GPT-4 is the most widely used large language model on the planet, which means it leaves the most training data, the most fingerprints, and the easiest patterns to detect. TextSight's classifier was trained on millions of GPT-4, GPT-4o, and ChatGPT outputs so it catches the polite-assistant register, the nested-clause syntax, and the "thoughtful synthesis" closers that other detectors miss. Free to try, no card, your first scan in about six seconds.
More than three quarters of public AI text in 2026 originates from the GPT-4 family. A generic detector misses the patterns that matter; a GPT-tuned classifier picks them up at the sentence level.
GPT-4 launched in March 2023, GPT-4o (the multimodal variant) in May 2024, GPT-5 in late 2025. Despite the version jumps, the GPT-4 family shares a coherent stylistic fingerprint that is distinct from earlier ChatGPT (GPT-3.5) and from competing models like Claude, Gemini, and Llama. That fingerprint is what TextSight scores against.
GPT-4 reads less templated than GPT-3.5. Paragraphs do not always open with "Firstly" or "Moreover", conclusions are not always announced with "In conclusion", and the rigid five-paragraph default has softened. To the casual eye, GPT-4 text is harder to distinguish from human writing than GPT-3.5 was. To a classifier looking at sentence-length distributions, hedging frequency, and macrostructure, the fingerprint is still loud.
ChatGPT defaults to a helpful-assistant voice that ships with stock openers: "Certainly!", "Of course!", "I would be happy to help.", "Great question!". Even when those openers are stripped, the underlying register persists. Sentences hedge uniformly, qualifications stack ("which, while important, often results in..."), and the closing paragraph almost always steps back to synthesise rather than ending on a specific claim.
ChatGPT, OpenAI Playground, and direct API calls all run on GPT-4-family weights, just with different system prompts and temperatures. ChatGPT's default voice is the most uniform; Playground output with temperature 1.2 sounds looser; API calls with custom system prompts ("write in casual blogger voice") soften the surface. TextSight scores the underlying fingerprint, not the surface polish, which is why custom-prompted GPT-4 still flags.
Five signals carry most of the weight in TextSight's GPT-4 classifier. They survive light edits, light prompt engineering, and even moderate fine-tuning.
GPT-4 leaned hard into specific words during 2023-24 RLHF training: intricate, tapestry, navigate (as metaphor), multifaceted, robust, delve, leverage, underscore, foster. These appear in topic sentences and conclusions at roughly five to seven times the rate of human writing on equivalent topics.
"Certainly!", "Of course!", "I would be happy to help.", "Great question!", "Absolutely!". Even when these are deleted, the second-sentence pattern often gives it away: a confident restatement of the prompt followed by an outline of what the answer will cover. Humans usually start with the answer.
"This approach, while elegant, often results in..." and "The method, which builds on prior work, demonstrates..." Humans use this construction occasionally. GPT-4 uses it almost every paragraph. The density itself, more than any single instance, is the signal.
GPT-4 rarely produces sentences under 12 words. Human writers regularly drop to 5 to 8 word sentences for emphasis ("It worked." "Here is why.") A passage of 300 plus words with no short sentences is a strong GPT-4 signal independent of any vocabulary or structural tells.
GPT-4's closing paragraph almost always steps back and synthesises themes rather than ending on a specific claim. "As we move forward, the interplay between..." or "Ultimately, the path forward demands..." Closing sentences with this synthesis pattern, especially with metaphor vocabulary (path forward, journey, landscape, tapestry), score as GPT-4 at roughly 85 percent probability in TextSight's internal classification.
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A model-tuned classifier trained on the largest sample we have, with weighted signals and per-sentence scoring so you see exactly which lines triggered the flag.
The training set spans essays, blog posts, emails, product descriptions, scripts, marketing copy, and technical documentation. It includes raw GPT-4, GPT-4 with system prompts encouraging different styles, GPT-4o multimodal text output, and a growing GPT-5 sample. The volume is why TextSight's GPT-4 accuracy beats generic multi-model detectors by 5 to 10 points on the GPT-4 family specifically.
Structural signals (sentence-length floor, nested-clause density, burstiness) weight roughly 40 percent of the score. Vocabulary signals (the tapestry / navigate / delve cluster) weight 30 percent. Macrostructure (the closing-synthesis pattern, paragraph templating) weights 20 percent. Punctuation and hedging weight 10 percent. The weights are tuned quarterly against fresh GPT-4 samples.
The classifier runs at both levels. Each sentence gets a per-sentence probability score, which produces the green / yellow / red colour map you see in the UI. The document-level Authenticity Score is the weighted aggregate, with longer windows getting higher weight. Short passages under 300 words are flagged as directional rather than precise.
Around 90 percent on long-form GPT-4 text (500 plus words), 75 to 82 percent on shorter passages, 70 to 80 percent on heavily fine-tuned GPT-4. False positive rate sits at 1 to 2 percent on native English and 4 to 6 percent on ESL writing. TextSight publishes per-model accuracy rather than a single aggregate number because a "98% accurate" headline across all models hides which models the tool is actually good at.
GPT-4 is the model most submissions, articles, and emails ride on. These are the workflows where catching it has measurable payoff.
GPT-4 is the model students reach for first in 2026. Knowing the specific GPT-4 fingerprint helps teachers distinguish raw GPT-4 submissions from heavily-edited drafts that started with GPT-4 outlines. Sentence-level flags showing the "intricate tapestry" vocabulary or the synthesis-paragraph pattern are stronger evidence than a single percentage.
Content agencies and publishing teams hire freelancers who often use GPT-4 as an outline or first-draft tool. Knowing what unedited GPT-4 looks like helps editors push back constructively ("This paragraph reads like a first draft, not your final copy") rather than make blanket "no AI" demands that are not enforceable.
Most SME content workflows use GPT-4 for outline drafts, then rewrite. Detecting GPT-4 patterns in published articles helps the team identify articles that did not get enough authenticity before going live, before Google's helpful-content classifier finds them first.
GPT-4 cover letters share the same tells listed above and recruiters in 2025-26 have learned to recognise them on sight. A high GPT-4 score on a cover letter does not bin the applicant, but it does tell the recruiter to weight the resume and interview signals more heavily than the prose.
A small but growing use case: maintainers of large open-source projects checking whether pull-request descriptions look auto-generated. GPT-4 cover-style PR text reads differently from genuine contributor explanations, and a quick scan catches it before review time gets spent on a low-effort submission.
All numbers are on long-form text (500 plus words) from TextSight's internal benchmark, retrained quarterly as model families evolve.
Average sentence length 16 to 22 words and flat. Voice is rigid, templated, and transition-heavy. Detection accuracy 95 plus percent because the structural defaults are loud and detectors have had years to learn them.
Average sentence length 22 to 26 words with slight variance. Voice is institutional, uniform, nested-clause heavy. Detection accuracy around 90 percent. The bulk of detectable AI text in 2026 sits here.
Average sentence length 20 to 28 words with more variance than GPT-4. Voice is similar to GPT-4o with softer hedging and slightly looser structure. Detection accuracy 85 to 90 percent and rising as the training sample grows.
Average sentence length 14 to 22 words and varied. Voice is conversational, first-person, with more personality than any GPT variant. Detection accuracy around 88 percent. The detector relies more on vocabulary and less on structure for Claude.
Gemini runs list-heavy and bulleted with 18 to 24 word sentences (around 87 percent). Llama 3 is looser with 14 to 30 word sentence spread and more grammatical variance (around 82 percent). Both are smaller slices of public AI text than the GPT-4 family.
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See pricing →Free to try, no card, your first scan in about six seconds. Around 90 percent accuracy on long-form GPT-4 text with sentence-level highlights.