AI SEO Statistics: Rehab Center (2026-07 edition)
15 questions · 45/45 expected AI responses · 3 models · measured 2026-07-04
The question bank
The questions we tested — a frozen buyer-intent benchmark for rehab center.
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 15 questions
Model by model
18.9% 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 15 rehab center benchmark questions, 2026-07 edition. Last column: equal-model mean.
| Behavior | ChatGPT | Claude | Gemini | Equal-model mean |
|---|---|---|---|---|
| Recommends hiring a professional | 60% | 53.3% | 40% | 51.1% |
| Suggests DIY first | 33.3% | 33.3% | 13.3% | 26.6% |
| Names specific providers | 6.7% | 20% | 33.3% | 20% |
| Gives price or cost info | 0% | 33.3% | 20% | 17.8% |
| Tells to check reviews | 13.3% | 13.3% | 0% | 8.9% |
| Tells to verify credentials | 40% | 20% | 6.7% | 22.2% |
| Mentions case studies / portfolio | 0% | 0% | 0% | 0% |
| Mentions local proximity | 33.3% | 26.7% | 13.3% | 24.4% |
| Gives selection criteria | 66.7% | 60% | 46.7% | 57.8% |
| Warns about red flags | 26.7% | 26.7% | 20% | 24.5% |
| Asks a clarifying question | 66.7% | 80% | 6.7% | 51.1% |
| 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 15 benchmark questions.
| Behavior | All-model agreement |
|---|---|
| Recommends hiring a professional | 60% |
| Suggests DIY first | 66.7% |
| Names specific providers | 73.3% |
| Gives price or cost info | 60% |
| Tells to check reviews | 86.7% |
| Tells to verify credentials | 66.7% |
| Mentions case studies / portfolio | 100% |
| Mentions local proximity | 66.7% |
| Gives selection criteria | 66.7% |
| Warns about red flags | 93.3% |
| Asks a clarifying question | 20% |
| Recommends multiple quotes | 100% |
By model
How each assistant handled Rehab Center questions.
Reading the 45 answers model by model shows how differently the three assistants treat the same rehab center questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 60% (ChatGPT) down to 40% (Gemini), a 20-point gap on an identical question set.
Across the 15 rehab center answers it produced, ChatGPT recommended hiring a professional in 60% of them and suggested a DIY approach first 33.3% 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 0% of the time. ChatGPT asked a clarifying question before answering in 66.7% of cases, warned about red flags or scams in 26.7%, and told the buyer to verify credentials in 40%, averaging 570 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 33.3%; a selection-criteria checklist appeared in 66.7% of its answers and a recommendation to gather multiple quotes in 0%.
Across the 15 rehab center answers it produced, Claude recommended hiring a professional in 53.3% of them and suggested a DIY approach first 33.3% of the time. It named a specific provider in 20% of answers (about 0.9 distinct providers per answer) and included price or cost information 33.3% of the time. Claude asked a clarifying question before answering in 80% of cases, warned about red flags or scams in 26.7%, and told the buyer to verify credentials in 20%, averaging 298 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 60% of its answers and a recommendation to gather multiple quotes in 0%.
Across the 15 rehab center answers it produced, Gemini recommended hiring a professional in 40% of them and suggested a DIY approach first 13.3% of the time. It named a specific provider in 33.3% 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 6.7% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 6.7%, averaging 243 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 13.3%; a selection-criteria checklist appeared in 46.7% of its answers and a recommendation to gather multiple quotes in 0%.
Taken together, ChatGPT is the assistant most likely to route a rehab center buyer to a professional (60%) and Gemini the least (40%). ChatGPT produced the longest answers, at 570 words on average. Specific providers were named most often by Gemini (33.3%) — even there, roughly one answer in 3 carried a name.
Where they disagree
The behaviors where the choice of model changes the answer.
Question-level model disagreement is 18.9% — 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 a rehab center buyer happens to ask matters most:
- Asks a clarifying question: from 6.7% (Gemini) to 80% (Claude) — a 73-point spread.
- Gives price or cost information: from 0% (ChatGPT) to 33.3% (Claude) — a 33-point spread.
- Tells the buyer to verify credentials: from 6.7% (Gemini) to 40% (ChatGPT) — a 33-point spread.
- Names a specific provider: from 6.7% (ChatGPT) to 33.3% (Gemini) — a 27-point spread.
- Recommends hiring a professional: from 40% (Gemini) to 60% (ChatGPT) — a 20-point spread.
The widest single gap — asks a clarifying question, 73 points — means a rehab center 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 rehab center market.
Where they agree
The points of near-consensus in Rehab Center.
On other behaviors the three models move almost in lockstep — the points of near-consensus for rehab center, where all three landed within a few points of each other:
- Mentions case studies or portfolio: 0% across all three models.
- Recommends multiple quotes: 0% across all three models.
- Warns about red flags or scams: 20%–26.7% across all three (a 7-point spread).
- Tells the buyer to check reviews: 0%–13.3% across all three (a 13-point spread).
Measured question by question, the three assistants coded a response the same way most consistently on "mentions case studies or portfolio" (identical coding in 100% of questions) and least consistently on "asks a clarifying question" (20%).
Every behavior, measured
All twelve coded behaviors for Rehab Center, averaged across the three models.
The behaviors AI models reproduce most often for rehab center are gives selection criteria (57.8% on average), recommends hiring a professional (51.1%) and asks a clarifying question (51.1%); the rarest are recommends multiple quotes (0%), mentions case studies or portfolio (0%) and tells the buyer to check reviews (8.9%). 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:
- Gives selection criteria: 57.8% on average (ChatGPT 66.7%, Claude 60%, Gemini 46.7%) — a 20-point spread.
- Recommends hiring a professional: 51.1% on average (ChatGPT 60%, Claude 53.3%, Gemini 40%) — a 20-point spread.
- Asks a clarifying question: 51.1% on average (ChatGPT 66.7%, Claude 80%, Gemini 6.7%) — a 73-point spread.
- Suggests a DIY approach first: 26.6% on average (ChatGPT 33.3%, Claude 33.3%, Gemini 13.3%) — a 20-point spread.
- Warns about red flags or scams: 24.5% on average (ChatGPT 26.7%, Claude 26.7%, Gemini 20%) — a 7-point spread.
- Mentions local proximity: 24.4% on average (ChatGPT 33.3%, Claude 26.7%, Gemini 13.3%) — a 20-point spread.
- Tells the buyer to verify credentials: 22.2% on average (ChatGPT 40%, Claude 20%, Gemini 6.7%) — a 33-point spread.
- Names a specific provider: 20% on average (ChatGPT 6.7%, Claude 20%, Gemini 33.3%) — a 27-point spread.
- Gives price or cost information: 17.8% on average (ChatGPT 0%, Claude 33.3%, Gemini 20%) — a 33-point spread.
- Tells the buyer to check reviews: 8.9% on average (ChatGPT 13.3%, Claude 13.3%, Gemini 0%) — a 13-point spread.
- Mentions case studies or portfolio: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).
- Recommends multiple quotes: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).
Trust signals
How well the models protect the rehab center buyer.
Beyond whether to hire, the rubric codes how carefully each assistant protects the rehab center buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 8.9% of answers on average. Verifying credentials or certifications appeared in 22.2%. Warning about red flags or scams appeared in 24.5%.
On structuring the decision, a selection-criteria checklist showed up in 57.8% of answers on average and a recommendation to gather multiple quotes in 0%. The single least-reproduced protective signal for rehab center is "recommends multiple quotes" at 0% 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 Rehab Center providers?
For service providers the decisive question is whether these systems name anyone at all. Across 45 rehab center answers, a specific provider was named in 20% of responses on average — roughly 0.5 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for rehab center: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
The question set
What these 15 Rehab Center questions cover.
The 15 questions behind every percentage on this page form a frozen rehab center (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 rehab center 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 rehab center 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 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 2026-07-04 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: Rehab Center (2026-07 edition).” AuthoritySpecialist.com. https://authorityspecialist.com/research/ai-seo-statistics/services/health/rehab-center