CiteTrue vs ZeroGPT
ZeroGPT tries to answer "was this text written by AI?". CiteTrue answers a narrower and much more checkable question: "do these references exist, and do they say what the text claims?" The second question has a right answer you can verify by opening a link; the first does not.
Why our verdicts hold up
Most citation checkers hand the whole judgement to a language model and show you what it says. Models hallucinate — that is the very problem this category exists to solve — so we put the model last, not first.
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1. Parse, don’t guess
Every reference is split into its parts — authors, title, year, journal, DOI — by a parser built for citation formats, not by asking a model what it thinks the title is.
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2. Look it up in the authorities
The parsed reference is queried against Crossref and OpenAlex, and against the open web when a reference has no DOI. What comes back are real records with real identifiers.
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3. Score the match deterministically
Candidate records are scored with a Sørensen-Dice similarity over title tokens. A record counts as a match only if the title score clears 0.7 and the author list agrees — a year mismatch is reported as a notice, never quietly accepted. The same reference always produces the same score.
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4. Only then, ask a model
A language model is called only when the deterministic pass finds no authentic match — to read the candidates and the source text and decide whether anything fits. It is the fallback, not the judge. That ordering is why a hallucinated verdict has nowhere to enter: by the time a model is involved, the evidence is already on the table.
The practical consequence: a reference marked authentic here matched a real record on title and authors, and you can open that record. It is not a model's impression of whether the citation looks plausible.
How fast it gets through a bibliography
Checking one reference means several lookups across different sources. Doing that one reference at a time is what makes a tool feel slow on a long bibliography, so we run them in parallel and stream the results back.
- References checked in parallel
- 20 at a time in the app, 100 through the API
- A 50-reference bibliography
- Goes out as three waves of parallel lookups, not fifty queued ones
- Results as they land
- Streamed per reference — you read the first verdicts while the rest are still running
- Bulk work
- A supervisor’s stack of theses runs through the API at five times the app’s concurrency
Different jobs, compared
| What you are comparing | CiteTrue | ZeroGPT |
|---|---|---|
| The question it answers | Do these references exist, and do they support the claim? | Does this text look AI-written? |
| Can the answer be checked? | Yes — every verdict links to the record it is based on | No — a probability score with nothing behind it to open |
| False accusations | A failed check names the reference and the reason, so it can be argued with | Detectors are known to flag human writing, especially by non-native speakers |
| Use in an integrity case | Evidence you can put in front of the author | Widely disputed as sole evidence |
| Cost | Free for 30 a day, then from $5.99/mo | Free tier with paid plans |
| Convenience | Paste the bibliography; results per reference | Paste the text; one score for the whole thing |
So which should you use?
If you are trying to establish whether a document is trustworthy, fabricated citations are the hard evidence and a detector score is not. Check the references first — that finding stands up in a conversation with the author, and a probability score does not.
Check your references now
30 references a day, free, no card. Paste a bibliography and see for yourself.