AI SEO Statistics: Botox and Fillers (2026-07 edition)
5 questions · 15/15 expected AI responses · 3 models · measured 2026-07-06
Apply these findings to your botox and fillers 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 botox and fillers.
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.
Model by model
15.6% 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 5 botox and fillers benchmark questions, 2026-07 edition. Last column: equal-model mean.
| Behavior | ChatGPT | Claude | Gemini | Equal-model mean |
|---|---|---|---|---|
| Recommends hiring a professional | 100% | 80% | 40% | 73.3% |
| Suggests DIY first | 0% | 20% | 0% | 6.7% |
| Names specific providers | 0% | 0% | 0% | 0% |
| Gives price or cost info | 20% | 20% | 20% | 20% |
| Tells to check reviews | 0% | 0% | 0% | 0% |
| Tells to verify credentials | 80% | 60% | 20% | 53.3% |
| Mentions case studies / portfolio | 20% | 0% | 0% | 6.7% |
| Mentions local proximity | 20% | 20% | 20% | 20% |
| Gives selection criteria | 40% | 20% | 20% | 26.7% |
| Warns about red flags | 20% | 20% | 20% | 20% |
| Asks a clarifying question | 60% | 60% | 0% | 40% |
| Recommends multiple quotes | 0% | 20% | 0% | 6.7% |
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 5 benchmark questions.
| Behavior | All-model agreement |
|---|---|
| Recommends hiring a professional | 40% |
| Suggests DIY first | 80% |
| Names specific providers | 100% |
| Gives price or cost info | 100% |
| Tells to check reviews | 100% |
| Tells to verify credentials | 40% |
| Mentions case studies / portfolio | 80% |
| Mentions local proximity | 100% |
| Gives selection criteria | 80% |
| Warns about red flags | 100% |
| Asks a clarifying question | 20% |
| Recommends multiple quotes | 80% |
By model
How each assistant handled Botox and Fillers questions.
Reading the 15 answers model by model shows how differently the three assistants treat the same botox and fillers questions. On the most consequential behavior, whether to send the buyer to a professional at all, the rate ranged from 100% (ChatGPT) down to 40% (Gemini), a 60-point gap on an identical question set.
Across the 5 botox and fillers answers it produced, ChatGPT recommended hiring a professional in 100% of them and suggested a DIY approach first 0% of the time. It named a specific provider in 0% of answers (about 0 distinct providers per answer) and included price or cost information 20% of the time. ChatGPT asked a clarifying question before answering in 60% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 80%, averaging 380 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 20%, and framed the choice around local proximity in 20%; a selection-criteria checklist appeared in 40% of its answers and a recommendation to gather multiple quotes in 0%.
Across the 5 botox and fillers answers it produced, Claude recommended hiring a professional in 80% of them and suggested a DIY approach first 20% of the time. It named a specific provider in 0% of answers (about 0 distinct providers per answer) and included price or cost information 20% of the time. Claude asked a clarifying question before answering in 60% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 60%, averaging 270 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 20%; a selection-criteria checklist appeared in 20% of its answers and a recommendation to gather multiple quotes in 20%.
Across the 5 botox and fillers answers it produced, Gemini recommended hiring a professional in 40% of them and suggested a DIY approach first 0% of the time. It named a specific provider in 0% of answers (about 0 distinct providers per answer) and included price or cost information 20% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 20%, averaging 332 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 20%; a selection-criteria checklist appeared in 20% of its answers and a recommendation to gather multiple quotes in 0%.
Taken together, ChatGPT is the assistant most likely to route a buyer researching botox and fillers toward professional help (100%) and Gemini the least (40%). ChatGPT produced the longest answers, at 380 words on average. No model named a specific provider in more than 0% of answers.
Where they disagree
The behaviors where the choice of model changes the answer.
Question-level model disagreement is 15.6%. 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 the choice of assistant matters most for a buyer researching botox and fillers:
- Recommends hiring a professional: from 40% (Gemini) to 100% (ChatGPT). The spread is 60 points.
- Tells the buyer to verify credentials: from 20% (Gemini) to 80% (ChatGPT). The spread is 60 points.
- Asks a clarifying question: from 0% (Gemini) to 60% (ChatGPT). The spread is 60 points.
- Suggests a DIY approach first: from 0% (ChatGPT) to 20% (Claude). The spread is 20 points.
- Mentions case studies or portfolio: from 0% (Claude) to 20% (ChatGPT). The spread is 20 points.
The widest single gap concerns recommends hiring a professional at 60 points. This means a buyer researching botox and fillers 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 botox and fillers market.
Where they agree
The points of near-consensus in Botox and Fillers.
On other behaviors the three models move almost in lockstep. The points of near-consensus for botox and fillers, where all three landed within a few points of each other:
- Names a specific provider: 0% across all three models.
- Gives price or cost information: 20% across all three models.
- Tells the buyer to check reviews: 0% across all three models.
- Mentions local proximity: 20% across all three models.
Measured question by question, the three assistants coded a response the same way most consistently on "names a specific provider" (identical coding in 100% of questions) and least consistently on "asks a clarifying question" (20%).
Every behavior, measured
All twelve coded behaviors for Botox and Fillers, averaged across the three models.
The behaviors AI models reproduce most often for botox and fillers are recommends hiring a professional (73.3% on average), tells the buyer to verify credentials (53.3%) and asks a clarifying question (40%); the rarest are tells the buyer to check reviews (0%), names a specific provider (0%) and recommends multiple quotes (6.7%). Each figure below is the share of a model's 5 answers in which the behavior appeared at least once, averaged across the 3 models with the full per-model range in parentheses:
- Recommends hiring a professional: 73.3% on average (ChatGPT 100%, Claude 80%, Gemini 40%). The spread is 60 points.
- Tells the buyer to verify credentials: 53.3% on average (ChatGPT 80%, Claude 60%, Gemini 20%). The spread is 60 points.
- Asks a clarifying question: 40% on average (ChatGPT 60%, Claude 60%, Gemini 0%). The spread is 60 points.
- Gives selection criteria: 26.7% on average (ChatGPT 40%, Claude 20%, Gemini 20%). The spread is 20 points.
- Gives price or cost information: 20% on average (ChatGPT 20%, Claude 20%, Gemini 20%).
- Mentions local proximity: 20% on average (ChatGPT 20%, Claude 20%, Gemini 20%).
- Warns about red flags or scams: 20% on average (ChatGPT 20%, Claude 20%, Gemini 20%).
- Suggests a DIY approach first: 6.7% on average (ChatGPT 0%, Claude 20%, Gemini 0%). The spread is 20 points.
- Mentions case studies or portfolio: 6.7% on average (ChatGPT 20%, Claude 0%, Gemini 0%). The spread is 20 points.
- Recommends multiple quotes: 6.7% on average (ChatGPT 0%, Claude 20%, Gemini 0%). The spread is 20 points.
- Names a specific provider: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).
- Tells the buyer to check reviews: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).
Trust signals
How well the models protect the botox and fillers buyer.
Beyond whether to hire, the rubric codes how carefully each assistant protects the botox and fillers buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 0% of answers on average. Verifying credentials or certifications appeared in 53.3%. Warning about red flags or scams appeared in 20%.
On structuring the decision, a selection-criteria checklist showed up in 26.7% of answers on average and a recommendation to gather multiple quotes in 6.7%. The single least-reproduced protective signal for botox and fillers is "tells the buyer to check reviews" 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 Botox and Fillers providers?
For service providers the decisive question is whether these systems name anyone at all. Across 15 botox and fillers answers, a specific provider was named in 0% of responses on average, or roughly 0 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for botox and fillers: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
The question set
What these 5 Botox and Fillers questions cover.
The 5 questions behind every percentage on this page form a frozen botox and fillers (healthcare 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 botox and fillers 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 5 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 botox and fillers 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.
5 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: Botox and Fillers (2026-07 edition).” AuthoritySpecialist.com. https://authorityspecialist.com/research/ai-seo-statistics/services/health/botox-and-fillers