AI SEO Statistics: Sober Living (2026-07 edition)

Reading the 120 answers model by model shows how differently the three assistants treat the same sober living questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 35% (ChatGPT) down to 2.5% (Gemini), a 33-point gap on an identical question set.

Across the 40 sober living answers it produced, ChatGPT recommended hiring a professional in 35% of them and suggested a DIY approach first 30% of the time. It named a specific provider in 22.5% of answers (about 0.5 distinct providers per answer) and included price or cost information 5% of the time. ChatGPT asked a clarifying question before answering in 60% of cases, warned about red flags or scams in 10%, and told the buyer to verify credentials in 22.5%, averaging 463 words per answer. On the remaining cues it told the buyer to check reviews in 10%, pointed to case studies or a portfolio in 5%, and framed the choice around local proximity in 42.5%; a selection-criteria checklist appeared in 47.5% of its answers and a recommendation to gather multiple quotes in 0%.

Across the 40 sober living answers it produced, Claude recommended hiring a professional in 10% of them and suggested a DIY approach first 12.5% of the time. It named a specific provider in 10% of answers (about 0.3 distinct providers per answer) and included price or cost information 10% of the time. Claude asked a clarifying question before answering in 80% of cases, warned about red flags or scams in 12.5%, and told the buyer to verify credentials in 27.5%, averaging 281 words per answer. On the remaining cues it told the buyer to check reviews in 7.5%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 35%; a selection-criteria checklist appeared in 50% of its answers and a recommendation to gather multiple quotes in 0%.

Across the 40 sober living answers it produced, Gemini recommended hiring a professional in 2.5% of them and suggested a DIY approach first 5% of the time. It named a specific provider in 20% of answers (about 0.5 distinct providers per answer) and included price or cost information 7.5% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 7.5%, and told the buyer to verify credentials in 5%, averaging 261 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 15%; a selection-criteria checklist appeared in 17.5% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a sober living buyer to a professional (35%) and Gemini the least (2.5%). ChatGPT produced the longest answers, at 463 words on average. Specific providers were named most often by ChatGPT (22.5%) — even there, roughly one answer in 4 carried a name.

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

• Recommends multiple quotes: 0% across all three models.
• Gives price or cost information: 5%–10% across all three (a 5-point spread).
• Mentions case studies or portfolio: 0%–5% across all three (a 5-point spread).
• Warns about red flags or scams: 7.5%–12.5% across all three (a 5-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" (12.5%).

Beyond whether to hire, the rubric codes how carefully each assistant protects the sober living buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 5.8% of answers on average. Verifying credentials or certifications appeared in 18.3%. Warning about red flags or scams appeared in 10%.

On structuring the decision, a selection-criteria checklist showed up in 38.3% of answers on average and a recommendation to gather multiple quotes in 0%. The single least-reproduced protective signal for sober living 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.

The 40 questions behind every percentage on this page were drawn from real sober living (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 sober living 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.