Consensus vs Elicit: pricing, features and which to choose

Consensus is made by Consensus NLP, Inc. and presents itself as an AI search engine for research. Its documentation says it searches more than 400 million scholarly articles, including 350 million peer-reviewed papers, while an earlier company primer cited more than 200 million papers. The company says more than five million researchers, students, and clinicians use it across more than 10,000 universities. It is best understood as a retrieval and explanation layer over scholarly literature, rather than a general chatbot. Its defining feature is traceability: answer sentences link to papers, and users can inspect supporting quotations. The company does not publish a founding year or funding total in the material described here, so its identity rests mainly on product scope, audience, and scholarly-search positioning.

A typical workflow begins with a natural-language research question. Consensus searches its paper database, retrieves relevant results, and generates a response from those results rather than relying only on general model knowledge. Users can inspect citations, run Pro searches, and open Study Snapshot entries covering details such as methodology, sample size, and study duration. Pro analysis summarizes up to 20 papers, while Deep Review searches across up to 50 papers for broader literature work. The service also flags retracted papers and supports API and MCP connections. This workflow helps users form reading lists, compare findings, screen evidence, or gain orientation in an unfamiliar field. It is most useful before close reading, because important claims still require checking the full papers, methods, limitations, and surrounding evidence.

The main limitation is dependence on indexed scholarly metadata and AI summaries. Citation links make checking easier, but they cannot guarantee that a synthesis captures study quality, conflicting findings, publication bias, or methodological nuance. Consensus is less suited to citation-network exploration than ResearchRabbit, Connected Papers, or Litmaps, and less suited to full reference management than Elicit or Scite. Its 2026 privacy policy says it does not sell personal information or use queries, uploads, or personal data to train large language models, while still describing account, cookie, provider, and analytics processing. Choose Consensus for fast, citation-linked answers and evidence comparison; choose ResearchRabbit for visual discovery and iterative paper mapping. It best fits students, clinicians, researchers, and teams seeking orientation before deeper evaluation.

Elicit is made by Elicit Research, PBC, an AI company that grew from Ought and focuses on scientific research and evidence-backed decisions. Its product searches academic literature, organizes papers into tables, extracts information, and guides systematic reviews. Elicit announced a $22 million Series A in February 2025 at a reported $100 million valuation, led by Spark Capital and Footwork. The company said it had more than 400,000 monthly researchers at that time, while its homepage later claimed more than five million researchers. Those figures use different dates and definitions. Elicit is active and has expanded from paper search into reports, routines, libraries, and organizational research workflows. This makes it useful for recurring work where teams need a consistent structure for reviewing literature and recording study details.

A normal Elicit workflow begins with a natural-language research question. Search returns relevant papers, after which the user can inspect titles, abstracts, full text, and extracted fields in a table. The user can add columns describing population, intervention, outcome, method, or another question, then export results. Research Reports automate search, screening, extraction, and report writing around a selected question and template. Systematic Reviews provide setup, paper gathering, title and abstract screening, optional full-text screening, data extraction, and a final report. Users can upload PDFs, import from Zotero, save papers in a library, chat with papers, and use a browser extension for institutional full-text access. In practice, the table format helps reviewers compare studies without moving repeatedly between separate documents or tools.

Elicit is strongest for researchers who need structured literature work rather than a general chatbot answer. It can reduce repetitive screening and extraction, but generated fields still need checking against papers, especially for nuanced methods, contradictory findings, and incomplete full text. Compared with Consensus, Elicit offers deeper table extraction and systematic-review workflows. Compared with SciSpace, it emphasizes structured review automation and research reports. Compared with NotebookLM, it searches a large paper corpus rather than relying mainly on user-uploaded sources. Choose Elicit for repeatable literature reviews, academic teams, and research operations; choose a general assistant for broad web research or creative writing. Its main tradeoff is that users gain review structure while accepting plan limits and the need for expert judgment.