DeAIze vs Crossplag: Which Tells You What to Fix?

Crossplag and DeAIze both score text for machine-made patterns, but they answer different questions. Crossplag’s detector returns a document-level reading of how much of your text looks generated. DeAIze returns a document score plus a per-sentence AI probability, so you can see which specific lines carry the signal and edit them. If you searched “deaize vs crossplag” you are probably holding a flagged draft and deciding where to spend your next hour. That decision is the whole point of this page.

What Crossplag actually reports

Crossplag is a plagiarism and AI detection platform used by schools and publishers. Its AI detector returns a single reading for the document you submit: a number that describes how much of the submitted text the model classifies as likely machine-generated. It is a document-level output. You get one figure, not a map.

That is genuinely useful in some workflows. If you are triaging a pile of submissions and only need to know which ones deserve a closer look, a single reading per document is fast and cheap to interpret. You do not need sentence-level detail to sort a stack into “read this one properly” and “this one is fine.”

What it does not do is tell you which sentences pulled the score up. If your draft reads as half-flagged, you still have to find the half yourself. For a writer trying to fix their own work, that is the expensive part.

What DeAIze reports instead

DeAIze is a web app with two halves: an AI detector and an AI humanizer. Detection works at sentence level. The whole document gets a score, and each sentence is highlighted by AI probability. You see the reading and the reasons behind it in the same view.

That changes what you do next. Instead of guessing which paragraphs are dragging the number up, you look at the highlights and start there. A flagged sentence is a specific object you can rewrite. A flagged document is a mood.

The humanizer half suggests revised wording for your own draft. It does not write the piece for you and it does not claim to. You compare the suggestion with your original and verify claims, quantities, citations, negations and qualifiers before accepting anything. Semantic similarity is a review aid, not proof that every meaning survived the edit.

If you are weighing the two tools on cost rather than output, the pricing page lists DeAIze credit packs and the free rewrite trial terms side by side.

The honest limit on both tools

Neither tool proves authorship. Not Crossplag, not DeAIze, not anything else on the market. A detector output is a model reading of surface patterns. It is evidence about text, not evidence about who typed it.

Score definitions also differ by instrument. A model score, a share of qualifying prose, and a similarity percentage are three different quantities. Comparing a Crossplag number to a Turnitin number to a DeAIze number as if they were the same unit is a category error, and it is the most common mistake people make when they are scared about a flag.

We ran our own detector on two corpora on 2026-10-04. A reference sample of 1,504 passages drawn from DeAIze guides scored a mean of 27% and a median of 20%, with 487 of those 1,504 passages at or above our 30% flag threshold. Unedited model output, 12 passages generated from fixed prompts and left alone, scored a mean of 52% and a median of 49%.

Read that carefully. The reference sample was selected by metadata, not by verified human authorship, and passages from the same guide are not independent samples. Those are passage counts, not a false-positive rate for human writers. The overlap between the two distributions is the real story: some human-edited prose scores high, and some model output scores low. That is why a single number cannot settle an authorship question.

Where the two approaches genuinely differ

	Crossplag AI detector	DeAIze detector
Output level	One document reading	Document score plus per-sentence AI probability
What you learn	Whether the text as a whole reads machine-made	Which lines carry the signal
Best for	Triaging submissions at volume	Editing your own draft line by line
Rewriting help	Separate from detection	Built in, with a rewrite trial of 200 words after email confirmation
Pricing model	Platform subscription	Pay-as-you-go credit packs, credits never expire
Languages	Broad	English-first; detection and rewriting cover English and 9 other languages
What neither does	Prove authorship	Prove authorship

There is no winner column here because the columns answer different questions. If your job is sorting, a document score is the right instrument. If your job is fixing, you need the sentence map.

Why editors end up wanting both

A realistic pipeline uses a coarse tool first and a fine tool second. Run a bulk reading across everything you received. Anything that comes back clean, move on. Anything that comes back borderline or high, open in a sentence-level tool and look at the actual lines.

The reason this matters is that a high document score is not actionable on its own. You cannot send a student or a freelancer a single percentage and expect them to do anything useful with it. You can send them a marked-up page with the flagged sentences highlighted and say: these lines, rewrite these. That is a task. The other is an accusation.

If you are on the receiving end of that accusation rather than the sending end, the false positive guide walks through what to document and how to respond without pretending the flag did not happen.

A worked example, clearly labelled as an illustration

Here is a passage of the kind that tends to score high. This is invented for demonstration, not a real measurement:

In today’s rapidly evolving digital environment, it is essential for organisations to leverage comprehensive strategies that facilitate seamless integration across multiple operational domains. By implementing robust frameworks, stakeholders can ensure optimal outcomes while maintaining alignment with overarching objectives.

No numbers in it. No specifics. Four abstract nouns doing the work of one concrete one. Every sentence is the same length and the same shape. If you run this through any sentence-level detector, most lines will highlight.

Now the same idea, edited by a person who knows what they mean:

We moved three teams onto one system last quarter. It broke twice. The second failure cost us a week, and that week is why the migration plan now has a rollback step.

Same topic. Different instrument. The second version has dates, counts, a failure, a consequence. A detector reading it has much less to grab onto.

That is the entire editing method. Not synonym-swapping. Specifics.

What the rewrite side actually does

DeAIze’s humanizer suggests revised wording for text you wrote. It is an editing aid, not a laundering service. You still own every claim in the final draft, and you still have to check that the suggestion did not quietly change a number, drop a citation, or flip a negation.

Three things to verify every time before you accept a suggestion:

1. Quantities and units. Did “3,000 words” stay 3,000 words?
2. Negations and qualifiers. Did “does not” survive as “does not”? Did “usually” become “always”?
3. Named entities and citations. Did the author name, the date, or the reference stay attached to the right claim?

If a suggestion fails any of those, reject it. Fluency is not accuracy.

For longer documents the workflow matters more than the tool. Rewriting a Word document preserves fonts, styles and tables, which means you can work through a report without rebuilding its formatting afterward. Uploads accept .txt, .docx and .pdf. If you are handling a thesis or a grant proposal with structured sections, the citation-safe workflow is worth reading before you start.

What to do when the two tools disagree

They will disagree. A document that Crossplag reads as mostly clean might have four sentences that DeAIze highlights hard. A document Crossplag flags might come back from DeAIze with most sentences well under threshold.

Neither result is a contradiction, because they are not measuring the same thing at the same resolution. The useful move is not to decide which tool is right. It is to ask what each one is telling you.

Document-level flag, sentence-level clean: look at the overall structure. Repeated paragraph shapes, uniform sentence lengths, and a lack of specific detail can pull a document reading up even when individual lines look fine.

Document-level clean, sentence-level flags: usually a few boilerplate lines. Transitional sentences, summary sentences, and anything that opens with “Furthermore” or “Additionally” are common culprits. Fix those and move on.

Both flagged: that is a real editing job, and it is worth doing properly rather than running the text through a paraphraser. If you want the underlying mechanics, how AI detection works explains what the models are actually scoring.

The cost question, answered plainly

Crossplag sells platform access. DeAIze sells credit packs at $20.99 for 1,000 words, $44.99 for 3,000 words, $99.99 for 10,000 words, and $199.99 for 30,000 words. No subscription. Credits never expire. There is a rewrite trial of 200 words after email confirmation, no credit card required; detection has separate configurable limits shown in the current interface.

If you process a steady stream of documents, a subscription can make sense. If you fix one draft every few weeks, buying credits as you need them is cheaper and you are not paying for idle months. That is the whole trade-off. Pick the shape that matches your actual volume, not the shape that sounds more professional.

Privacy and what happens to your text

Text you submit to DeAIze is processed for the current task only. It is never used for training and never resold. That matters more than people think, because the drafts people run through detectors are often the ones they least want in a training corpus: unpublished theses, client work, internal reports.

If you are evaluating any detector, ours included, the test is simple. Find the privacy page. Read what it says about retention and training. If it is vague, that is your answer. The privacy guide for pasting text into detectors covers what to check and what to redact before you paste.

Where each tool fits in a real week

A content team handling a backlog:

1. Run everything through a bulk document-level reading.
2. Sort into clean, borderline, flagged.
3. Open only the borderline and flagged items in a sentence-level tool.
4. Rewrite the highlighted lines using your own knowledge of the subject.
5. Re-read the whole piece for meaning, not just for score.

A student with one flagged essay:

1. Run it through a sentence-level detector.
2. Note which sentences highlight and ask why each one does.
3. Rewrite them with specifics you actually know.
4. Check that no citation, number or qualifier moved.
5. Keep the original and the revision, dated.

A freelancer whose client flagged a deliverable:

1. Do not argue about the score.
2. Show the sentence-level breakdown.
3. Explain what you changed and why.
4. Agree on a disclosure line for future work.

None of those workflows require the detector to be right about authorship. They only require it to point at lines. That is the difference between a tool that starts an argument and a tool that ends one.

Ready to see which sentences read machine-made?

Paste your draft into the AI detector and look at the highlights rather than the headline number. The sentence map is where the actual work is. If you want to see what a rewrite suggestion looks like on your own text, the draft editor shows the original and the revision side by side so you can check every claim before you accept anything. And when you are ready to buy, credit packs start at $20.99 with no subscription and no expiry, or you can create an account and use the 200-word rewrite trial first.

Detectors disagree with each other for structural reasons, not because one of them is lying. Read the score, find the lines, fix your own prose. That order works regardless of which tool you open first.