AI peer review tools compared (2026): 40 reviewers, editors and chats

Forty AI peer reviewers, language editors, chats and human editing services in one table: price, what is free, languages, verified references, statistics by code, Word output and confidentiality. Read on 17 Sep 2026, with a source on every row.

Here are the forty tools and services you can use today to review a manuscript before sending it to a journal: AI content reviewers, language editors, the general-purpose chats half the department already uses, human editing from the big publishers and the tools sold to journals themselves. One row each, with its price, what it gives away and what it actually does.

I write this, and I am an interested party: Previum is mine and sits in the first row. That is why the table carries no adjectives, only cells. I read every figure on each tool’s own page on 17 September 2026 and a second reader checked it again the same day; where they disagreed, the second reader’s figure is the one here. Prices change, sometimes month to month, so every row links to its source so you can see it with your own eyes before deciding.

And where another tool suits you better than mine, I say so: there is a whole section for that.

Figures read on 17 September 2026

Who is comparing: Víctor Ciudad-Fernández

PhD in Psychology (University of Valencia), statistical consultant. PhD cum laude from the University of Valencia and MSc in Methodology (Complutense University of Madrid). I publish in indexed journals such as Nature Reviews Psychology, the Journal of Behavioral Addictions and the Journal of Psychopathology and Clinical Science, and I review manuscripts for Q1 psychology journals. I build Previum; I also wrote this comparison, under my own name.

The whole table

Forty rows in five blocks. ✓ means it does it; — that it does not, or does not declare it on its website; n/c that I could not check it. When it does it with a caveat, the caveat is what you read. Previum goes first, with what it does not do written the same way as what it does.

AI content reviewers

AI language editors and pre-submission checks

General-purpose chats

Human editing and review

Tools sold to journals

What Previum does not do today

The public benchmark

Only one tool in the table publishes an open benchmark another tool can be measured against: Reviewer3 publishes ReviewBench, with 1,000 eLife articles and the comments from the human reviewers, from Reviewer3 and from two general-purpose models, with an automatic judge and its code. We ran our engine on 200 of those articles with their code and their judge, and put the averages side by side.

On agreement with human reviewers there is no significant difference (93.4% versus 95.6% of the claims the humans address; 67.9% versus 70.4% with the same stance). They win on pointing at a specific claim (77.1% versus 73.1%); we win on anchoring in the text (98.1% versus 83.0%), on the objection being able to undermine the claim (97.2% versus 89.7%) and on saying why it matters (100% versus 97.4%). The JSON with intervals is on /en/evidence.

References tell a similar story: they publish 98.5% on 476 citations they do not itemize; we publish 88.2% on the 636 of the original Walters and Wilder benchmark, one by one (92.3% of the fabricated ones caught, 86.4% of the real ones recognized). For the rest of the table I have found no comparable benchmark; if there is one, write to me and I will add it.

Metric (their benchmark, 200 articles)Reviewer3Previum
Addresses the claims human reviewers address95.6%93.4%
With the same stance as the humans70.4%67.9%
Points at a specific claim of the paper77.1%73.1%
Carries an anchor in the text (table, figure, quote)83.0%98.1%
Could undermine the claim89.7%97.2%
Says why it matters97.4%100%

Figures measured on 6 September 2026 over 200 eLife articles from ReviewBench, with their code and their judge. The first two rows do not differ in a statistically significant way; the next four do.

The full method, metric by metric and with what cannot be concluded, is in our benchmark against Reviewer3.

What to check before you trust any of them

These work on all forty rows, mine included, and on human editing too. If a tool makes any of them hard to answer, that difficulty is itself the answer.

1. Who set the standard, and is that person named?

Every review tool encodes somebody’s opinion of what matters in a manuscript, and the useful question is whose. Look for a named person with a traceable record in your field: someone who reviews for these journals, publishes in them, or edits for them. “Built with leading researchers” is not a name. Check the about page, then that name in Google Scholar or ORCID.

2. Does it quote your text, or talk about it in the abstract?

The fastest test of whether a review is real. A criticism that quotes the sentence it objects to can be checked, argued with and acted on; “the methodology could be strengthened” can be written without reading anything. Take the first output you get and count the criticisms containing a verbatim sentence of yours. Zero means you are holding a description of papers in general.

3. Does it verify the references, or repeat them?

A language model can produce a perfectly formed citation that refers to nothing, and telling it not to invent changes little, since it cannot check itself. Ask whether references are checked against an index and which one, then verify by hand: take three citations it produced and search them in Crossref or PubMed. One miss and you stop using it for citations.

4. Does it tell you what to fix, or only that something is wrong?

Diagnosis without treatment is where most automated review stops. “Your sample size is not justified” is true and you already suspected it; what you need is which justification your design calls for, what to compute, and what sentence goes into the manuscript. Read one full criticism and ask whether you could act on it tomorrow morning without consulting anybody.

5. What happens to your manuscript afterwards?

You are uploading unpublished work, sometimes with clinical material behind it. What matters is whether the file is stored, for how long, whether it trains anything, and whether you can have it deleted. This applies to every category, mine included. Read the privacy policy rather than the marketing page and search it for “train”, “retain” and “delete”; if those words never appear, the silence is information.

6. Does it rewrite your paper, and what does that do to your authorship?

There is a real line between a tool that tells you a paragraph does not work and one that hands you a replacement paragraph. Cross it often and you are submitting sentences you did not write and cannot defend when a reviewer asks about them. Read your journal’s instructions for authors, usually under a heading on generative AI or research integrity: responsibility for the text sits with people who can answer for it.

7. Is the criticism specific to your field, or true of any paper?

Psychology and health sciences have objections that barely exist elsewhere and that sink manuscripts here: what counts as adequate reliability evidence, measurement invariance before comparing latent scores, causal language on a cross-sectional design, effect sizes with intervals instead of a naked p value. Run a manuscript you know well and ask whether anything came back that a reader outside your field could not have said.

8. Can you tell whether it read the whole thing?

Long manuscripts can get truncated, and a review of your introduction presented as a review of your paper buys false confidence about the part that gets you rejected: the fatal objections live in method and analysis, in the middle of the file. Plant the test yourself: put a checkable oddity deep in the results, a reported degrees of freedom that cannot match your sample size, and see whether it comes back.

The twenty-minute test

Every claim on this page, mine included, is checkable in one sitting with a manuscript whose flaws you already know: the best candidate is a paper of yours rejected after review, because you hold the reports and therefore the answer key.

When something else suits you better

Plainly, because a comparison the author always wins is useless for deciding:

Frequently asked questions

Which is the cheapest tool to review a manuscript?

Entirely free: PaperReview.ai (English only, 15 pages, machine-learning venues) and q.e.d Science (with an institutional email). With a full one-off report: ScholarsReview $4.99, Previum €12, Review-it.ai $6.99 and Reviewer3 $11. What separates them is less the price than what they check: of those four, only Previum recomputes p values with code and returns your Word file with comments in the margin; only Reviewer3 has an API, a chat and an AI-text detector.

Does any of them review manuscripts in Spanish?

Reviewing the content and returning the report in Spanish is what Previum does (also in English, Portuguese and French, with the manuscript in any of them). Editing the Spanish prose is what Trinka, QuillBot, DeepL Write, Wordvice AI and, less deeply, Grammarly do. General-purpose chats work in any language, with whatever quality you know how to ask for. The rest of the table works in English and declares no other language.

What is the difference between “verifying references” and “checking reference format”?

Verifying means asking a bibliographic database whether the cited work exists with that title, those authors and that DOI: Previum does it (against eight databases, already in Basic), as do Reviewer3, PeerReviewAI, Manusights, Paperpal on its paid plans, Octym and, in packs, ScholarsReview. Checking format means making sure the citation is well written and matched to the list, without asking whether it exists: that is what Penelope, Preflight, Editage Digital Editing, Scribbr’s Citation Editing and human editing do. The cell in the table tells them apart.

Where do the figures come from and when do they expire?

Every price was read on the tool’s own page on 17 September 2026 and checked by a second reader the same day; where they disagreed, the second reader’s figure is the one here. Several figures are promotional (Paperpal, Trinka, Wordvice AI, Enago Lite) and the cell says so. Every row carries its source: look at it before deciding, and if you spot an outdated one, write to me and I will correct it with the date.

Why trust a comparison written by a competitor?

Not blindly: that is why every row links to its source, the table includes what Previum does not do, there is a section on the cases where another tool suits you better, and the benchmark figures come from Reviewer3’s own benchmark, with their code and their judge. If you find an error, write to me and I will fix it with the date of the change.

Before you decide with a table, decide with your manuscript: upload it and read the finding that decides, with the quoted sentence and the fix, plus your references checked against eight databases and your p values recomputed. Basic report free with your account.

Review my manuscript free

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