Built to detect Google Gemini output across 1.5 Pro, 1.5 Flash, Ultra, and Gemini in Workspace. Sentence-level highlights surface the bullet-heavy, citation-style, disclaimer-padded prose that legacy detectors quietly let through. Multi-model classifier with substantial Gemini training data, not a GPT detector with Gemini bolted on. Free to try. No card.
Three structural patterns separate Gemini-family text from GPT-4 and Claude output, and those same patterns are what legacy GPT-tuned detectors quietly miss.
Gemini is now everywhere a Google user already lives. The gemini.google.com surface, the Help me write button inside Google Docs and Gmail, formula explanations inside Sheets, speaker-note suggestions inside Slides, the system Assistant on Pixel devices, and AI Overviews in Search itself. The same underlying Gemini family powers every one of those surfaces, which means the same stylistic fingerprints show up in any submitted text.
Gemini formats by default. Most outputs of any length include markdown headers, sub-headers, and at least one bullet list. Asked for a paragraph, Gemini frequently returns a paragraph plus three supporting bullets. Asked for a short answer, it returns numbered points. Density of structural markdown is the single strongest Gemini signal and survives a paste into a rich-text editor as visible bullet runs the writer rarely strips.
Gemini was built next to Search and that lineage shows in the prose. Outputs read like a featured snippet expanded into a paragraph: short topic sentence, three balanced supporting claims, a closing sentence that paraphrases the topic sentence. Trailing citation markers (a superscript 1, a bracketed [1], an according-to-X attribution) appear even when the user did not ask for sources, and they often dangle without a corresponding footnote because the writer pasted prose without the reference list.
Gemini appends boilerplate disclaimers far more readily than Claude or GPT-4, especially on health, finance, and legal topics. Phrases like "please consult a qualified professional", "this information is for educational purposes only", and "always speak to an expert before acting on this" land at the end of paragraphs by reflex. The connective adverbs ("furthermore", "moreover", "it is worth noting", "in addition to the above") arrive at a noticeably higher rate than in human writing. A cluster of connectives in a short passage is a strong flag. For the mechanics behind how a classifier reads these signals, see how AI detectors work.
Naming the entry points helps reviewers understand where Gemini prose enters a workflow, even when the writer never mentions Gemini directly.
The direct chat surface (formerly bard.google.com) and the dedicated Gemini app on Android and iOS. Free users land on Gemini 1.5 Flash, paid Advanced subscribers on Gemini 1.5 Pro and Ultra. Most cut-and-paste Gemini prose still originates here, and the markdown formatting is at its heaviest because the surface renders rich markdown by default.
Help me write inside Docs, draft assist inside Gmail, formula explainers inside Sheets, and speaker-note suggestions inside Slides. This is the surface that is hardest to spot socially because the writer never leaves the document they are already editing. Workspace Gemini still produces the bullet-heavy, citation-flavoured prose, just dropped directly into a Doc paragraph or an email body.
Pixel devices ship Gemini as the system Assistant. Long-press the power button on a Pixel 8 or 9 and the response that comes back is Gemini-shaped. Circle to Search summaries sit on the same model. Dictated drafts that pass through these surfaces inherit the Gemini cadence even when the user dictated in their own words.
AI Overviews extract from indexed web pages and wrap them in Gemini connective prose. The quoted spans inside an Overview read human because they came from human sources, but the framing and the synthesis read as Gemini. The classifier weighs the connective and structural prose more heavily than the quoted spans, so AI Overview content scans clearly when pasted as a block.
Pro at $19.99 a month standard, $14.99 a month on yearly, is the right fit for solo writers, teachers, and reviewers. Business at $39.99 a month standard, $29.99 a month on yearly, fits agency and content teams running 50-plus deliverables a month. Gemini coverage is included at every tier. Full details on the pricing page.
Billed $89.88/year — Save $30
Billed $179.88/year — Save $60
Billed $359.88/year — Save $120
Yearly billing saves 25%. View full pricing →
Five markers do most of the lifting on Gemini-family text. Bullet density, numbered enumerations, markdown bold runs, the disclaimer reflex, and the SERP-summary cadence. Each carries weight independently and the combinations are what push a Gemini score into the high band.
Gemini answers in shapes. Most outputs above 150 words include at least one bullet list, often two. Even prose paragraphs frequently end on a colon and a three-item bullet block. The bullets survive a paste into Google Docs or a brand CMS as visible runs that the writer rarely cleans up. Bullet density per 400 words is the single strongest Gemini signal in the multi-model feature space.
"There are four main reasons", "five key strategies", "three important considerations to keep in mind". Gemini opens paragraphs with counted enumerations where GPT-4 prefers flowing claims and Claude prefers a hedged single sentence. Counted enumerations per passage run well above the human baseline on Gemini-family outputs, and the classifier weights that frequency heavily.
Trailing superscripts, bracketed numerals, and according-to-X attributions appear even when the user did not ask for sources. The reference markers frequently dangle because the writer pasted the prose without the source block at the bottom. A dangling citation marker is a near-certain Gemini fingerprint and pairs with the SERP cadence as a structural give-away.
"Please consult a qualified professional". "This information is for educational purposes only". "Always speak to a licensed expert before acting on this". Gemini closes paragraphs and documents with these boilerplate disclaimers far more readily than Claude or GPT-4, particularly on regulated topics. A trailing disclaimer paragraph is a near-certain flag.
Search-grounded outputs read like a featured snippet expanded into a paragraph. Short topic sentence, three balanced supporting claims, a concluding sentence that paraphrases the topic sentence back at the reader. This shape is rare in human essay writing and common in Gemini on factual prompts, especially when the model browses behind the scenes to ground its answer.
Five workflows where a GPT-tuned detector quietly lets Gemini through and where a Gemini-aware classifier earns its keep.
Schools and universities running Workspace for Education give every student a Docs sidebar with Help me write powered by Gemini. A teacher using a GPT-tuned detector sees a green score; the same essay through a Gemini-aware tool flags the numbered enumerations, the bullet runs, and the trailing disclaimer paragraph. Sentence-level highlights give the teacher specific lines to discuss in a one-on-one rather than a vague "this feels AI" verdict.
Many freelancers have moved to Gemini because Help me write lives inside the Doc they are already editing. Agencies running drafts through legacy GPT-tuned detectors see Gemini come back clean and assume the work was human. A Gemini-aware classifier identifies the SERP-summary cadence and connective boilerplate that survive light editing and produce a defensible scan record before payment release.
Gemini is widely used to draft cover letters and short writing samples precisely because it lives inside Gmail and Docs. Recruiters scanning submissions catch Gemini-drafted samples and weight the resume over the prose. A high Gemini score does not bin the candidate; it tells the recruiter what the writing sample actually demonstrates about the candidate's voice.
Editors at publications, newsletter platforms, and tech blogs see Gemini in submitted pitches and first drafts. Knowing the bullet density, numbered enumerations, and disclaimer reflex are Gemini markers lets an editor give targeted feedback ("the second paragraph reads like a featured snippet, not your voice") instead of an unenforceable blanket no-AI policy.
Some regulated industries (legal, finance, healthcare) restrict AI use in client-facing documents. Compliance teams using a Gemini-aware detector catch Gemini-drafted documents that GPT-tuned tools miss, particularly where staff use Gmail or Docs Help me write throughout the workday and never label the output as AI-assisted.
The clearest way to see the training-distribution problem is to take cold Gemini outputs and run them through several detectors side by side. Legacy GPT-tuned tools tend to read the same Gemini text as mostly human, where a Gemini-aware classifier flags it. Here is why the gap shows up. For a wider field comparison, see our best AI detector 2026 roundup.
Bullet density, numbered enumeration frequency, markdown bold runs, connective adverb rate, the disclaimer reflex, the SERP cadence, and dangling citation markers all carry weight independently of the GPT-flavoured features in the same classifier. Long-form Gemini text gives all of those markers room to fire, which is where a Gemini-aware classifier is strongest. The multi-model training set spans Bard, Gemini 1.0, 1.5 Pro, 1.5 Flash, and Ultra so the feature extractors stay honest across the family.
Legacy detectors trained primarily on GPT samples routinely score Gemini long-form as mostly human. GPTZero's classifier is tuned on GPT distribution, and Gemini's search-grounded, outline-heavy text does not light up the same features. Neither legacy tool is broken on what it was built for; both were simply trained on a different distribution than Gemini sits in, so the outline-and-disclaimer shape slides past them.
Under a couple of hundred words there is less material and fewer markers fire, so every detector gets less reliable on short Gemini snippets, TextSight included. Short submissions are directional, not definitive. The 5-band Authenticity Score tells a reviewer which short results to treat as borderline rather than dressing them up as verdicts.
A document with a borderline overall score still surfaces which specific sentences read as Gemini (yellow or red) and which read as human (green). Reviewers see the evidence rather than just a number. This matters most on lightly-edited Gemini drafts where the headline score sits in the borderline band but the structural fingerprints survive every line.
Model-tuned classifier for Anthropic Claude output, including Sonnet, Opus, and Haiku.
Claude detector →Model-tuned classifier for OpenAI GPT-4, GPT-4o, and GPT-5 output across long-form prose.
GPT-4 detector →Rewrite Gemini-flagged drafts into prose that clears classifier review without breaking voice.
Open AI rewriter →Multi-model detection across GPT, Claude, Gemini, and Llama with sentence-level highlights.
Open detector →Free to try. No card. Built to catch Gemini 1.5 Pro, Flash, Ultra, and Workspace prose that legacy detectors miss.
How TextSight fits other teams and workflows.