Elicit vs Consensus vs SciSpace
All three search academic papers with AI and hand you a summary. They are not interchangeable — one is built for screening and extraction, one for a fast read on where the evidence sits, one for breadth. Here is what each actually does, and where each admits it falls short.
They mostly search the same papers
Start here, because it changes how you read every comparison table on the internet. Elicit's help centre says it searches over 138 million papers from Semantic Scholar, PubMed and OpenAlex. Consensus says its 220-million-paper database is built from Semantic Scholar and OpenAlex plus proprietary partnerships with publishers — including, by its own account, six of the twelve largest, and the full PubMed corpus.
In other words, the same open infrastructure sits underneath most of this market. Index size is a weak tiebreaker: a bigger number often means more preprints, more conference abstracts and more near-duplicate records, not more of the peer-reviewed work you were looking for. What actually differs is what each tool does after the search.
Elicit — for screening and extraction
Elicit is the one built for review methodology rather than browsing. Its Extract Data feature pulls the same fields — sample size, intervention, outcome, whatever you define — out of every paper into a single table, which is precisely the artefact a systematic review needs and the thing a chat interface is worst at. Its Systematic Review workflow is built around the PRISMA 2020 reporting guideline.
The design decision that matters most: every extraction is one click from the quote, table or figure it came from. That makes checking cheap, which is the only reason to trust automated extraction at all.
Elicit is also unusually direct about its limits. Its own help centre notes that summaries can still miss a paper's nuance or misunderstand what a number refers to, and that these mistakes look identical to correct answers until you inspect the source. Take that at face value — it is the most useful sentence any of these vendors publishes.
Consensus — for a fast read on the evidence
Consensus answers questions rather than returning a reading list. Ask a yes/no research question and the Consensus Meter shows how the retrieved studies split between yes, no and possibly. For getting oriented in an unfamiliar area in ninety seconds, nothing else is as fast.
Consensus documents the catch, and you should read it before you quote a percentage in a seminar. The meter runs over roughly the first 20 results, not the literature. Every claim counts equally — a meta-analysis and a single case report each contribute one vote. And it can miss qualifiers in your question: Consensus's own example is asking whether ibuprofen is safe for adults and having a finding about children classified as a "yes."
Used as a compass, it is excellent. Used as a citation — "70% of studies agree" — it is a claim you cannot defend.
SciSpace — for breadth and reading support
SciSpace is the widest of the three in scope, if not always the clearest about scale: its own pages quote index figures ranging from 200 million to over 300 million depending which page you land on. Its Deep Review runs at three depths — a standard scan of roughly 50 papers, a higher-quality pass over about 150, and a deep mode that goes to 700 or more.
Its real differentiator is reading support: a Copilot that explains an unfamiliar passage or acronym in a PDF, and translation across 75+ languages. If you are reading outside your discipline or outside your first language, that is worth more than any index count.
One caution. SciSpace publishes head-to-head benchmarks showing SciSpace winning. So do most vendors. A benchmark run by the company being benchmarked is marketing, not evidence — from any of the three.
How to pick, in one line each
- Doing a systematic or scoping review → Elicit, for extraction tables and PRISMA-shaped output.
- Sizing up a question you know nothing about → Consensus, then verify the studies it surfaced.
- Reading widely, or across languages → SciSpace.
- Chasing a citation network → none of them; follow "Cited by" in Google Scholar, which remains better at forward citation search.
All three run a free tier and paid plans, and all three change their pricing often enough that any figure printed in a blog post is probably already wrong — check the vendor's pricing page on the day you decide.
These tools surface papers; they don't all hand you a reference in your department's style. Drop the details into our free citation generator for a correct reference and in-text citation in APA, MLA, Chicago, Harvard or IEEE.
The gap none of them closes
Retrieval-based tools solved the crude problem — they will not invent a paper the way a general chatbot still happily will. The subtler problem is untouched. The paper is real, the citation resolves, and the sentence attached to it slightly overstates what the authors found. No reviewer catches that by clicking the DOI; the DOI works.
Which is why the verification step never moves. Whatever tool surfaced the source, before a claim goes in your draft, someone has to confirm the source says it. That someone is you.
Draft from sources, not from memory
CiteGood writes from 250M+ live papers and grades every sentence against the exact source it cites — so the checking is already done when you read it.
Try CiteGood free →Frequently asked questions
Which is best for a systematic review — Elicit, Consensus or SciSpace?
Elicit. Its Systematic Review workflow is built around the PRISMA 2020 reporting guideline and its Extract Data tables pull the same fields out of every paper into one grid, which is the shape a screening and extraction stage actually needs. Consensus and SciSpace are discovery tools first.
Do these tools search the same papers?
They overlap heavily. Elicit draws on Semantic Scholar, PubMed and OpenAlex; Consensus builds on Semantic Scholar and OpenAlex plus direct publisher partnerships. The same open corpora sit underneath most of the market, so a paper missing from one is often missing from all three.
Can Elicit, Consensus or SciSpace hallucinate citations?
They don't invent papers the way a general chatbot does, because they retrieve from a real index before summarising. The failure mode is different: the paper is real, but the summary can misread a finding, miss a caveat, or attach a number to the wrong variable. Elicit publishes a limitations page saying exactly this. Always open the source before you cite it.
Is the Consensus Meter a measure of scientific consensus?
Not quite. Consensus documents that the meter classifies roughly the first 20 results as yes, no or possibly, and that every claim counts the same regardless of whether it comes from a meta-analysis or an n = 1 case report. Read it as a quick sense of the spread, not a verdict.
Do any of them write the paper for you?
SciSpace and Elicit both help you draft from what you've collected, and Consensus is search-first. None of them removes the verification step: whatever text comes out, you are the one signing your name to the claims and the references.