* Whether you've managed to integrate either into a larger Zotero or Notion workflow.
* If one has a clear edge in cost-to-value for sustained academic work.
In-house researcher for a small biotech firm. I run literature reviews constantly and manage the budget for these tools myself, so I've had to be strict on ROI.
- **Use Case Fit**: Perplexity is a discovery engine, Elicit is a systematic review assistant. Perplexity is best for exploring a new field or finding recent developments outside of core journals. Elicit is built for the specific, repeatable workflow of screening papers, extracting data into a table, and summarizing findings from known academic databases.
- **Citation Depth & Control**: Elicit wins for depth. You can ask it to pull papers from the last three years, focus on RCTs, and extract participant counts or outcomes into a spreadsheet. Perplexity gives you a link to the source, but you can't filter by study type or extract structured data from the papers it finds.
- **Real Pricing**: Perplexity is ~$20/month for Pro. Elicit's basic plan is free for 1,000 credits a month, but a serious review burns through that in days. Paid plans start around $10/month for students and go to ~$80/month (annually) for teams needing bulk work. The hidden cost with Elicit is the learning curve to frame queries correctly.
- **Where It Breaks**: Perplexity can hallucinate or misrepresent findings from dense papers - it's summarizing summaries sometimes. Elicit can be rigid; if your question isn't phrased just right, you'll get irrelevant papers, and it's weaker for very new or pre-print material.
My pick is Elicit for any formal, structured literature review where you need to document your search method and extract data. For a quick landscape scan or to find commentary on a fresh preprint, I'd use Perplexity. To decide, tell us if you need a reproducible process for a thesis or just a weekly digest of new papers in your field.
That's a great breakdown. You nailed Perplexity's strength for initial exploration. I'm trying to learn a new cloud topic and that's exactly how I use it, like a super-smart starting point.
But your point about control over the *type* of literature is key. I tried using it to find specific technical benchmarks and it kept mixing blog opinions with actual whitepapers. It's amazing for breadth, but you have to do the filtering yourself later.
For a proper lit review, it sounds like Elicit's structured approach is way more efficient. Would you say Perplexity is almost a step before the actual review? Like for scoping the field?
That's a great point about Perplexity being a step before the actual review. I'm also using it exactly like that to scope out new DevOps tools before diving in.
But I'm curious, for you guys doing more formal reviews, does the mixing of blogs and whitepapers in Perplexity ever lead to bad starting points? Like, you think a concept is standard because a popular blog post says so, but then the real papers show something different later.
Thanks for sharing your experience, this is super helpful for me as a beginner.