Best AI Detector: 3 Text Checkers Compared

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Three documents are flagged as likely AI-generated. One is an analyst’s report written from the company template. Another is an editor’s translation. The third began as an AI draft. The score doesn’t tell you which is which. It gives you a reason to look at the work, not a record of how it was written.

This comparison covers text only, not AI-generated images, audio or video. The recommendations are based on documented language coverage, how useful the reports are to reviewers, and the work each product fits. They aren’t an accuracy leaderboard from a head-to-head test of Hong Kong copy.

A woman reading documents at a desk with a laptop

The top 3 AI detectors, by use case

Rank	Tool	Best for	What to check
1 — Best overall	Copyleaks	Teams reviewing English, Traditional Chinese and Simplified Chinese	Its API documents both Chinese variants, but its optional AI Logic phrase explanations aren’t available in Chinese.
2 — Best for English draft review	GPTZero	Editors, teachers and individuals who want to review highlighted sentences	Its percentage is confidence in a document classification, not the share of words written by AI.
3 — Best for publishing workflows	Originality.ai	Teams that want AI detection and plagiarism checks in one workflow	Both Chinese variants are listed. Keep the scan model and credit usage in mind when comparing results.

Copyleaks is the best starting point for a multilingual team. Its API documentation distinguishes Simplified Chinese from Traditional Chinese and tells you which languages receive additional phrase-level explanations. That’s more useful than an unspecific claim of Chinese support. It still doesn’t establish how well the product handles Hong Kong business prose, written Cantonese or a document that switches between English and Chinese.

GPTZero is a practical first check for an English draft. Paste the text or upload the file to see a document-level classification and sentence highlights. On a long report, the highlights give an editor somewhere to start. They don’t establish who wrote each sentence, and a highlighted line deserves particular care when reviewing work by someone writing in a second language.

Originality.ai fits a publishing desk that already checks for both overlap and AI generation. Plagiarism detection searches for matching material. AI detection estimates how likely the writing is to fall into a generation category. A draft can be original and still receive a high AI score. Treat the two checks as separate findings, even when they come from the same interface.

A man reviewing material beside a laptop

What an 80% AI score does—and doesn’t—say

In GPTZero, a percentage alongside a classification reflects the detector’s confidence in that class for the document. An 80% reading is not a count of the words written by AI. A “Mixed” result doesn’t allocate the work between the author and a tool, either.

Consider a batch of 1,000 submissions, 100 of which were AI-generated. Say a detector catches 90 of those but incorrectly flags 5% of the other 900. That leaves 135 flags: 90 correct and 45 false positives. It caught nine out of ten AI-generated texts in this example, yet one in three flags still points to a human-written submission. These figures illustrate the arithmetic; they aren’t test results for any product above.

If two detectors disagree, averaging their percentages doesn’t solve the problem. The tools may be reporting different categories, thresholds and scoring conventions. Save the exact version of the draft, the scan date and the detector model. Then look at the passage that prompted the question.

Handwritten notes and a highlighter on a desk

What public evaluations can tell you

The strongest tool in one study isn’t automatically the best fit for your documents. In a 2026 evaluation of five tools on 180 academic-writing samples, Originality.ai led at 84.4% overall accuracy. Across the tested tools, accuracy on hybrid texts ranged from 54.2% to 70.8%. That was one study with specific samples and tool versions. It doesn’t rank today’s products on a set of Traditional Chinese client reports.

An earlier study by Liang and colleagues found that the detectors they evaluated could misclassify English writing by non-native speakers. It’s a reason to build a fair review process, not evidence that every current product has the same error rate. If your writers regularly translate copy, use standard wording or work in English as a second language, include examples of those documents in your own trial.

For a bilingual business document, be clear about what you actually scanned. A product specification, its standard disclaimer and the writer’s original explanation serve different purposes. If your comparison is about original prose, keep the scope consistent across documents and note which template sections you excluded.

A fair way to handle a flagged draft

Start by identifying the issue. Are you checking a published AI-use policy, a potential plagiarism problem, or the accuracy of the material? Those are different questions. If grades, disciplinary action or a contractual decision are at stake, a detector result alone isn’t enough.

For an editorial review, I’d start with the source material: draft history, interview notes, citations and the figures that appear in the copy. Ask the author to explain the section in question. If the policy allows grammar assistance, translation or brainstorming, distinguish that from drafting that the policy prohibits. Not having a complete version history doesn’t prove misconduct.

If you’re buying for a team, assemble a small set of non-confidential examples with known origins. Include human writing, AI drafts and human-edited AI drafts separately for English, Traditional Chinese and Simplified Chinese. Track false positives, missed cases and how long follow-up takes. A small trial can tell you whether the workflow is useful, not whether the tool achieves a universal accuracy rate. Check the organisation’s rules and the vendor’s data terms before uploading student work, client files or unpublished material.

Someone making handwritten notes beside a laptop

Should a website article score 0% AI before publication?

Google’s public guidance doesn’t require a pass from a third-party AI detector. It warns against using automation to produce pages at scale without adding value for users. I’d spend editorial time on checking prices, service details, examples and sources before trying to change a detector score. A lower score won’t correct a wrong price or a claim your business can’t support.

If you’re choosing today, try Copyleaks for three-language review, GPTZero for English drafts and Originality.ai for a publishing workflow that also needs plagiarism checks. Use the report to decide what needs a closer look. Leave the authorship decision to a review that can consider the evidence.

Sources and image credits

Product documentation checked on 12 November 2026. The photographs illustrate review and teaching settings, not product screens or benchmark results.