{"id":30881,"date":"2026-09-03T11:52:40","date_gmt":"2026-09-03T11:52:40","guid":{"rendered":"https:\/\/plagiarismcheck.org\/blog\/?p=30881"},"modified":"2026-09-03T11:52:55","modified_gmt":"2026-09-03T11:52:55","slug":"ai-detection-accuracy-and-method-evidence-numbers-and-competitor-comparison","status":"publish","type":"post","link":"https:\/\/plagiarismcheck.org\/blog\/ai-detection-accuracy-and-method-evidence-numbers-and-competitor-comparison\/","title":{"rendered":"AI Detection accuracy and method: evidence, numbers, and competitor comparison"},"content":{"rendered":"<p><span style=\"font-weight: 400;\">Unlike most popular AI detectors, our tool doesn&#8217;t produce a general document score. It checks every sentence, deciding the probability of it being AI-generated, and flags parts with AI traces in the report. How much AI presence is too much is up to you to judge.<\/span><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-30885\" src=\"https:\/\/plagiarismcheck.org\/blog\/wp-content\/uploads\/2026\/08\/Screenshot-2026-08-06-12.09.09.png\" alt=\"AI detection accuracy\" width=\"674\" height=\"334\" srcset=\"https:\/\/plagiarismcheck.org\/blog\/wp-content\/uploads\/2026\/08\/Screenshot-2026-08-06-12.09.09.png 674w, https:\/\/plagiarismcheck.org\/blog\/wp-content\/uploads\/2026\/08\/Screenshot-2026-08-06-12.09.09-300x149.png 300w\" sizes=\"auto, (max-width: 674px) 100vw, 674px\" \/><\/p>\n<p><span style=\"font-weight: 400;\">\u201c20% AI-generated\u201d result leaves you wondering whether it is a whole machine-generated paragraph or just small edits across the document. Highlighted lines give you a conversation-starting point and clear evidence to make data-driven decisions.\u00a0<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">How accurate is the result?<\/span><\/h2>\n<h3><span style=\"font-weight: 400;\">99.23% AI detection accuracy<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">We checked 33,699 sentences in 709 documents generated by seven frontier AI models and correctly classified 99.23% of them as AI, measured per sentence.<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">1.04% false positives (sentence level)<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Of 280,781 sentences written by people, we misclassified 1.04% as AI.<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">0.00% false positives (document level)<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">With a sentence-by-sentence check approach, a misclassified line doesn&#8217;t condemn the whole paper. This way, at the document level, our false-positive rate drops to zero. None of the 2,805 human-written documents were wrongly classified as AI.<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">Why two false-positive figures<\/span><\/h2>\n<section class=\"pc-research-embed pc-research-table\"><div class=\"pc-research-table-scroll\" style=\"max-height:420px;\"><\/p>\n<table>\n<thead>\n<tr>\n<th><b>Metric<\/b><\/th>\n<th><b>Result<\/b><\/th>\n<th><b>What it means<\/b><\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><b>Recall (sentence)<\/b><\/td>\n<td><b>99.23%<\/b><\/td>\n<td><span style=\"font-weight: 400\">Of all AI-written sentences, we correctly flag 99.23%. Measures how much AI text we catch.<\/span><\/td>\n<\/tr>\n<tr>\n<td><b>False positives (sentence)<\/b><\/td>\n<td><b>1.04%<\/b><\/td>\n<td><span style=\"font-weight: 400\">Of all human-written sentences, we wrongly flag 1.04%. Measures how often we unfairly accuse human text.<\/span><\/td>\n<\/tr>\n<tr>\n<td><b>Recall (document)<\/b><\/td>\n<td><b>100%<\/b><\/td>\n<td><span style=\"font-weight: 400\">Of 709 fully AI-written documents, we correctly identified every one.<\/span><\/td>\n<\/tr>\n<tr>\n<td><b>False positives (document)<\/b><\/td>\n<td><b>0.00%<\/b><\/td>\n<td><span style=\"font-weight: 400\">Of 2,805 human-written documents, zero were wrongly classified as AI.<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><\/div><div class=\"pc-research-embed-actions\"><button type=\"button\" class=\"inline-flex justify-center items-center gap-8 px-12 py-4 m-0 rounded bg-B400 text-sm text-white font-semibold whitespace-nowrap transition-[background,outline] hover:bg-B500 focus:bg-B500 focus:outline focus:outline-2 focus:outline-B200 is-small\" data-copy-citation=\"Table.\nNote. AI Detection accuracy and method: evidence, numbers, and competitor comparison, by Samuel Lee, 2026, PlagiarismCheck Blog (https:\/\/plagiarismcheck.org\/blog\/ai-detection-accuracy-and-method-evidence-numbers-and-competitor-comparison\/). \u00a9 2026 by Samuel Lee. CC BY 4.0.\" title=\"Table.\nNote. AI Detection accuracy and method: evidence, numbers, and competitor comparison, by Samuel Lee, 2026, PlagiarismCheck Blog (https:\/\/plagiarismcheck.org\/blog\/ai-detection-accuracy-and-method-evidence-numbers-and-competitor-comparison\/). \u00a9 2026 by Samuel Lee. CC BY 4.0.\">Embed<\/button><\/div><\/section>\n<p><span style=\"font-weight: 400;\">Was part of an essay generated by ChatGPT, or was it the detector catching formulaic-sounding extracts in a handful of sentences? With a general metric per document, it is hard to tell.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Occasionally, every detector mismarks the sentences that resemble robotic patterns as potentially AI. On their own, they don&#8217;t prove the document&#8217;s origin. What matters is the whole paper&#8217;s shape, the consistency of highlighted text, and its structure. Usually, an AI-generated essay won&#8217;t contain scattered machine-sounding phrases. It will have flagged whole chunks of writing, if not the majority of the text.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">So, we don&#8217;t hurry into classifying the whole paper. We analyze every sentence and highlight those containing AI traces. <\/span><span style=\"font-weight: 400;\">You set the threshold for what proportion of flagged content is alarming, and we give you evidence to decide whether it crosses your limit.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Initially, we classify every sentence. Hence, the primary metrics we provide are per sentence.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Our competitors provide one verdict per document, and their metrics are per document. To compare like for like, we translated our sentence measure into a document-level figure.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">At the document level, our error rate drops to zero, as with our approach, one misclassified sentence doesn&#8217;t condemn the whole paper.<\/span><\/li>\n<\/ul>\n<p><button type=\"button\" class=\"inline-flex justify-center items-center gap-8 px-12 py-4 m-0 rounded bg-B400 text-sm text-white font-semibold whitespace-nowrap transition-[background,outline] hover:bg-B500 focus:bg-B500 focus:outline focus:outline-2 focus:outline-B200 is-small\" data-copy-citation=\"Samuel Lee. (September 3, 2026). AI Detection accuracy and method: evidence, numbers, and competitor comparison. PlagiarismCheck Blog. https:\/\/plagiarismcheck.org\/blog\/ai-detection-accuracy-and-method-evidence-numbers-and-competitor-comparison\/\" title=\"Samuel Lee. (September 3, 2026). AI Detection accuracy and method: evidence, numbers, and competitor comparison. PlagiarismCheck Blog. https:\/\/plagiarismcheck.org\/blog\/ai-detection-accuracy-and-method-evidence-numbers-and-competitor-comparison\/\">Copy citation<\/button><\/li>\n<\/ul>\n<h2><span style=\"font-weight: 400;\">The threshold you set<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Checking thesis papers and editing a social media post are two different tasks that require different approaches to measurement. Sentence-by-sentence analysis allows us to provide a flexible threshold rather than a fixed operating point.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">You choose which setting fits your tasks today. High sensitivity is recommended for early intervention, and a more conservative setting is appropriate when the stakes are high and the consequences serious. The same scan supports both, with no re-processing needed.<\/span><\/p>\n<section class=\"pc-research-embed pc-research-table\"><h4 class=\"text-xl pc-research-h4\">How to interpret results<\/h4><div class=\"pc-research-table-scroll\" style=\"max-height:420px;\"><\/p>\n<table>\n<thead>\n<tr>\n<th><b>Content flagged as AI<\/b><\/th>\n<th><b>AI recall<\/b><\/th>\n<th><b>95% CI*<\/b><\/th>\n<th><b>False positives<\/b><\/th>\n<th><b>95% CI<\/b><\/th>\n<th><b>Suggested use<\/b><\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><span style=\"font-weight: 400\">&lt;5% of sentences<\/span><\/td>\n<td><b>100%<\/b><\/td>\n<td><span style=\"font-weight: 400\">99.5\u2013100<\/span><\/td>\n<td><b>5.45%<\/b><\/td>\n<td><span style=\"font-weight: 400\">4.67\u20136.36<\/span><\/td>\n<td><span style=\"font-weight: 400\">Screening only<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400\">&gt;10% of sentences<\/span><\/td>\n<td><b>100%<\/b><\/td>\n<td><span style=\"font-weight: 400\">99.5\u2013100<\/span><\/td>\n<td><b>2.10%<\/b><\/td>\n<td><span style=\"font-weight: 400\">1.63\u20132.70<\/span><\/td>\n<td><span style=\"font-weight: 400\">High sensitivity<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400\">&gt;20% of sentences<\/span><\/td>\n<td><b>100%<\/b><\/td>\n<td><span style=\"font-weight: 400\">99.5\u2013100<\/span><\/td>\n<td><b>0.64%<\/b><\/td>\n<td><span style=\"font-weight: 400\">0.41\u20131.01<\/span><\/td>\n<td><span style=\"font-weight: 400\">Balanced<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400\">&gt;30% of sentences<\/span><\/td>\n<td><b>100%<\/b><\/td>\n<td><span style=\"font-weight: 400\">99.5\u2013100<\/span><\/td>\n<td><b>0.25%<\/b><\/td>\n<td><span style=\"font-weight: 400\">0.12\u20130.51<\/span><\/td>\n<td><span style=\"font-weight: 400\">Conservative<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400\">&gt;<\/span><b>50% of sentences<\/b><\/td>\n<td><b>100%<\/b><\/td>\n<td><span style=\"font-weight: 400\">99.5\u2013100<\/span><\/td>\n<td><b>0.00%<\/b><\/td>\n<td><span style=\"font-weight: 400\">0.00\u20130.14<\/span><\/td>\n<td><b>Disciplinary review<\/b><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><i><span style=\"font-weight: 400\">(*CI=confidence interval)<\/span><\/i><\/p>\n<p><i><span style=\"font-weight: 400\">Measured on 709 AI documents and 2,805 human documents, July 2026.<\/span><\/i><\/p>\n<p><\/div><div class=\"pc-research-embed-actions\"><button type=\"button\" class=\"inline-flex justify-center items-center gap-8 px-12 py-4 m-0 rounded bg-B400 text-sm text-white font-semibold whitespace-nowrap transition-[background,outline] hover:bg-B500 focus:bg-B500 focus:outline focus:outline-2 focus:outline-B200 is-small\" data-copy-citation=\"Table: How to interpret results\nNote. AI Detection accuracy and method: evidence, numbers, and competitor comparison, by Samuel Lee, 2026, PlagiarismCheck Blog (https:\/\/plagiarismcheck.org\/blog\/ai-detection-accuracy-and-method-evidence-numbers-and-competitor-comparison\/). \u00a9 2026 by Samuel Lee. CC BY 4.0.\" title=\"Table: How to interpret results\nNote. AI Detection accuracy and method: evidence, numbers, and competitor comparison, by Samuel Lee, 2026, PlagiarismCheck Blog (https:\/\/plagiarismcheck.org\/blog\/ai-detection-accuracy-and-method-evidence-numbers-and-competitor-comparison\/). \u00a9 2026 by Samuel Lee. CC BY 4.0.\">Embed<\/button><\/div><\/section>\n<h2><span style=\"font-weight: 400;\">Which AI models we detect<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">When trained to recognize one model, the detector fails to catch the other provider&#8217;s output. Here is evidence that we are not just a ChatGPT detector. The total spread across seven flagship models is 1.56 points, which means we classify them with 98.31%-99.87% accuracy.<\/span><\/p>\n<section class=\"pc-research-embed pc-research-table\"><h4 class=\"text-xl pc-research-h4\">Which models we detect<\/h4><div class=\"pc-research-table-scroll\" style=\"max-height:420px;\"><\/p>\n<table>\n<thead>\n<tr>\n<th><b>Provider<\/b><\/th>\n<th><b>Model<\/b><\/th>\n<th><b>Documents<\/b><\/th>\n<th><b>Sentences<\/b><\/th>\n<th><b>Detected<\/b><\/th>\n<th><b>Recall<\/b><\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td rowspan=\"3\"><b>Google<\/b><\/td>\n<td><span style=\"font-weight: 400\">Gemini 3.1 Pro<\/span><\/td>\n<td><span style=\"font-weight: 400\">116<\/span><\/td>\n<td><span style=\"font-weight: 400\">4,578<\/span><\/td>\n<td><span style=\"font-weight: 400\">4,572<\/span><\/td>\n<td><b>99.87%<\/b><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400\">Gemini Flash 3.5<\/span><\/td>\n<td><span style=\"font-weight: 400\">93<\/span><\/td>\n<td><span style=\"font-weight: 400\">4,311<\/span><\/td>\n<td><span style=\"font-weight: 400\">4,245<\/span><\/td>\n<td><b>98.47%<\/b><\/td>\n<\/tr>\n<tr>\n<td><b><i>Provider total<\/i><\/b><\/td>\n<td><b>209<\/b><\/td>\n<td><b>8,889<\/b><\/td>\n<td><b>8,817<\/b><\/td>\n<td><b>99.19%<\/b><\/td>\n<\/tr>\n<tr>\n<td rowspan=\"3\"><b>Anthropic<\/b><\/td>\n<td><span style=\"font-weight: 400\">Claude Sonnet 4.6<\/span><\/td>\n<td><span style=\"font-weight: 400\">100<\/span><\/td>\n<td><span style=\"font-weight: 400\">5,035<\/span><\/td>\n<td><span style=\"font-weight: 400\">5,020<\/span><\/td>\n<td><b>99.70%<\/b><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400\">Claude Opus 4.7<\/span><\/td>\n<td><span style=\"font-weight: 400\">100<\/span><\/td>\n<td><span style=\"font-weight: 400\">5,576<\/span><\/td>\n<td><span style=\"font-weight: 400\">5,553<\/span><\/td>\n<td><b>99.59%<\/b><\/td>\n<\/tr>\n<tr>\n<td><b><i>Provider total<\/i><\/b><\/td>\n<td><b>200<\/b><\/td>\n<td><b>10,611<\/b><\/td>\n<td><b>10,573<\/b><\/td>\n<td><b>99.64%<\/b><\/td>\n<\/tr>\n<tr>\n<td rowspan=\"3\"><b>OpenAI<\/b><\/td>\n<td><span style=\"font-weight: 400\">GPT-5.4<\/span><\/td>\n<td><span style=\"font-weight: 400\">100<\/span><\/td>\n<td><span style=\"font-weight: 400\">7,421<\/span><\/td>\n<td><span style=\"font-weight: 400\">7,365<\/span><\/td>\n<td><b>99.25%<\/b><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400\">GPT-5.5<\/span><\/td>\n<td><span style=\"font-weight: 400\">100<\/span><\/td>\n<td><span style=\"font-weight: 400\">3,709<\/span><\/td>\n<td><span style=\"font-weight: 400\">3,668<\/span><\/td>\n<td><b>98.89%<\/b><\/td>\n<\/tr>\n<tr>\n<td><b><i>Provider total<\/i><\/b><\/td>\n<td><b>200<\/b><\/td>\n<td><b>11,130<\/b><\/td>\n<td><b>11,033<\/b><\/td>\n<td><b>99.13%<\/b><\/td>\n<\/tr>\n<tr>\n<td><b>xAI<\/b><\/td>\n<td><span style=\"font-weight: 400\">Grok 4.3<\/span><\/td>\n<td><span style=\"font-weight: 400\">100<\/span><\/td>\n<td><span style=\"font-weight: 400\">3,069<\/span><\/td>\n<td><span style=\"font-weight: 400\">3,017<\/span><\/td>\n<td><b>98.31%<\/b><\/td>\n<\/tr>\n<tr>\n<td><b>All<\/b><\/td>\n<td><b>7 models<\/b><\/td>\n<td><b>709<\/b><\/td>\n<td><b>33,699<\/b><\/td>\n<td><b>33,440<\/b><\/td>\n<td><b>99.23%<\/b><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><i><span style=\"font-weight: 400\">Unedited model output, generated and tested in July 2026. Recall measured per sentence.<\/span><\/i><\/p>\n<p><\/div><div class=\"pc-research-embed-actions\"><button type=\"button\" class=\"inline-flex justify-center items-center gap-8 px-12 py-4 m-0 rounded bg-B400 text-sm text-white font-semibold whitespace-nowrap transition-[background,outline] hover:bg-B500 focus:bg-B500 focus:outline focus:outline-2 focus:outline-B200 is-small\" data-copy-citation=\"Table: Which models we detect\nNote. AI Detection accuracy and method: evidence, numbers, and competitor comparison, by Samuel Lee, 2026, PlagiarismCheck Blog (https:\/\/plagiarismcheck.org\/blog\/ai-detection-accuracy-and-method-evidence-numbers-and-competitor-comparison\/). \u00a9 2026 by Samuel Lee. CC BY 4.0.\" title=\"Table: Which models we detect\nNote. AI Detection accuracy and method: evidence, numbers, and competitor comparison, by Samuel Lee, 2026, PlagiarismCheck Blog (https:\/\/plagiarismcheck.org\/blog\/ai-detection-accuracy-and-method-evidence-numbers-and-competitor-comparison\/). \u00a9 2026 by Samuel Lee. CC BY 4.0.\">Embed<\/button><\/div><\/section>\n<p><span style=\"font-weight: 400;\">As new models emerge and are continually upgraded, we retrain our detector to stay up to date. You can follow our recent upgrades <\/span><span style=\"font-weight: 400;\">here<\/span><span style=\"font-weight: 400;\">.<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">How we checked<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">To minimize false positives, the detector must be trained and tested on purely human-written text. AI presence in it affects the accuracy rate and test results. Here is how we avoided that.<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">Human text corpus from pre-AI era<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Our main corpus is the British Academic Written English (BAWE) collection, the real university coursework gathered between 2004 and 2007. It is 15 years before ChatGPT was launched; hence, it physically cannot contain AI traces. BAWE contains 2,688 documents (277,033 sentences), to which we added our own internal human corpus of 117 documents (3,748 sentences). 280,781 purely human-written sentences in total were used to test our AI checker.<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">AI text corpus from frontier models<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Seven LLM models, roughly 100 documents from each, 33,699 sentences in total. Each text used for testing is a pure AI output, with no paraphrasing or manual editing.<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">Per-sentence analysis<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Each document submitted for checking is split into sentences. The detector classifies each of them, and the test results are counted at the sentence level. <\/span><span style=\"font-weight: 400;\">The document-level score is calculated based on sentence analysis, and the final verdict is defined by the threshold you set, from 5% to 50%.<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">Data transparency<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Every rate we publish is reported with a 95% Wilson confidence interval. We reveal our sample size, 280,781 sentences, making the 1.04% false-positive rate figure meaningful rather than demonstrative.<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">Versioned and updated<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">AI models are being upgraded, and so are our AI detector and this report. Every figure and piece of data on this page is marked with a model version and the date when the test was run. We keep improving the tool, our test methods, and this report regularly.<\/span><\/p>\n<section class=\"pc-research-embed pc-research-table\"><h4 class=\"text-xl pc-research-h4\">How we tested<\/h4><div class=\"pc-research-table-scroll\" style=\"max-height:420px;\"><\/p>\n<table>\n<thead>\n<tr>\n<th><b>Element<\/b><\/th>\n<th><b>Description<\/b><\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><b>Segmentation<\/b><\/td>\n<td><span style=\"font-weight: 400\">Every document is split into sentences, and each sentence is classified independently as AI or human. We do not produce a single whole-document verdict \u2014 the model shows which specific sentences look AI-written.<\/span><\/td>\n<\/tr>\n<tr>\n<td><b>AI test set<\/b><\/td>\n<td><span style=\"font-weight: 400\">Roughly 100 documents per model across 7 frontier LLMs, 33,699 sentences total. All text is unedited model output \u2014 no paraphrasing or manual revision.<\/span><\/td>\n<\/tr>\n<tr>\n<td><b>Human test set<\/b><\/td>\n<td><span style=\"font-weight: 400\">Two corpora, 2,805 documents and 280,781 sentences in total. BAWE (2,688 docs \/ 277,033 sentences, FPR 1.05%) and our internal human corpus (117 docs \/ 3,748 sentences, FPR 0.61%). Combined FPR is 1.04%.<\/span><\/td>\n<\/tr>\n<tr>\n<td><b>Contamination control<\/b><\/td>\n<td><span style=\"font-weight: 400\">Our primary corpus BAWE was collected in 2004\u20132007, fifteen years before ChatGPT, so it physically cannot contain AI-written text. This is the main reason our FPR figure is trustworthy in a way most published numbers are not.<\/span><\/td>\n<\/tr>\n<tr>\n<td><b>Document-level derivation<\/b><\/td>\n<td><span style=\"font-weight: 400\">A document is flagged as AI if more than a set percentage of its sentences are flagged. We report thresholds from 5% to 50% so an institution can choose its own tolerance. The 0.00% FPR figure is at the &gt;50% threshold.<\/span><\/td>\n<\/tr>\n<tr>\n<td><b>Statistics<\/b><\/td>\n<td><span style=\"font-weight: 400\">All rates reported with 95% Wilson confidence intervals. The sample size (280,781 human sentences) is what makes a 1.04% figure meaningful rather than noise.<\/span><\/td>\n<\/tr>\n<tr>\n<td><b>Versioning<\/b><\/td>\n<td><span style=\"font-weight: 400\">Figures are as of July 2026. Performance shifts as new LLMs launch \u2014 re-run quarterly.<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><\/div><div class=\"pc-research-embed-actions\"><button type=\"button\" class=\"inline-flex justify-center items-center gap-8 px-12 py-4 m-0 rounded bg-B400 text-sm text-white font-semibold whitespace-nowrap transition-[background,outline] hover:bg-B500 focus:bg-B500 focus:outline focus:outline-2 focus:outline-B200 is-small\" data-copy-citation=\"Table: How we tested\nNote. AI Detection accuracy and method: evidence, numbers, and competitor comparison, by Samuel Lee, 2026, PlagiarismCheck Blog (https:\/\/plagiarismcheck.org\/blog\/ai-detection-accuracy-and-method-evidence-numbers-and-competitor-comparison\/). \u00a9 2026 by Samuel Lee. CC BY 4.0.\" title=\"Table: How we tested\nNote. AI Detection accuracy and method: evidence, numbers, and competitor comparison, by Samuel Lee, 2026, PlagiarismCheck Blog (https:\/\/plagiarismcheck.org\/blog\/ai-detection-accuracy-and-method-evidence-numbers-and-competitor-comparison\/). \u00a9 2026 by Samuel Lee. CC BY 4.0.\">Embed<\/button><\/div><\/section>\n<h2><span style=\"font-weight: 400;\">How this compares<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Most detectors on the market claim 98-99.98% accuracy. However, those numbers, including ours, are marketing figures, based on the vendors&#8217; own tests.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">What distinguishes our study is that we publish how many human-written content samples we tested, a metric none of the competitors disclose. This matters because AI presence in human-claimed texts distorts false-positive rate figures, making them look better than they are and reducing test precision.<\/span><\/p>\n<section class=\"pc-research-embed pc-research-table\"><h4 class=\"text-xl pc-research-h4\">Detection accuracy comparison<\/h4><div class=\"pc-research-table-scroll\" style=\"max-height:420px;\"><\/p>\n<table>\n<tbody>\n<tr>\n<td><b>Detector<\/b><\/td>\n<td><b>Claimed Accuracy<\/b><\/td>\n<td><b>Claimed False Positive Rate<\/b><\/td>\n<td><b>Human Sample Tested<\/b><\/td>\n<td><b>Source (Vendor Data)<\/b><\/td>\n<\/tr>\n<tr>\n<td><b>PlagiarismCheck.org<\/b><\/td>\n<td><span style=\"font-weight: 400\">99.23%<\/span><\/p>\n<p><span style=\"font-weight: 400\">recall, sentence<\/span><\/td>\n<td><span style=\"font-weight: 400\">0.00%<\/span><\/p>\n<p><span style=\"font-weight: 400\">document<\/span><\/td>\n<td><span style=\"font-weight: 400\">2,805 documents<\/span><\/p>\n<p><span style=\"font-weight: 400\">280,781 sentences<\/span><\/td>\n<td><span style=\"font-weight: 400\">This page<\/span><\/td>\n<\/tr>\n<tr>\n<td><b>Pangram<\/b><\/td>\n<td><span style=\"font-weight: 400\">99.98%<\/span><\/td>\n<td><span style=\"font-weight: 400\">~0.004%<\/span><\/td>\n<td><span style=\"font-weight: 400\">not published<\/span><\/td>\n<td><a href=\"https:\/\/www.pangram.com\/blog\/third-party-pangram-evals\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400\">pangram.com<\/span><\/a><\/td>\n<\/tr>\n<tr>\n<td><b>Originality.ai<\/b><\/td>\n<td><span style=\"font-weight: 400\">99% (Lite) \/ 99%+ (Turbo)<\/span><\/td>\n<td><span style=\"font-weight: 400\">0.5% \/ 1.5%<\/span><\/td>\n<td><span style=\"font-weight: 400\">not published<\/span><\/td>\n<td><a href=\"https:\/\/originality.ai\/blog\/ai-accuracy\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400\">originality.ai<\/span><\/a><\/td>\n<\/tr>\n<tr>\n<td><b>GPTZero<\/b><\/td>\n<td><span style=\"font-weight: 400\">99%<\/span><\/td>\n<td><span style=\"font-weight: 400\">&lt;1%<\/span><\/td>\n<td><span style=\"font-weight: 400\">not published<\/span><\/td>\n<td><a href=\"https:\/\/gptzero.me\/news\/gptzero-accuracy-stats\/\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400\">gptzero.me<\/span><\/a><\/td>\n<\/tr>\n<tr>\n<td><b>Copyleaks<\/b><\/td>\n<td><span style=\"font-weight: 400\">99%+<\/span><\/td>\n<td><span style=\"font-weight: 400\">&lt;0.2%<\/span><\/td>\n<td><span style=\"font-weight: 400\">not published<\/span><\/td>\n<td><a href=\"https:\/\/copyleaks.com\/blog\/ai-detector-continues-top-accuracy-third-party\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400\">copyleaks.com<\/span><\/a><\/td>\n<\/tr>\n<tr>\n<td><b>Turnitin<\/b><\/td>\n<td><span style=\"font-weight: 400\">98%<\/span><\/td>\n<td><span style=\"font-weight: 400\">&lt;1%<\/span><\/td>\n<td><span style=\"font-weight: 400\">not published<\/span><\/td>\n<td><a href=\"https:\/\/www.turnitin.com\/solutions\/topics\/ai-writing\/\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400\">turnitin.com<\/span><\/a><\/td>\n<\/tr>\n<tr>\n<td><b>Winston AI<\/b><\/td>\n<td><span style=\"font-weight: 400\">99.98%<\/span><\/td>\n<td><span style=\"font-weight: 400\">not stated<\/span><\/td>\n<td><span style=\"font-weight: 400\">not published<\/span><\/td>\n<td><a href=\"https:\/\/gowinston.ai\/setting-new-standards-in-ai-content-detection\/\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400\">gowinston.ai<\/span><\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><i><span style=\"font-weight: 400\">All figures retrieved in July, 2026. Independent studies frequently report different numbers from what vendors claim. ZeroGPT states 98% accuracy, while independent tests <\/span><\/i><a href=\"https:\/\/fast.io\/resources\/zerogpt-ai-detector-review-2026\/\" target=\"_blank\" rel=\"noopener\"><i><span style=\"font-weight: 400\">show<\/span><\/i><\/a><i><span style=\"font-weight: 400\"> 74%-80%.<\/span><\/i><\/p>\n<p><\/div><div class=\"pc-research-embed-actions\"><button type=\"button\" class=\"inline-flex justify-center items-center gap-8 px-12 py-4 m-0 rounded bg-B400 text-sm text-white font-semibold whitespace-nowrap transition-[background,outline] hover:bg-B500 focus:bg-B500 focus:outline focus:outline-2 focus:outline-B200 is-small\" data-copy-citation=\"Table: Detection accuracy comparison\nNote. AI Detection accuracy and method: evidence, numbers, and competitor comparison, by Samuel Lee, 2026, PlagiarismCheck Blog (https:\/\/plagiarismcheck.org\/blog\/ai-detection-accuracy-and-method-evidence-numbers-and-competitor-comparison\/). \u00a9 2026 by Samuel Lee. CC BY 4.0.\" title=\"Table: Detection accuracy comparison\nNote. AI Detection accuracy and method: evidence, numbers, and competitor comparison, by Samuel Lee, 2026, PlagiarismCheck Blog (https:\/\/plagiarismcheck.org\/blog\/ai-detection-accuracy-and-method-evidence-numbers-and-competitor-comparison\/). \u00a9 2026 by Samuel Lee. CC BY 4.0.\">Embed<\/button><\/div><\/section>\n<p><span style=\"font-weight: 400;\">Accuracy rate by itself is not representative unless it is backed by a research method and transparent study figures. This is why we publish our corpora, sample sizes, confidence intervals, and threshold curve.<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">What we haven&#8217;t tested yet<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">There are some gaps in the 2026 results we openly warn you about and plan to fill in the next tests.<\/span><\/p>\n<ul>\n<li><b>Paraphrased and humanized text.<\/b><span style=\"font-weight: 400;\"> All the figures above apply to unedited AI output. We have not yet published the data for text run through tools designed to disguise AI use, so we won&#8217;t claim parity with competitors until revealing the numbers.<\/span><\/li>\n<li><b>Hybrid content.<\/b><span style=\"font-weight: 400;\"> Mixed documents containing part human-written, part AI text are the most common real-world case and exactly what our sentence-level analysis is designed for. However, we haven&#8217;t benchmarked it yet, and this is the next test we run.<\/span><\/li>\n<li><b>Independent tests. <\/b><span style=\"font-weight: 400;\">All published figures are internal, not verified by independent study \u2013 yet. We are working with researchers to change it, and the offer below stays open.<\/span><\/li>\n<\/ul>\n<p><button type=\"button\" class=\"inline-flex justify-center items-center gap-8 px-12 py-4 m-0 rounded bg-B400 text-sm text-white font-semibold whitespace-nowrap transition-[background,outline] hover:bg-B500 focus:bg-B500 focus:outline focus:outline-2 focus:outline-B200 is-small\" data-copy-citation=\"Samuel Lee. (September 3, 2026). AI Detection accuracy and method: evidence, numbers, and competitor comparison. PlagiarismCheck Blog. https:\/\/plagiarismcheck.org\/blog\/ai-detection-accuracy-and-method-evidence-numbers-and-competitor-comparison\/\" title=\"Samuel Lee. (September 3, 2026). AI Detection accuracy and method: evidence, numbers, and competitor comparison. PlagiarismCheck Blog. https:\/\/plagiarismcheck.org\/blog\/ai-detection-accuracy-and-method-evidence-numbers-and-competitor-comparison\/\">Copy citation<\/button><\/li>\n<\/ul>\n<h2><span style=\"font-weight: 400;\">Free access for researchers<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">We invite researchers and independent evaluators to open testing. <a href=\"https:\/\/docs.google.com\/forms\/d\/e\/1FAIpQLSd6qOwejoLteZ7H1wqfs6gA_VmJJx2z0Tg-S-aTHRC9KlKU8Q\/viewform\" target=\"_blank\" rel=\"noopener\">Reach out to get free access<\/a> to our <a href=\"https:\/\/plagiarismcheck.org\/ai-detector\/\" target=\"_blank\" rel=\"noopener\">AI detector<\/a>, with no review of your results before publication.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"Unlike most popular AI detectors, our tool doesn&#8217;t produce a general document score. It checks every sentence, deciding the probability of it being AI-generated, and flags parts with AI traces in the report. How much AI presence is too much is up to you to judge. \u201c20% AI-generated\u201d result leaves you wondering whether it is [&hellip;]","protected":false},"author":19,"featured_media":30883,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[355],"tags":[],"plag_author":[385],"class_list":["post-30881","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog","plag_author-samuel-lee"],"acf":[],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One SEO 4.9.10 - aioseo.com -->\n\t<meta name=\"description\" content=\"How accurate is PlagiarismCheck.org AI detector and how it compares to competitors? 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