AI SEO Statistics: Non Invasive Fat Reduction (2026-07 edition)
37 questions · 111/111 expected AI responses · 3 models · measured 2026-07-06
Apply these findings to your non invasive fat reduction 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 non invasive fat reduction.
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 37 questions
Model by model
20.3% 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 37 non invasive fat reduction benchmark questions, 2026-07 edition. Last column: equal-model mean.
| Behavior | ChatGPT | Claude | Gemini | Equal-model mean |
|---|---|---|---|---|
| Recommends hiring a professional | 75.7% | 67.6% | 24.3% | 55.9% |
| Suggests DIY first | 5.4% | 5.4% | 2.7% | 4.5% |
| Names specific providers | 0% | 0% | 5.4% | 1.8% |
| Gives price or cost info | 8.1% | 2.7% | 2.7% | 4.5% |
| Tells to check reviews | 10.8% | 8.1% | 0% | 6.3% |
| Tells to verify credentials | 40.5% | 37.8% | 5.4% | 27.9% |
| Mentions case studies / portfolio | 35.1% | 13.5% | 5.4% | 18% |
| Mentions local proximity | 10.8% | 5.4% | 0% | 5.4% |
| Gives selection criteria | 43.2% | 35.1% | 18.9% | 32.4% |
| Warns about red flags | 8.1% | 27% | 10.8% | 15.3% |
| Asks a clarifying question | 64.9% | 73% | 5.4% | 47.8% |
| Recommends multiple quotes | 2.7% | 8.1% | 0% | 3.6% |
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 37 benchmark questions.
| Behavior | All-model agreement |
|---|---|
| Recommends hiring a professional | 40.5% |
| Suggests DIY first | 89.2% |
| Names specific providers | 94.6% |
| Gives price or cost info | 89.2% |
| Tells to check reviews | 86.5% |
| Tells to verify credentials | 48.6% |
| Mentions case studies / portfolio | 62.2% |
| Mentions local proximity | 86.5% |
| Gives selection criteria | 54.1% |
| Warns about red flags | 75.7% |
| Asks a clarifying question | 18.9% |
| Recommends multiple quotes | 89.2% |
By model
How each assistant handled Non Invasive Fat Reduction questions.
Reading the 111 answers model by model shows how differently the three assistants treat the same non invasive fat reduction questions. On the most consequential behavior, whether to send the buyer to a professional at all, the rate ranged from 75.7% (ChatGPT) down to 24.3% (Gemini), a 51-point gap on an identical question set.
Across the 37 non invasive fat reduction answers it produced, ChatGPT recommended hiring a professional in 75.7% of them and suggested a DIY approach first 5.4% of the time. It named a specific provider in 0% of answers (about 0 distinct providers per answer) and included price or cost information 8.1% of the time. ChatGPT asked a clarifying question before answering in 64.9% of cases, warned about red flags or scams in 8.1%, and told the buyer to verify credentials in 40.5%, averaging 455 words per answer. On the remaining cues it told the buyer to check reviews in 10.8%, pointed to case studies or a portfolio in 35.1%, and framed the choice around local proximity in 10.8%; a selection-criteria checklist appeared in 43.2% of its answers and a recommendation to gather multiple quotes in 2.7%.
Across the 37 non invasive fat reduction answers it produced, Claude recommended hiring a professional in 67.6% of them and suggested a DIY approach first 5.4% of the time. It named a specific provider in 0% of answers (about 0 distinct providers per answer) and included price or cost information 2.7% of the time. Claude asked a clarifying question before answering in 73% of cases, warned about red flags or scams in 27%, and told the buyer to verify credentials in 37.8%, averaging 275 words per answer. On the remaining cues it told the buyer to check reviews in 8.1%, pointed to case studies or a portfolio in 13.5%, and framed the choice around local proximity in 5.4%; a selection-criteria checklist appeared in 35.1% of its answers and a recommendation to gather multiple quotes in 8.1%.
Across the 37 non invasive fat reduction answers it produced, Gemini recommended hiring a professional in 24.3% of them and suggested a DIY approach first 2.7% of the time. It named a specific provider in 5.4% of answers (about 0.1 distinct providers per answer) and included price or cost information 2.7% of the time. Gemini asked a clarifying question before answering in 5.4% of cases, warned about red flags or scams in 10.8%, and told the buyer to verify credentials in 5.4%, averaging 259 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 5.4%, and framed the choice around local proximity in 0%; a selection-criteria checklist appeared in 18.9% 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 non invasive fat reduction toward professional help (75.7%) and Gemini the least (24.3%). ChatGPT produced the longest answers, at 455 words on average. Specific providers were named most often by Gemini (5.4%). Even there, roughly one answer in 19 carried a name.
Where they disagree
The behaviors where the choice of model changes the answer.
Question-level model disagreement is 20.3%. 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 non invasive fat reduction:
- Asks a clarifying question: from 5.4% (Gemini) to 73% (Claude). The spread is 68 points.
- Recommends hiring a professional: from 24.3% (Gemini) to 75.7% (ChatGPT). The spread is 51 points.
- Tells the buyer to verify credentials: from 5.4% (Gemini) to 40.5% (ChatGPT). The spread is 35 points.
- Mentions case studies or portfolio: from 5.4% (Gemini) to 35.1% (ChatGPT). The spread is 30 points.
- Gives selection criteria: from 18.9% (Gemini) to 43.2% (ChatGPT). The spread is 24 points.
The widest single gap concerns asks a clarifying question at 68 points. This means a buyer researching non invasive fat reduction 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 non invasive fat reduction market.
Where they agree
The points of near-consensus in Non Invasive Fat Reduction.
On other behaviors the three models move almost in lockstep. The points of near-consensus for non invasive fat reduction, where all three landed within a few points of each other:
- Suggests a DIY approach first: 2.7%–5.4% across all three (a 3-point spread).
- Names a specific provider: 0%–5.4% across all three (a 5-point spread).
- Gives price or cost information: 2.7%–8.1% across all three (a 5-point spread).
- Recommends multiple quotes: 0%–8.1% across all three (a 8-point spread).
Measured question by question, the three assistants coded a response the same way most consistently on "names a specific provider" (identical coding in 94.6% of questions) and least consistently on "asks a clarifying question" (18.9%).
Every behavior, measured
All twelve coded behaviors for Non Invasive Fat Reduction, averaged across the three models.
The behaviors AI models reproduce most often for non invasive fat reduction are recommends hiring a professional (55.9% on average), asks a clarifying question (47.8%) and gives selection criteria (32.4%); the rarest are names a specific provider (1.8%), recommends multiple quotes (3.6%) and gives price or cost information (4.5%). Each figure below is the share of a model's 37 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: 55.9% on average (ChatGPT 75.7%, Claude 67.6%, Gemini 24.3%). The spread is 51 points.
- Asks a clarifying question: 47.8% on average (ChatGPT 64.9%, Claude 73%, Gemini 5.4%). The spread is 68 points.
- Gives selection criteria: 32.4% on average (ChatGPT 43.2%, Claude 35.1%, Gemini 18.9%). The spread is 24 points.
- Tells the buyer to verify credentials: 27.9% on average (ChatGPT 40.5%, Claude 37.8%, Gemini 5.4%). The spread is 35 points.
- Mentions case studies or portfolio: 18% on average (ChatGPT 35.1%, Claude 13.5%, Gemini 5.4%). The spread is 30 points.
- Warns about red flags or scams: 15.3% on average (ChatGPT 8.1%, Claude 27%, Gemini 10.8%). The spread is 19 points.
- Tells the buyer to check reviews: 6.3% on average (ChatGPT 10.8%, Claude 8.1%, Gemini 0%). The spread is 11 points.
- Mentions local proximity: 5.4% on average (ChatGPT 10.8%, Claude 5.4%, Gemini 0%). The spread is 11 points.
- Suggests a DIY approach first: 4.5% on average (ChatGPT 5.4%, Claude 5.4%, Gemini 2.7%). The spread is 3 points.
- Gives price or cost information: 4.5% on average (ChatGPT 8.1%, Claude 2.7%, Gemini 2.7%). The spread is 5 points.
- Recommends multiple quotes: 3.6% on average (ChatGPT 2.7%, Claude 8.1%, Gemini 0%). The spread is 8 points.
- Names a specific provider: 1.8% on average (ChatGPT 0%, Claude 0%, Gemini 5.4%). The spread is 5 points.
Trust signals
How well the models protect the non invasive fat reduction buyer.
Beyond whether to hire, the rubric codes how carefully each assistant protects the non invasive fat reduction buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 6.3% of answers on average. Verifying credentials or certifications appeared in 27.9%. Warning about red flags or scams appeared in 15.3%.
On structuring the decision, a selection-criteria checklist showed up in 32.4% of answers on average and a recommendation to gather multiple quotes in 3.6%. The single least-reproduced protective signal for non invasive fat reduction is "recommends multiple quotes" at 3.6% 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 Non Invasive Fat Reduction providers?
For service providers the decisive question is whether these systems name anyone at all. Across 111 non invasive fat reduction answers, a specific provider was named in 1.8% 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 non invasive fat reduction: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
The question set
What these 37 Non Invasive Fat Reduction questions cover.
The 37 questions behind every percentage on this page form a frozen non invasive fat reduction (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 non invasive fat reduction 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 37 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 non invasive fat reduction 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.
37 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: Non Invasive Fat Reduction (2026-07 edition).” AuthoritySpecialist.com. https://authorityspecialist.com/research/ai-seo-statistics/services/health/non-invasive-fat-reduction