A GPTZero alternative that answers the middle case.
Three named verdicts and a score on every sentence — free, no account.
50 free scans a day · 2,500 words per scan · text is scored in memory and never stored
Why this page exists
The middle case needs a name, not a percentage
GPTZero is often the first detector a teacher or student tries: paste text, read a percentage. That habit is useful and incomplete. A headline figure does not tell you which sentences earned it, and a number in the forties is the tool declining to decide. markhuman keeps the free and the instant — 50 scans a day, 2,500 words each, no account — and changes what comes back: a named verdict, with a score on every sentence.
We refuse to score fewer than 50 words. Below that length, any detector's output is closer to noise than to evidence. Mixed documents (a model drafted, a person edited) get their own verdict rather than a percentage you have to interpret. That mixed case is the document most people actually have, and it is the one a single AI-probability handles worst.
Will this agree with GPTZero on the same text? Sometimes. Detectors are different classifiers trained on different corpora with thresholds drawn in different places. Disagreement is normal. Where two tools split, the honest reading is that the text is genuinely ambiguous and deserves a human look — not that one vendor is lying.
We do not publish head-to-head benchmarks against GPTZero or anyone else, and this page contains no accuracy percentage for either tool. Vendor-measured numbers describe the vendor's own test set, not your document. Short passages, non-native English, and heavily edited drafts are hard cases for the entire field, ours included. Text is scored in memory and never stored.
The number problem
One score cannot
describe two authors
A single AI probability works cleanly on a document that is entirely one thing. It breaks on the document most people actually have in front of them — a draft that started in a chat window and got edited, or a human draft a model tightened. That document does not have one author, so it does not have one honest number.
Human-Written
The rhythm, the vocabulary, and the shape of the argument all sit inside the range a person produces. A number near zero tells you the same thing, but it makes you decide where zero-ish ends.
AI-Assisted
The verdict a single percentage cannot express. A model drafted it and a person rewrote it, or a person drafted it and a model tightened it. On a 0-100 scale this lands in the middle and gets argued about. Here it is a named answer.
AI-Generated
The document reads as model output from the first line to the last — including text that has already been through a humanizer, which is the case the model was specifically trained on.
The practical consequence shows up the moment a result has to be discussed with another person. Sixty-two percent starts an argument about where the line should have been. AI-Assisted starts an argument about the writing, which is the argument worth having. It is also the category most AI policies are quietly missing: they say what happens if a student or a contributor used a model, and say nothing at all about the far more common case where someone used one and then did real work on top of it.
Approach & audience
Different design
centre, different tool
This is not a quality ranking — it is a description of what each tool was shaped around. GPTZero was shaped around the classroom and the signals it made its name on. markhuman was shaped around handing a writer, an editor, or a reviewer a report they can act on line by line.
GPTZero
- Publicly built its early reputation on perplexity and burstiness as its headline signals.
- Positioned at teachers and institutions reviewing student work.
- The classroom is the design centre — the reader of the report is usually not the writer of the text.
markhuman
- Scores joint lexical and structural patterns across the document, then again on every sentence.
- Explicitly trained on AI text pushed through commercial humanizers and paraphrasers.
- Anyone can run it on their own writing — no account, no card, no institution required.
If you are a teacher marking a stack of submissions inside a gradebook, a tool designed for exactly that is a reasonable thing to want. If you are the person holding the draft — a writer checking their own work, an editor vetting a contribution, a reviewer who has to explain a decision — the report you need is the one that shows you which lines earned the verdict.
Reading a report
Category first,
sentences second
The order matters. People who read the percentage first end up negotiating with it. People who read the verdict and then the shading end up looking at the writing.
Start with the category
Read the verdict before the number. It is the call the engine is willing to defend, and it already accounts for the mixed case instead of leaving it to you.
Then read the shading
Every sentence carries its own score on a four-step strength scale, shaded in place inside your text. Two documents can score alike and look completely different once the shading is on.
Argue with the sentences
A percentage is hard to discuss. A specific highlighted line is not — you can point at it, defend it, or rewrite it. That is the part of a report that actually changes what happens next.
What we will not claim
No benchmark numbers on this page
We do not publish head-to-head benchmarks. There is no third-party-verified accuracy figure on this page for markhuman and none for GPTZero, and you should be sceptical of any comparison page that shows you one without an independent study behind it. No detector is perfect. Treat a report as evidence to weigh — especially on short passages, non-native English, and heavily edited drafts — not as proof on its own.
Everything above is either a structural fact about the markhuman report — three verdicts, a score on every sentence, no stored text — or a description of what each tool is publicly positioned to do. Where you want the full picture of what the detector can and cannot tell you, the AI detector page spells it out in more detail.
FAQ
Questions about switching
What the three verdicts change in practice, what the engine looks at, and what happens when two detectors disagree.
Is markhuman a free GPTZero alternative?
The scanner on this page runs without an account and without a card: 50 scans a day, up to 2,500 words per scan, and at least 50 words needed before the engine will score anything. Paid plans exist for longer documents and larger monthly volumes, but nothing on this page is gated behind them.
What does a three-way verdict give me that a single AI percentage does not?
A decision. A percentage in the middle of the range is the most common result on real documents and the least useful one, because the tool has quietly handed the judgement back to you. markhuman names the middle case — AI-Assisted — as its own category, so a mixed document gets described as mixed rather than rounded toward whichever neighbour your threshold happens to sit next to.
What exactly counts as AI-Assisted?
Either direction of collaboration: a machine draft that a person edited, or a human draft that a model rewrote. It is not a hedge or a low-confidence flag — it is a positive claim that the text carries both signatures. If you are setting a policy, this is usually the category that policy needs to speak to, because it is where most real work now lands.
Does markhuman use perplexity and burstiness?
Not as its headline signal. It scores joint lexical and structural patterns across the whole document and again sentence by sentence, then reconciles those into one verdict. The training set also includes AI text run through commercial humanizers and paraphrasers, mirrored against human originals, because that is the evasion route people actually use.
Will markhuman and GPTZero return the same result on the same text?
Sometimes, and we cannot promise you which times. Detectors are trained on different data and draw their thresholds in different places, so disagreement between any two of them is normal rather than a sign that one is broken. If two tools disagree on a passage, that passage is genuinely ambiguous and deserves a human read.
Can I check text that has already been through a humanizer?
That is the case the model is specifically trained for, and it is still the hardest one. Paste the rewritten version rather than the original and read the sentence shading rather than only the headline verdict. Treat a borderline result on heavily rewritten text with more caution than a clean one.
Run the same paragraph through both
The honest way to pick a detector is to feed it writing you already know the answer to. Free — 2,500 words per scan, 50 scans a day, no card.