Original research · 2026-07 edition

AI SEO Statistics: Dental Practice (2026-07 edition)

15 questions · 45 AI responses · 3 models · measured 2026-07-04

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The question bank

The questions we tested — sampled from real buyer journeys in dental practice.

Each model answered every question once, same wording, same day. These are the prompts behind every percentage on this page.

My gums bleed every time I brush, is that a sign of an infection or can it wait until my next cleaning?
Are those at-home teeth whitening kits actually safe for your enamel or should I just pay for a professional treatment?
How much does a standard dental cleaning and X-ray usually cost out-of-pocket if I don't have insurance?
What specific qualities or certifications should I look for when choosing a dentist for a toddler's first visit?
I chipped a back molar eating something hard; what is the immediate first aid and how soon do I need to see someone?
What is the difference between a general dentist and a periodontist when it comes to treating advanced gum disease?
Is it common for dental offices to offer Saturday appointments or late evening hours for people who work full-time?
If a new dentist says I need five fillings but I have no pain, is it worth getting a second opinion?
Show all 15 questions
I have a 1,500 dollar budget for cosmetic work; what are my best options for fixing a small gap in my front teeth?
Do most dental offices offer monthly payment plans or third-party financing for expensive procedures like root canals?
I have extreme dental anxiety and haven't gone in years; what kind of sedation options should I look for in a provider?
Invisalign versus traditional braces—which one is typically faster for a 30-year-old with mild crowding?
My jaw clicks and hurts whenever I wake up; what kind of dental specialist handles TMJ issues?
How can I tell if a dentist is using modern technology like digital impressions instead of those messy physical molds?
An old silver filling just fell out of my tooth; is it dangerous if I accidentally swallowed part of it?

Model by model

20-point average divergence: which AI you ask changes the answer.

The divergence index is the average gap between the most and least likely model per behavior. Higher = the models disagree more about dental practice buyers.

Behavior rates across 15 dental practice buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional87%67%67%67%
Suggests DIY first13%20%13%93%
Names specific providers7%20%13%80%
Gives price or cost info20%20%20%60%
Tells to check reviews0%13%7%87%
Tells to verify credentials13%20%7%87%
Mentions case studies / portfolio7%0%0%93%
Mentions local proximity40%27%7%60%
Gives selection criteria67%47%47%60%
Warns about red flags20%20%13%60%
Asks a clarifying question67%33%0%20%
Recommends multiple quotes13%13%7%73%

By model

How each assistant handled Dental Practice questions.

Reading the 45 answers model by model shows how differently the three assistants treat the same dental practice questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 86.7% (ChatGPT) down to 66.7% (Claude), a 20-point gap on an identical question set.

Across the 15 dental practice answers it produced, ChatGPT recommended hiring a professional in 86.7% of them and suggested a DIY approach first 13.3% of the time. It named a specific provider in 6.7% of answers (about 0.2 distinct providers per answer) and included price or cost information 20% of the time. ChatGPT asked a clarifying question before answering in 66.7% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 13.3%, averaging 400 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 6.7%, and framed the choice around local proximity in 40%; a selection-criteria checklist appeared in 66.7% of its answers and a recommendation to gather multiple quotes in 13.3%.

Across the 15 dental practice answers it produced, Claude recommended hiring a professional in 66.7% of them and suggested a DIY approach first 20% of the time. It named a specific provider in 20% of answers (about 0.3 distinct providers per answer) and included price or cost information 20% of the time. Claude asked a clarifying question before answering in 33.3% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 20%, averaging 275 words per answer. On the remaining cues it told the buyer to check reviews in 13.3%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 26.7%; a selection-criteria checklist appeared in 46.7% of its answers and a recommendation to gather multiple quotes in 13.3%.

Across the 15 dental practice answers it produced, Gemini recommended hiring a professional in 66.7% of them and suggested a DIY approach first 13.3% of the time. It named a specific provider in 13.3% of answers (about 0.5 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 13.3%, and told the buyer to verify credentials in 6.7%, averaging 300 words per answer. On the remaining cues it told the buyer to check reviews in 6.7%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 6.7%; a selection-criteria checklist appeared in 46.7% of its answers and a recommendation to gather multiple quotes in 6.7%.

Taken together, ChatGPT is the assistant most likely to route a dental practice buyer to a professional (86.7%) and Claude the least (66.7%). ChatGPT produced the longest answers, at 400 words on average. Specific providers were named most often by Claude (20%) — even there, roughly one answer in 5 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 20 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a dental practice buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 66.7% (ChatGPT) — a 67-point spread.
  • Mentions local proximity: from 6.7% (Gemini) to 40% (ChatGPT) — a 33-point spread.
  • Recommends hiring a professional: from 66.7% (Claude) to 86.7% (ChatGPT) — a 20-point spread.
  • Gives selection criteria: from 46.7% (Claude) to 66.7% (ChatGPT) — a 20-point spread.
  • Names a specific provider: from 6.7% (ChatGPT) to 20% (Claude) — a 13-point spread.

The widest single gap — asks a clarifying question, 67 points — means a dental practice 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 dental practice market.

Where they agree

The points of near-consensus in Dental Practice.

On other behaviors the three models move almost in lockstep — the points of near-consensus for dental practice, where all three landed within a few points of each other:

  • Gives price or cost information: 20% across all three models.
  • Recommends multiple quotes: 6.7%–13.3% across all three (a 7-point spread).
  • Suggests a DIY approach first: 13.3%–20% across all three (a 7-point spread).
  • Mentions case studies or portfolio: 0%–6.7% across all three (a 7-point spread).

Measured question by question, the three assistants coded a response the same way most consistently on "suggests a DIY approach first" (identical coding in 93.3% of questions) and least consistently on "asks a clarifying question" (20%).

Every behavior, measured

All twelve coded behaviors for Dental Practice, averaged across the three models.

The behaviors AI models reproduce most often for dental practice are recommends hiring a professional (73.4% on average), gives selection criteria (53.4%) and asks a clarifying question (33.3%); the rarest are mentions case studies or portfolio (2.2%), tells the buyer to check reviews (6.7%) and recommends multiple quotes (11.1%). Each figure below is the share of a model's 15 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: 73.4% on average (ChatGPT 86.7%, Claude 66.7%, Gemini 66.7%) — a 20-point spread.
  • Gives selection criteria: 53.4% on average (ChatGPT 66.7%, Claude 46.7%, Gemini 46.7%) — a 20-point spread.
  • Asks a clarifying question: 33.3% on average (ChatGPT 66.7%, Claude 33.3%, Gemini 0%) — a 67-point spread.
  • Mentions local proximity: 24.5% on average (ChatGPT 40%, Claude 26.7%, Gemini 6.7%) — a 33-point spread.
  • Gives price or cost information: 20% on average (ChatGPT 20%, Claude 20%, Gemini 20%).
  • Warns about red flags or scams: 17.8% on average (ChatGPT 20%, Claude 20%, Gemini 13.3%) — a 7-point spread.
  • Suggests a DIY approach first: 15.5% on average (ChatGPT 13.3%, Claude 20%, Gemini 13.3%) — a 7-point spread.
  • Names a specific provider: 13.3% on average (ChatGPT 6.7%, Claude 20%, Gemini 13.3%) — a 13-point spread.
  • Tells the buyer to verify credentials: 13.3% on average (ChatGPT 13.3%, Claude 20%, Gemini 6.7%) — a 13-point spread.
  • Recommends multiple quotes: 11.1% on average (ChatGPT 13.3%, Claude 13.3%, Gemini 6.7%) — a 7-point spread.
  • Tells the buyer to check reviews: 6.7% on average (ChatGPT 0%, Claude 13.3%, Gemini 6.7%) — a 13-point spread.
  • Mentions case studies or portfolio: 2.2% on average (ChatGPT 6.7%, Claude 0%, Gemini 0%) — a 7-point spread.

Trust signals

How well the models protect the dental practice buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the dental practice buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 6.7% of answers on average. Verifying credentials or certifications appeared in 13.3%. Warning about red flags or scams appeared in 17.8%.

On structuring the decision, a selection-criteria checklist showed up in 53.4% of answers on average and a recommendation to gather multiple quotes in 11.1%. The single least-reproduced protective signal for dental practice is "tells the buyer to check reviews" at 6.7% on average — 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 Dental Practice providers?

For service providers the decisive question is whether these systems name anyone at all. Across 45 dental practice answers, a specific provider was named in 13.3% of responses on average — roughly 0.3 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for dental practice: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 15 Dental Practice questions cover.

The 15 questions behind every percentage on this page were drawn from real dental practice (healthcare services; buyer hiring decisions for this specific service) buyer journeys. Each was put to all 3 models once, with identical wording, so the rates above describe how the assistants handled this exact dental practice question set — not 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 15 answers in which the behavior appeared at least once — not a confidence score. Because each model answered every question exactly once on 2026-07-04, the figures describe this specific dental practice 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.

15 standardized buyer questions per industry, one response per model per question (ChatGPT (gpt-5-mini), Claude (claude-sonnet-5), Gemini (gemini-3-flash-preview)), collected 2026-07-04, coded against a fixed 12-behavior rubric with human QA. AI outputs vary with model version, location and time — figures describe this sample and window, and are refreshed each edition. Read the full methodology →