AI FACT CHECKER
AI Fact Checker
An AI fact checker reads a claim and tells you whether it holds up. Paste one below: a statistic, a quote, a citation. One model checks it free, once a day. Paid plans run four independent models on a whole draft and show you where they disagree.
Council comes with any paid plan. Deep needs Max.
This usually takes 3-5 seconds.
Deep takes a minute or two: four pro models answer, two critics review, one mediator writes the final call. Leave this page open and the result lands here.
Claim Checker runs the moment you sign in, on the text you just pasted. Nothing to enter twice.
A saved check is kept for 24 hours.
You've used up today's free runs on this check.
Come back tomorrow for another free run.
Checking one line? The same check has its own page: Is this claim true?
We asked four AI models for sources on 100 questions. They made up 31% to 54% of them. — our own benchmark, 100 questions, three runs, August 2026. See the measurement.
What is an AI fact checker?
An AI fact checker is a tool that reads a piece of text, isolates its factual claims, and checks each one for accuracy, flagging fabricated citations, invented statistics, misattributed quotes, and wrong dates before the content is published.
Most AI fact checking tools ask a single model to grade the text. That is the weak version, because the model doing the checking shares the same blind spots as the model that wrote it. TrueStandard runs your draft through four to five models from rival labs at once. It reports the specific claims they do not all agree on. Agreement across independent models is a signal a claim is sound; disagreement points you straight at what needs a human. Four models gave us 1,200 answers. They made up 946 different sources. Only 2 of those fakes came from more than one model.
How AI fact checking works at TrueStandard
Four steps, about 60 seconds, one clear list of what to verify.
Paste your draft
Drop in the AI-assisted text you are about to publish: a newsletter issue, an article, a client deliverable, a script.
Four to five models check it in parallel
Frontier models from different vendors independently evaluate every factual claim at the same time.
Disagreements are surfaced
You get back the specific claims the models split on, ranked by how much they diverge. The consensus claims pass; the contested ones get flagged.
You get a receipt
A shareable record of what was checked and where the models agreed. It is proof you fact-checked, for an editor, a client, or your audience.
Single-model vs multi-model AI fact checking
The question that decides whether a check is worth trusting is independence: was the claim evaluated by something that does not share the original model's blind spots?
| Approach | What it catches | Independent? | Best for |
|---|---|---|---|
| Re-ask the same AI to double-check | Little. It shares the blind spot that made the error | No | A quick gut-check, not verification |
| A single-model AI fact checker | Some claims, from one model's point of view | One model | A fast first pass |
| An AI detector (Turnitin, GPTZero) | A guess at whether AI wrote it, not whether it is true | Wrong question | Nothing, for accuracy |
| Manual source-checking by hand | Nearly everything, if you have the time | Yes (human) | One high-stakes claim, slowly |
| TrueStandard multi-model consensus | Every claim the vendors disagree on, flagged | Four vendors | Every claim in a draft, in ~60s |
The top rows are fast but not independent, or independent but slow. Multi-model consensus keeps the independence and drops the tab-switching.
Why one AI can't fact-check another reliably
A single model is trained to be helpful, which makes it agreeable: ask it to check its own work and it will often defend what it just wrote. Ask a different single model and you have swapped one set of blind spots for another. The failure is structural. Independent benchmarks still put hallucination rates for frontier models in the 17 to 34 percent range on factual claims. Verification needs more than one independent judge, from more than one vendor, with disagreement made visible.
Because the models come from different labs, trained on different data, they rarely invent the same wrong fact in the same way. When they split, they have handed you the exact claim to verify.
What the fact checker catches
The failure modes that read as true and get published anyway.
Fabricated citations
References and sources that look real, in the right format, but do not exist. The most damaging AI failure mode.
Invented statistics
Precise-sounding numbers with no basis: the stat that anchors your argument and cannot be traced to a real source.
Misattributed quotes
Plausible wording assigned to the wrong person, or to no one at all.
Wrong dates and facts
Confident specifics like dates, names, and figures, stated with the same certainty as the claims that happen to be right.
Statistics checker: is that number real?
Numbers are the claims that travel. A precise-looking figure gets screenshotted and repeated long after anyone remembers where it came from, and AI drafts produce them freely: the study that found 13%, the market worth 1.8 trillion. Paste the sentence with the number in it and the checker tells you whether the figure matches anything published, what range real studies land in, and how the sentence should read. It will not invent a source to back the number, and it says so when it cannot find one.
Check a statistic ↑One claim at a time, on its own page
The check above also lives at a URL built for one question: is this claim true? Same model, same verdict, same daily allowance. Bookmark it for the next statistic that looks too clean.
Open Is This Claim True? →Checking references? The citation checker resolves every DOI and link in a draft and reads each source against the sentence that cites it.
Open the AI Citation Checker →Checking an AI answer against policies you approved? Paste it with its links into the check for verifying AI answers against your sources.
Frequently Asked Questions
What is the best AI fact checker?
The most reliable AI fact checkers verify claims across multiple independent models, because a lone model shares the blind spots that produced the error. TrueStandard checks your draft with four to five frontier models from different vendors at once and flags every claim they disagree on. For a single claim, a free single-model checker is a fine fast pass; for a full draft you are about to publish, multi-model consensus is the safer choice.
Is there a free AI fact checker?
Yes. The check at the top of this page is free: one claim, one model, once a day, no account. To check an entire draft across four to five models at once and get a ranked list of the claims they disagree on, you use the full fact checker, which starts at 20 dollars a month.
Can I use this as a statistics checker?
Yes. Paste the sentence that carries the number, so the checker can see what the figure is claimed to measure. It reports whether the statistic matches published research, the range real studies land in, and a corrected sentence. One model does this free, once a day; a paid plan puts four models on it and shows where they disagree.
Can AI fact-check itself?
Not reliably. A model asked to check its own output runs the same process that produced the claim, so it tends to confirm its own work, and re-pasting into a fresh window of the same model has the same blind spots. AI fact checking requires independence: judgment from models trained differently, by different labs, with the points of disagreement surfaced.
How does AI fact checking work?
AI fact checking isolates the factual claims in a piece of text and evaluates each one for accuracy. A single-model tool asks one AI to judge the claims. A multi-model tool like TrueStandard runs the same claims through several models from different vendors in parallel and compares the results: claims all the models agree on pass, and claims they disagree on are flagged for a human to verify. The whole check takes about 60 seconds.
What is the difference between an AI fact checker and an AI detector?
They answer different questions. An AI detector guesses whether a text was written by AI, based on writing style, and it is often wrong, false-flagging human writing. An AI fact checker ignores who or what wrote the text and checks whether the claims in it are true.
How accurate is AI fact checking?
No fact checker is perfect. So we publish our own numbers. In August 2026 we asked four models for sources on 100 questions. We ran it three times. Then we checked every DOI against Crossref and DataCite. The models made up 31% to 54% of the sources they gave us. Those 1,200 answers held 946 different fake sources. Only 2 came from more than one model.
Check every claim in your draft at once.
The check above handles one claim. A verification run takes the whole draft. Four models read every claim and flag each one they disagree on. It takes about 60 seconds. One prevented correction pays for the month.