Original research · 2026-07 edition

AI SEO Statistics: Orthodontist (2026-07 edition)

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

The question bank

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

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

My 8-year-old's front teeth are coming in really crowded, do I need to see an orthodontist now or wait until they are older?
Why is my jaw clicking every time I chew and can braces actually fix that?
Is it safe to use those mail-order aligner kits or should I definitely see an orthodontist in person?
Can I just use a night guard to fix a small gap in my front teeth instead of getting full braces?
What should I look for in an orthodontist's before and after photos to know if they're actually good?
What is the difference between getting clear aligners from a specialist versus a general dentist?
How much does a typical 18-month treatment for adult braces cost out of pocket if my insurance doesn't cover it?
Are there orthodontists that offer monthly payment plans without doing a credit check?
Show all 15 questions
Which is faster for closing a gap: traditional metal braces or clear aligners?
I play a wind instrument; will ceramic braces interfere with my playing more than lingual braces?
How often do I realistically need to go into the office for adjustments if I choose clear aligners?
What are some red flags I should watch out for during a free orthodontic consultation?
If an orthodontist says I need two teeth pulled before braces, should I get a second opinion?
My wire just snapped and is poking my cheek, but my orthodontist is closed for the weekend—what can I do?
I'm getting married in six months; is there any orthodontic treatment that can show results that quickly?

Model by model

21-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 orthodontist buyers.

Behavior rates across 15 orthodontist buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional87%93%47%47%
Suggests DIY first13%7%7%93%
Names specific providers7%27%40%60%
Gives price or cost info13%13%20%73%
Tells to check reviews7%13%0%87%
Tells to verify credentials20%13%0%73%
Mentions case studies / portfolio13%13%7%80%
Mentions local proximity13%13%13%80%
Gives selection criteria33%40%27%60%
Warns about red flags20%20%7%73%
Asks a clarifying question53%40%0%33%
Recommends multiple quotes27%27%7%67%

By model

How each assistant handled Orthodontist questions.

Reading the 45 answers model by model shows how differently the three assistants treat the same orthodontist questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 93.3% (Claude) down to 46.7% (Gemini), a 47-point gap on an identical question set.

Across the 15 orthodontist 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 0.1 distinct providers per answer) and included price or cost information 13.3% of the time. ChatGPT asked a clarifying question before answering in 53.3% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 20%, averaging 423 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 13.3%, and framed the choice around local proximity in 13.3%; a selection-criteria checklist appeared in 33.3% of its answers and a recommendation to gather multiple quotes in 26.7%.

Across the 15 orthodontist answers it produced, Claude recommended hiring a professional in 93.3% of them and suggested a DIY approach first 6.7% of the time. It named a specific provider in 26.7% of answers (about 0.3 distinct providers per answer) and included price or cost information 13.3% of the time. Claude asked a clarifying question before answering in 40% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 13.3%, averaging 267 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 13.3%, and framed the choice around local proximity in 13.3%; a selection-criteria checklist appeared in 40% of its answers and a recommendation to gather multiple quotes in 26.7%.

Across the 15 orthodontist answers it produced, Gemini recommended hiring a professional in 46.7% of them and suggested a DIY approach first 6.7% of the time. It named a specific provider in 40% of answers (about 0.9 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 6.7%, and told the buyer to verify credentials in 0%, averaging 292 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 13.3%; a selection-criteria checklist appeared in 26.7% of its answers and a recommendation to gather multiple quotes in 6.7%.

Taken together, Claude is the assistant most likely to route an orthodontist buyer to a professional (93.3%) and Gemini the least (46.7%). ChatGPT produced the longest answers, at 423 words on average. Specific providers were named most often by Gemini (40%) — even there, roughly one answer in 3 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 20.7 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant an orthodontist buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 53.3% (ChatGPT) — a 53-point spread.
  • Recommends hiring a professional: from 46.7% (Gemini) to 93.3% (Claude) — a 47-point spread.
  • Names a specific provider: from 6.7% (ChatGPT) to 40% (Gemini) — a 33-point spread.
  • Tells the buyer to verify credentials: from 0% (Gemini) to 20% (ChatGPT) — a 20-point spread.
  • Recommends multiple quotes: from 6.7% (Gemini) to 26.7% (ChatGPT) — a 20-point spread.

The widest single gap — asks a clarifying question, 53 points — means an orthodontist 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 orthodontist market.

Where they agree

The points of near-consensus in Orthodontist.

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

  • Mentions local proximity: 13.3% across all three models.
  • Suggests a DIY approach first: 6.7%–13.3% across all three (a 7-point spread).
  • Mentions case studies or portfolio: 6.7%–13.3% across all three (a 7-point spread).
  • Gives price or cost information: 13.3%–20% 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" (33.3%).

Every behavior, measured

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

The behaviors AI models reproduce most often for orthodontist are recommends hiring a professional (75.6% on average), gives selection criteria (33.3%) and asks a clarifying question (31.1%); the rarest are tells the buyer to check reviews (6.7%), suggests a DIY approach first (8.9%) and mentions case studies or portfolio (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: 75.6% on average (ChatGPT 86.7%, Claude 93.3%, Gemini 46.7%) — a 47-point spread.
  • Gives selection criteria: 33.3% on average (ChatGPT 33.3%, Claude 40%, Gemini 26.7%) — a 13-point spread.
  • Asks a clarifying question: 31.1% on average (ChatGPT 53.3%, Claude 40%, Gemini 0%) — a 53-point spread.
  • Names a specific provider: 24.5% on average (ChatGPT 6.7%, Claude 26.7%, Gemini 40%) — a 33-point spread.
  • Recommends multiple quotes: 20% on average (ChatGPT 26.7%, Claude 26.7%, Gemini 6.7%) — a 20-point spread.
  • Warns about red flags or scams: 15.6% on average (ChatGPT 20%, Claude 20%, Gemini 6.7%) — a 13-point spread.
  • Gives price or cost information: 15.5% on average (ChatGPT 13.3%, Claude 13.3%, Gemini 20%) — a 7-point spread.
  • Mentions local proximity: 13.3% on average (ChatGPT 13.3%, Claude 13.3%, Gemini 13.3%).
  • Tells the buyer to verify credentials: 11.1% on average (ChatGPT 20%, Claude 13.3%, Gemini 0%) — a 20-point spread.
  • Mentions case studies or portfolio: 11.1% on average (ChatGPT 13.3%, Claude 13.3%, Gemini 6.7%) — a 7-point spread.
  • Suggests a DIY approach first: 8.9% on average (ChatGPT 13.3%, Claude 6.7%, Gemini 6.7%) — a 7-point spread.
  • Tells the buyer to check reviews: 6.7% on average (ChatGPT 6.7%, Claude 13.3%, Gemini 0%) — a 13-point spread.

Trust signals

How well the models protect the orthodontist buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the orthodontist 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 11.1%. Warning about red flags or scams appeared in 15.6%.

On structuring the decision, a selection-criteria checklist showed up in 33.3% of answers on average and a recommendation to gather multiple quotes in 20%. The single least-reproduced protective signal for orthodontist 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 Orthodontist providers?

For service providers the decisive question is whether these systems name anyone at all. Across 45 orthodontist answers, a specific provider was named in 24.5% of responses on average — roughly 0.4 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for orthodontist: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 15 Orthodontist questions cover.

The 15 questions behind every percentage on this page were drawn from real orthodontist (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 orthodontist 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 orthodontist 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 →