Will your paper be accepted? Journal acceptance probability checker
Run your title and abstract through a fast pre-submission check: acceptance odds for top, mid and open/emerging journal tiers, plus recommended target journals from a 17,500-venue index. The odds come from where the most similar papers in the 4.5M-paper local library were published, scaled by per-field acceptance-rate anchors. Create a free account: 25 credits to start, each run costs 15 credits.
A pre-check is the quick equivalent of asking a senior colleague: 'is this paper good enough to send out, or do I need another round?' Most authors never get an honest answer to that question because the people qualified to give it are too busy.
Our pre-check answers part of it from a title, abstract, and keywords — no full draft, no upload. The output is an acceptance probability for three journal tiers — top (Nature, Science, NEJM and peers), mid (PLOS ONE, domain Q1/Q2 journals) and open/emerging — plus the detected scientific field and the topics your abstract matched, with a one-click handoff to the Research Gap Finder when you want the gaps themselves.
How the estimate is made
The estimate is not pulled from a generic LLM — computing it makes no language-model call. It works in three steps. First, it retrieves up to 200 papers most similar to your title, abstract and keywords from our 4.5M-paper local library. Second, it looks up the journal each of those papers appeared in against our curated list of top, mid and open/emerging journals. Third, it turns that mix into three acceptance probabilities, anchored to acceptance-rate baselines for your detected field.
When too few of the similar papers sit in journals on our curated tier list, the odds are rougher: they lean on the Scimago quartiles of the journals those papers appeared in or, failing that, on the field's base acceptance rates. The odds reflect where work like yours has been published; they are not a judgement of your methods or results. The same run adds four quality sub-checks on the abstract — novelty, structure, methodology and style — each marked passing or needing attention, with a one-line reason. (Our 69K+ real peer reviews calibrate the full AI Review's agents, not this estimate.)
Where to publish — matched target journals
Every pre-check run also returns a ranked shortlist of journals your manuscript fits, drawn from a 17,500-venue index built from our 4.5M-paper local library. Each recommendation carries a match score, an open-access flag, a citation rate (OpenAlex cites per paper, not a Journal Impact Factor), the publisher, a tier, and similar papers we've already indexed in that venue. Venues flagged as possibly predatory — from Beall's archive or our name-pattern heuristic — are left out of the shortlist by default.
The journal matching makes no external API call: it runs locally against a baked FTS5 + Reciprocal Rank Fusion index, so a run returns in seconds, and the whole pre-check bundle (tier odds + journals + matched topics) costs 15 credits. The same panel appears in the full AI Review report.
When to use the pre-check vs the full review
The pre-check is the right call when:
You have a working title and abstract but the full draft isn't done yet — get a read before investing 80 more hours.
You're choosing between 2–3 target venues and want a quick read on which tier is realistic.
You just got an R&R and want a fresh read on where the revised abstract sits.
You're a PhD student and your advisor is on sabbatical for the next month.
What the full 8-agent review adds
When you want more than a triage read, upgrade to the full AI Review — 30 credits, pay-as-you-go, no subscription. The full review reads your manuscript and runs 8 specialist agents — Methodology, Formulas & Equations, Originality, Literature Coverage, Reproducibility, Clarity & Language, Figures & Tables, Prior Publication — after a prior-publication check across CrossRef, PubMed, arXiv, bioRxiv, medRxiv, Europe PMC and Unpaywall and our 4.5M-paper local library (12-second timeout per source). Its language-model agents are calibrated on 69K+ real peer reviews from 19+ open-review platforms.
Full turnaround: under 15 minutes. Output: a per-agent structured report with revision suggestions, copyable as Markdown, JSON or plain text.
Frequently asked questions
Paste your title, abstract, and 3–8 keywords into the pre-check. In seconds you get acceptance odds for top, mid and open/emerging journal tiers — estimated from where the most similar papers in the 4.5M-paper local library were published, scaled by per-field acceptance-rate anchors — plus the detected research field and a shortlist of journals that fit. It's a fast way to judge whether to submit now or revise first. Create a free account to run it — 25 credits to start, each run costs 15 credits.
It estimates how likely your manuscript is to be accepted before you submit. Ours gives a probability for each of three journal tiers — top, mid and open/emerging — from where the most similar papers in the 4.5M-paper local library were published, scaled by per-field acceptance-rate anchors. It is a triage signal from your title and abstract, not a reading of the full paper.
Creating an account is free and comes with 25 credits to start. Each pre-check run costs 15 credits — that covers the tier odds, detected field, journal shortlist, prior-publication scan and the topics your abstract matched, in one pass. Credits are bought in one-time packs of 50 and never expire; there's no subscription. We keep pre-submission feedback inexpensive on purpose, and earn revenue from authors who go on to use the full review or unlock a research-gap topic.
We have not published a measured accuracy for the acceptance estimate, so we will not quote one. What it is: the odds come from where the most similar papers in the 4.5M-paper local library were published, scaled by per-field acceptance-rate anchors; when too few of the similar papers sit in journals on our curated tier list, the odds are rougher: they lean on the Scimago quartiles of the journals those papers appeared in or, failing that, on the field's base acceptance rates. The benchmark we do publish is for the journal shortlist that comes with it: on 46 real published papers, the paper's actual venue was in our top 5 for 54.3% of them and in our top 10 for 67.4% — method and failures included on the benchmarks page.
No. Pre-Check has no model to train: it compares your title and abstract with papers already in our library, and what you paste is not added to that library. Each run is saved to your own My Pre-Checks history so you can reopen and compare runs as you revise.
It points you at them. Pre-Check reports how many similar papers it retrieved from our 4.5M-paper local library and the topics your abstract matched, then hands off in one click to the Research Gap Finder. Searching there is free with an account; unlocking one topic (50 credits) fills a literature comparison table from the same local library and writes the research gaps from its rows.
Yes. Every run returns a ranked shortlist of target journals from our 17,500-venue index — ten shown first, more on request — each with a match score (A–F letter grade), open-access flag, citation rate, publisher, tier, and similar papers we've indexed in that venue. Venues flagged as possibly predatory are left out of the shortlist by default. It is bundled into the same 15-credit pre-check run: the index is built from our 4.5M-paper local library, so the matching never touches a paid API.
Yes — please do. Iterating between the pre-check and revision is exactly the workflow we built it for. Each run costs 15 credits; if you re-run the same title and abstract soon after, we first point you to the saved result so you are not charged twice by accident, and you can still run it fresh.
Pre-Check matches your words against an English-language paper index, so it needs an English title and abstract. If most of what you paste is not English, it warns you before running — you can go ahead anyway, but the matches will be weak. For a paper drafted in another language, paste a translated abstract for the pre-check, then run the full AI Review on the manuscript.
Three things: (1) the odds are built from where papers like yours were actually published, not from a language model's impression of your abstract; (2) your detected field sets the acceptance-rate baselines they are anchored to; (3) the journal shortlist and matched topics come from our 4.5M-paper local library and a 17,500-venue index — ChatGPT can't query either. The pre-check is built for one job; ChatGPT is built for thousands.
If the top- or mid-tier odds look strong, work down the journal shortlist. If they look weak, read the quality sub-checks — they name the specific issues the run flagged — and revise. For line-level guidance, run the full 8-agent review, or take the matched topics to the Research Gap Finder (searching is free) for repositioning angles.