Original research · 2026-07 edition

AI SEO Statistics: Long Term Rehab Center (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 long term rehab center.

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

What’s the main difference between a 30-day program and a 6-month residential treatment center for chronic relapse?
My son has been through detox twice but keeps failing at home; is it time to look into a long-term facility?
How much does a long-term rehab stay typically cost out of pocket if our insurance only covers the first month?
What questions should I ask during a facility tour to make sure they actually have 24/7 medical supervision?
I need a list of red flags to watch out for when reading online reviews for inpatient recovery centers.
Is it better to send a family member to a rehab center near home or is it more effective to go out of state?
Do long-term rehabs allow you to keep your job or work remotely while you’re in treatment?
What are the success rates for long-term residential programs compared to intensive outpatient therapy?
Show all 15 questions
Can you explain the process of an involuntary commitment to a long-term facility for someone who is a danger to themselves?
We are looking for a facility that specializes in both substance abuse and severe clinical depression, what certifications should they have?
Are there any long-term rehab options that offer a sliding scale fee based on income for families without high-end insurance?
How do I know if a facility is evidence-based versus just using holistic or spiritual methods?
My sister needs a bed immediately but she has a dual diagnosis; who helps coordinate that kind of urgent placement?
What happens if someone wants to leave a long-term program early against medical advice?
Are there specific long-term centers that cater specifically to older adults or seniors with late-life addiction issues?

Model by model

18-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 long term rehab center buyers.

Behavior rates across 15 long term rehab center buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional47%27%33%73%
Suggests DIY first13%7%0%80%
Names specific providers7%27%13%80%
Gives price or cost info7%13%7%93%
Tells to check reviews13%13%0%80%
Tells to verify credentials47%27%20%67%
Mentions case studies / portfolio0%0%0%100%
Mentions local proximity53%40%27%60%
Gives selection criteria60%53%33%53%
Warns about red flags20%27%20%80%
Asks a clarifying question80%67%0%7%
Recommends multiple quotes0%0%0%100%

By model

How each assistant handled Long Term Rehab Center questions.

Reading the 45 answers model by model shows how differently the three assistants treat the same long term rehab center questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 46.7% (ChatGPT) down to 26.7% (Claude), a 20-point gap on an identical question set.

Across the 15 long term rehab center answers it produced, ChatGPT recommended hiring a professional in 46.7% 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 distinct providers per answer) and included price or cost information 6.7% of the time. ChatGPT asked a clarifying question before answering in 80% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 46.7%, averaging 584 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 53.3%; a selection-criteria checklist appeared in 60% of its answers and a recommendation to gather multiple quotes in 0%.

Across the 15 long term rehab center answers it produced, Claude recommended hiring a professional in 26.7% of them and suggested a DIY approach first 6.7% of the time. It named a specific provider in 26.7% of answers (about 0.6 distinct providers per answer) and included price or cost information 13.3% of the time. Claude 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 26.7%, averaging 311 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 40%; a selection-criteria checklist appeared in 53.3% of its answers and a recommendation to gather multiple quotes in 0%.

Across the 15 long term rehab center answers it produced, Gemini recommended hiring a professional in 33.3% of them and suggested a DIY approach first 0% 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 6.7% 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 254 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 26.7%; a selection-criteria checklist appeared in 33.3% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a long term rehab center buyer to a professional (46.7%) and Claude the least (26.7%). ChatGPT produced the longest answers, at 584 words on average. Specific providers were named most often by Claude (26.7%) — even there, roughly one answer in 4 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 18.1 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a long term rehab center buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 80% (ChatGPT) — a 80-point spread.
  • Tells the buyer to verify credentials: from 20% (Gemini) to 46.7% (ChatGPT) — a 27-point spread.
  • Gives selection criteria: from 33.3% (Gemini) to 60% (ChatGPT) — a 27-point spread.
  • Mentions local proximity: from 26.7% (Gemini) to 53.3% (ChatGPT) — a 27-point spread.
  • Recommends hiring a professional: from 26.7% (Claude) to 46.7% (ChatGPT) — a 20-point spread.

The widest single gap — asks a clarifying question, 80 points — means a long term 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 long term rehab center market.

Where they agree

The points of near-consensus in Long Term Rehab Center.

On other behaviors the three models move almost in lockstep — the points of near-consensus for long term 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.
  • Gives price or cost information: 6.7%–13.3% across all three (a 7-point spread).
  • Warns about red flags or scams: 20%–26.7% across all three (a 7-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" (6.7%).

Every behavior, measured

All twelve coded behaviors for Long Term Rehab Center, averaged across the three models.

The behaviors AI models reproduce most often for long term rehab center are gives selection criteria (48.9% on average), asks a clarifying question (48.9%) and mentions local proximity (40%); the rarest are recommends multiple quotes (0%), mentions case studies or portfolio (0%) and suggests a DIY approach first (6.7%). 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: 48.9% on average (ChatGPT 60%, Claude 53.3%, Gemini 33.3%) — a 27-point spread.
  • Asks a clarifying question: 48.9% on average (ChatGPT 80%, Claude 66.7%, Gemini 0%) — a 80-point spread.
  • Mentions local proximity: 40% on average (ChatGPT 53.3%, Claude 40%, Gemini 26.7%) — a 27-point spread.
  • Recommends hiring a professional: 35.6% on average (ChatGPT 46.7%, Claude 26.7%, Gemini 33.3%) — a 20-point spread.
  • Tells the buyer to verify credentials: 31.1% on average (ChatGPT 46.7%, Claude 26.7%, Gemini 20%) — a 27-point spread.
  • Warns about red flags or scams: 22.2% on average (ChatGPT 20%, Claude 26.7%, Gemini 20%) — a 7-point spread.
  • Names a specific provider: 15.6% on average (ChatGPT 6.7%, Claude 26.7%, Gemini 13.3%) — a 20-point spread.
  • Gives price or cost information: 8.9% on average (ChatGPT 6.7%, Claude 13.3%, Gemini 6.7%) — a 7-point spread.
  • Tells the buyer to check reviews: 8.9% on average (ChatGPT 13.3%, Claude 13.3%, Gemini 0%) — a 13-point spread.
  • Suggests a DIY approach first: 6.7% on average (ChatGPT 13.3%, Claude 6.7%, 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 long term rehab center buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the long term 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 31.1%. Warning about red flags or scams appeared in 22.2%.

On structuring the decision, a selection-criteria checklist showed up in 48.9% of answers on average and a recommendation to gather multiple quotes in 0%. The single least-reproduced protective signal for long term 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 Long Term Rehab Center providers?

For service providers the decisive question is whether these systems name anyone at all. Across 45 long term rehab center answers, a specific provider was named in 15.6% 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 long term rehab center: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 15 Long Term Rehab Center questions cover.

The 15 questions behind every percentage on this page were drawn from real long term rehab center (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 long term 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 long term 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 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 →