Original research · 2026-07 edition

AI SEO Statistics: Veterans Rehab Center (2026-07 edition)

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

See how AI talks about YOUR businessFull data table

The question bank

The questions we tested — sampled from real buyer journeys in veterans rehab center.

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

What are the warning signs that a veteran needs inpatient rehab rather than just weekly outpatient therapy?
Is it better to go to a VA hospital for addiction or a private rehab center that specializes in military members?
Does Tricare or the Community Care Network typically cover the full cost of a 30-day residential program?
How do I know if a rehab center actually understands military culture and isn't just using the word veteran for marketing?
What should I look for in a facility if my spouse has both chronic pain from service and an opioid dependency?
Are there veteran rehab programs that allow you to bring a service dog during the residential stay?
What is the average wait time for a bed in a specialized veteran recovery unit compared to a general facility?
Can a veteran be legally forced into rehab if they are a danger to themselves but refuse to go voluntarily?
Show all 15 questions
Are there any rehab centers that focus specifically on female veterans and MST-related trauma?
How do I transition back to civilian life and find a job after finishing a 90-day veteran treatment program?
What are the red flags to watch out for when touring a residential treatment facility for a former soldier?
Is it possible to find a rehab center that incorporates outdoor therapy like equine or wilderness programs for vets?
My brother is a vet in crisis right now; what is the fastest way to get him into a detox program today?
How much out-of-pocket cost should we expect for a private veteran rehab if insurance only pays a percentage?
Do veteran rehab centers help with filing for increased disability ratings or VA paperwork while the patient is in treatment?

Model by model

23-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 veterans rehab center buyers.

Behavior rates across 15 veterans rehab center buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional73%67%47%73%
Suggests DIY first20%13%0%80%
Names specific providers40%73%47%40%
Gives price or cost info20%33%33%60%
Tells to check reviews13%7%0%87%
Tells to verify credentials13%27%7%73%
Mentions case studies / portfolio13%0%0%87%
Mentions local proximity33%47%20%53%
Gives selection criteria40%40%13%33%
Warns about red flags7%7%13%80%
Asks a clarifying question47%47%0%20%
Recommends multiple quotes0%0%0%100%

By model

How each assistant handled Veterans Rehab Center questions.

Reading the 45 answers model by model shows how differently the three assistants treat the same veterans rehab center questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 73.3% (ChatGPT) down to 46.7% (Gemini), a 27-point gap on an identical question set.

Across the 15 veterans rehab center answers it produced, ChatGPT recommended hiring a professional in 73.3% of them and suggested a DIY approach first 20% of the time. It named a specific provider in 40% of answers (about 1.3 distinct providers per answer) and included price or cost information 20% of the time. ChatGPT asked a clarifying question before answering in 46.7% of cases, warned about red flags or scams in 6.7%, and told the buyer to verify credentials in 13.3%, averaging 552 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 33.3%; a selection-criteria checklist appeared in 40% of its answers and a recommendation to gather multiple quotes in 0%.

Across the 15 veterans rehab center answers it produced, Claude 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 73.3% of answers (about 3.2 distinct providers per answer) and included price or cost information 33.3% of the time. Claude asked a clarifying question before answering in 46.7% of cases, warned about red flags or scams in 6.7%, and told the buyer to verify credentials in 26.7%, averaging 294 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 46.7%; a selection-criteria checklist appeared in 40% of its answers and a recommendation to gather multiple quotes in 0%.

Across the 15 veterans rehab center answers it produced, Gemini recommended hiring a professional in 46.7% of them and suggested a DIY approach first 0% of the time. It named a specific provider in 46.7% of answers (about 1.2 distinct providers per answer) and included price or cost information 33.3% 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 238 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 20%; a selection-criteria checklist appeared in 13.3% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a veterans rehab center buyer to a professional (73.3%) and Gemini the least (46.7%). ChatGPT produced the longest answers, at 552 words on average. Specific providers were named most often by Claude (73.3%) — even there, roughly one answer in 1 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 23 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a veterans rehab center buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 46.7% (ChatGPT) — a 47-point spread.
  • Names a specific provider: from 40% (ChatGPT) to 73.3% (Claude) — a 33-point spread.
  • Mentions local proximity: from 20% (Gemini) to 46.7% (Claude) — a 27-point spread.
  • Gives selection criteria: from 13.3% (Gemini) to 40% (ChatGPT) — a 27-point spread.
  • Recommends hiring a professional: from 46.7% (Gemini) to 73.3% (ChatGPT) — a 27-point spread.

The widest single gap — asks a clarifying question, 47 points — means a veterans 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 veterans rehab center market.

Where they agree

The points of near-consensus in Veterans Rehab Center.

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

  • Recommends multiple quotes: 0% across all three models.
  • Warns about red flags or scams: 6.7%–13.3% across all three (a 7-point spread).
  • Gives price or cost information: 20%–33.3% across all three (a 13-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 "recommends multiple quotes" (identical coding in 100% of questions) and least consistently on "asks a clarifying question" (20%).

Every behavior, measured

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

The behaviors AI models reproduce most often for veterans rehab center are recommends hiring a professional (62.2% on average), names a specific provider (53.3%) and mentions local proximity (33.3%); the rarest are recommends multiple quotes (0%), mentions case studies or portfolio (4.4%) and tells the buyer to check reviews (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:

  • Recommends hiring a professional: 62.2% on average (ChatGPT 73.3%, Claude 66.7%, Gemini 46.7%) — a 27-point spread.
  • Names a specific provider: 53.3% on average (ChatGPT 40%, Claude 73.3%, Gemini 46.7%) — a 33-point spread.
  • Mentions local proximity: 33.3% on average (ChatGPT 33.3%, Claude 46.7%, Gemini 20%) — a 27-point spread.
  • Gives selection criteria: 31.1% on average (ChatGPT 40%, Claude 40%, Gemini 13.3%) — a 27-point spread.
  • Asks a clarifying question: 31.1% on average (ChatGPT 46.7%, Claude 46.7%, Gemini 0%) — a 47-point spread.
  • Gives price or cost information: 28.9% on average (ChatGPT 20%, Claude 33.3%, Gemini 33.3%) — a 13-point spread.
  • Tells the buyer to verify credentials: 15.6% on average (ChatGPT 13.3%, Claude 26.7%, Gemini 6.7%) — a 20-point spread.
  • Suggests a DIY approach first: 11.1% on average (ChatGPT 20%, Claude 13.3%, Gemini 0%) — a 20-point spread.
  • Warns about red flags or scams: 8.9% on average (ChatGPT 6.7%, Claude 6.7%, Gemini 13.3%) — a 7-point spread.
  • Tells the buyer to check reviews: 6.7% on average (ChatGPT 13.3%, Claude 6.7%, Gemini 0%) — a 13-point spread.
  • Mentions case studies or portfolio: 4.4% on average (ChatGPT 13.3%, Claude 0%, Gemini 0%) — a 13-point spread.
  • Recommends multiple quotes: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).

Trust signals

How well the models protect the veterans rehab center buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the veterans rehab center 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 15.6%. Warning about red flags or scams appeared in 8.9%.

On structuring the decision, a selection-criteria checklist showed up in 31.1% of answers on average and a recommendation to gather multiple quotes in 0%. The single least-reproduced protective signal for veterans 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 Veterans Rehab Center providers?

For service providers the decisive question is whether these systems name anyone at all. Across 45 veterans rehab center answers, a specific provider was named in 53.3% of responses on average — roughly 1.9 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for veterans rehab center: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 15 Veterans Rehab Center questions cover.

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