AI SEO Statistics: Adult Dating Websites (2026-07 edition)
40 questions · 120/120 expected AI responses · 3 models · measured 2026-07-06
Apply these findings to your adult dating websites SEO strategy.
This benchmark explains how AI assistants advise buyers. The related service page turns those findings into the technical, content, authority, and conversion priorities for this market.
The question bank
The questions we tested: a frozen buyer-intent benchmark for adult dating websites.
The question set was curated from a predefined buyer-intent taxonomy and held constant for this edition. Each model received the same wording. These are the prompts behind every percentage on this page.
Show all 40 questions
Model by model
24.2% question-level model disagreement.
This rate is the average pairwise disagreement between binary behavior codes across questions and behaviors. It is not the gap between the highest and lowest aggregated model percentages.
Behavior prevalence across 40 adult dating websites benchmark questions, 2026-07 edition. Last column: equal-model mean.
| Behavior | ChatGPT | Claude | Gemini | Equal-model mean |
|---|---|---|---|---|
| Recommends hiring a professional | 20% | 7.5% | 7.5% | 11.7% |
| Suggests DIY first | 32.5% | 15% | 5% | 17.5% |
| Names specific providers | 32.5% | 50% | 72.5% | 51.7% |
| Gives price or cost info | 10% | 7.5% | 15% | 10.8% |
| Tells to check reviews | 12.5% | 42.5% | 5% | 20% |
| Tells to verify credentials | 5% | 0% | 0% | 1.7% |
| Mentions case studies / portfolio | 5% | 2.5% | 0% | 2.5% |
| Mentions local proximity | 27.5% | 25% | 20% | 24.2% |
| Gives selection criteria | 45% | 57.5% | 30% | 44.2% |
| Warns about red flags | 37.5% | 50% | 22.5% | 36.7% |
| Asks a clarifying question | 60% | 80% | 5% | 48.3% |
| Recommends multiple quotes | 0% | 0% | 0% | 0% |
Question-level agreement
How often all measured models received the same binary code.
Agreement is calculated question by question for each behavior. A high value can coexist with a low behavior prevalence; it means the models usually agreed on whether the behavior appeared.
All-model binary agreement by behavior across 40 benchmark questions.
| Behavior | All-model agreement |
|---|---|
| Recommends hiring a professional | 77.5% |
| Suggests DIY first | 62.5% |
| Names specific providers | 32.5% |
| Gives price or cost info | 82.5% |
| Tells to check reviews | 57.5% |
| Tells to verify credentials | 95% |
| Mentions case studies / portfolio | 95% |
| Mentions local proximity | 55% |
| Gives selection criteria | 42.5% |
| Warns about red flags | 55% |
| Asks a clarifying question | 10% |
| Recommends multiple quotes | 100% |
By model
How each assistant handled Adult Dating Websites questions.
Reading the 120 answers model by model shows how differently the three assistants treat the same adult dating websites questions. On the most consequential behavior, whether to send the buyer to a professional at all, the rate ranged from 20% (ChatGPT) down to 7.5% (Claude), a 13-point gap on an identical question set.
Across the 40 adult dating websites answers it produced, ChatGPT recommended hiring a professional in 20% of them and suggested a DIY approach first 32.5% of the time. It named a specific provider in 32.5% of answers (about 2.1 distinct providers per answer) and included price or cost information 10% of the time. ChatGPT asked a clarifying question before answering in 60% of cases, warned about red flags or scams in 37.5%, and told the buyer to verify credentials in 5%, averaging 459 words per answer. On the remaining cues it told the buyer to check reviews in 12.5%, pointed to case studies or a portfolio in 5%, and framed the choice around local proximity in 27.5%; a selection-criteria checklist appeared in 45% of its answers and a recommendation to gather multiple quotes in 0%.
Across the 40 adult dating websites answers it produced, Claude recommended hiring a professional in 7.5% of them and suggested a DIY approach first 15% of the time. It named a specific provider in 50% of answers (about 2.8 distinct providers per answer) and included price or cost information 7.5% of the time. Claude asked a clarifying question before answering in 80% of cases, warned about red flags or scams in 50%, and told the buyer to verify credentials in 0%, averaging 250 words per answer. On the remaining cues it told the buyer to check reviews in 42.5%, pointed to case studies or a portfolio in 2.5%, and framed the choice around local proximity in 25%; a selection-criteria checklist appeared in 57.5% of its answers and a recommendation to gather multiple quotes in 0%.
Across the 40 adult dating websites answers it produced, Gemini recommended hiring a professional in 7.5% of them and suggested a DIY approach first 5% of the time. It named a specific provider in 72.5% of answers (about 2.9 distinct providers per answer) and included price or cost information 15% of the time. Gemini asked a clarifying question before answering in 5% of cases, warned about red flags or scams in 22.5%, and told the buyer to verify credentials in 0%, averaging 246 words per answer. On the remaining cues it told the buyer to check reviews in 5%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 20%; a selection-criteria checklist appeared in 30% of its answers and a recommendation to gather multiple quotes in 0%.
Taken together, ChatGPT is the assistant most likely to route an adult dating websites buyer to a professional (20%) and Claude the least (7.5%). ChatGPT produced the longest answers, at 459 words on average. Specific providers were named most often by Gemini (72.5%). Even there, roughly one answer in 1 carried a name.
Where they disagree
The behaviors where the choice of model changes the answer.
Question-level model disagreement is 24.2%. This is the average pairwise rate at which models received different binary codes across questions and behaviors. The observed rate spreads below are a separate measure showing where which assistant an adult dating websites buyer happens to ask matters most:
- Asks a clarifying question: from 5% (Gemini) to 80% (Claude). The spread is 75 points.
- Names a specific provider: from 32.5% (ChatGPT) to 72.5% (Gemini). The spread is 40 points.
- Tells the buyer to check reviews: from 5% (Gemini) to 42.5% (Claude). The spread is 38 points.
- Suggests a DIY approach first: from 5% (Gemini) to 32.5% (ChatGPT). The spread is 28 points.
- Gives selection criteria: from 30% (Gemini) to 57.5% (Claude). The spread is 28 points.
The widest single gap concerns asks a clarifying question at 75 points. This means an adult dating websites buyer can receive materially different guidance on the same question depending only on which assistant they happen to open, so any visibility strategy built on a single model's behavior describes only part of the adult dating websites market.
Where they agree
The points of near-consensus in Adult Dating Websites.
On other behaviors the three models move almost in lockstep. The points of near-consensus for adult dating websites, where all three landed within a few points of each other:
- Recommends multiple quotes: 0% across all three models.
- Tells the buyer to verify credentials: 0%–5% across all three (a 5-point spread).
- Mentions case studies or portfolio: 0%–5% across all three (a 5-point spread).
- Gives price or cost information: 7.5%–15% across all three (a 8-point spread).
Measured question by question, the three assistants coded a response the same way most consistently on "recommends multiple quotes" (identical coding in 100% of questions) and least consistently on "asks a clarifying question" (10%).
Every behavior, measured
All twelve coded behaviors for Adult Dating Websites, averaged across the three models.
The behaviors AI models reproduce most often for adult dating websites are names a specific provider (51.7% on average), asks a clarifying question (48.3%) and gives selection criteria (44.2%); the rarest are recommends multiple quotes (0%), tells the buyer to verify credentials (1.7%) and mentions case studies or portfolio (2.5%). Each figure below is the share of a model's 40 answers in which the behavior appeared at least once, averaged across the 3 models with the full per-model range in parentheses:
- Names a specific provider: 51.7% on average (ChatGPT 32.5%, Claude 50%, Gemini 72.5%). The spread is 40 points.
- Asks a clarifying question: 48.3% on average (ChatGPT 60%, Claude 80%, Gemini 5%). The spread is 75 points.
- Gives selection criteria: 44.2% on average (ChatGPT 45%, Claude 57.5%, Gemini 30%). The spread is 28 points.
- Warns about red flags or scams: 36.7% on average (ChatGPT 37.5%, Claude 50%, Gemini 22.5%). The spread is 28 points.
- Mentions local proximity: 24.2% on average (ChatGPT 27.5%, Claude 25%, Gemini 20%). The spread is 8 points.
- Tells the buyer to check reviews: 20% on average (ChatGPT 12.5%, Claude 42.5%, Gemini 5%). The spread is 38 points.
- Suggests a DIY approach first: 17.5% on average (ChatGPT 32.5%, Claude 15%, Gemini 5%). The spread is 28 points.
- Recommends hiring a professional: 11.7% on average (ChatGPT 20%, Claude 7.5%, Gemini 7.5%). The spread is 13 points.
- Gives price or cost information: 10.8% on average (ChatGPT 10%, Claude 7.5%, Gemini 15%). The spread is 8 points.
- Mentions case studies or portfolio: 2.5% on average (ChatGPT 5%, Claude 2.5%, Gemini 0%). The spread is 5 points.
- Tells the buyer to verify credentials: 1.7% on average (ChatGPT 5%, Claude 0%, Gemini 0%). The spread is 5 points.
- Recommends multiple quotes: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).
Trust signals
How well the models protect the adult dating websites buyer.
Beyond whether to hire, the rubric codes how carefully each assistant protects the adult dating websites buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 20% of answers on average. Verifying credentials or certifications appeared in 1.7%. Warning about red flags or scams appeared in 36.7%.
On structuring the decision, a selection-criteria checklist showed up in 44.2% of answers on average and a recommendation to gather multiple quotes in 0%. The single least-reproduced protective signal for adult dating websites is "recommends multiple quotes" at 0% on average. This is the clearest opening for content that supplies it, since the models are not yet reliably surfacing that guidance on their own.
Referral behavior
Do AI models name Adult Dating Websites providers?
For service providers the decisive question is whether these systems name anyone at all. Across 120 adult dating websites answers, a specific provider was named in 51.7% of responses on average, or roughly 2.6 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for adult dating websites: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
The question set
What these 40 Adult Dating Websites questions cover.
The 40 questions behind every percentage on this page form a frozen adult dating websites (professional services; buyer hiring decisions for this specific service) buyer-intent benchmark. Each was put to all 3 models once, with identical wording, so the rates above describe how the assistants handled this exact adult dating websites question set rather than a general prior or a hand-picked subset. The full list is shown earlier on this page; the coded percentages are what those specific questions produced.
How to read this
A note on the numbers.
A percentage here is the share of a model's 40 answers in which the behavior appeared at least once. It is not a confidence score. Because each model answered every question exactly once on 2026-07-06, the figures describe this specific adult dating websites question set and snapshot rather than a general prior. The full protocol and coding rubric are documented in the study methodology.
Methodology
A controlled snapshot, documented end to end.
40 frozen benchmark questions, one expected response per model per question (ChatGPT API (gpt-5-mini), Claude API (claude-sonnet-5), Gemini API (gemini-3-flash-preview)), collected 2026-07-06 and coded against a fixed 12-behavior rubric. The pipeline validates the schema, recomputes aggregates and reports consistency issues. AI outputs vary with model version, location and time, so the figures describe this edition's exact sample and measurement window. Read the full methodology →
Citation
Cite this edition.
Authority Specialist. “AI SEO Statistics: Adult Dating Websites (2026-07 edition).” AuthoritySpecialist.com. https://authorityspecialist.com/research/ai-seo-statistics/services/professional/adult-dating-websites