Original research · 2026-07 edition

AI SEO Statistics: Gym (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 gym.

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

I'm feeling sluggish and out of shape after working from home for two years, how do I know if I need a gym membership or just more walks?
Is it actually cheaper to build a home gym with a rack and weights or just pay for a monthly membership at a local place?
What should I look for in a gym if I'm specifically interested in powerlifting and need a place with enough squat racks?
How much is a reasonable monthly rate for a gym that includes group classes and a sauna in a mid-sized city?
What are the main pros and cons of those big 24-hour chain gyms versus a smaller local boutique fitness studio?
I need a gym that's open at 5 AM and has clean showers so I can go straight to my office job downtown.
What are some red flags in a gym contract I should watch out for before signing up for a full year?
I have a wedding in three months and want to tone up; should I look for a gym with personal trainers or just join a HIIT-focused studio?
Show all 15 questions
How can I tell if a gym is beginner-friendly or if it’s going to be full of bodybuilders that make me feel intimidated?
Do gyms usually charge an initiation fee or annual maintenance fee on top of the monthly price and can I get those waived?
Are there any gyms that offer reliable childcare while I workout and how much does that usually add to the cost?
I travel a lot for work; is it better to get a national gym pass or just pay drop-in fees at different places?
How do I check if a gym is actually clean and well-maintained without just trusting the tour they give me?
I have chronic lower back pain; should I join a regular gym or look for a medical fitness center with physical therapists on staff?
Which gyms are known for having the easiest cancellation policies because I don't want to get stuck in a forever contract?

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 gym buyers.

Behavior rates across 15 gym buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional40%27%40%60%
Suggests DIY first13%13%7%87%
Names specific providers20%33%40%60%
Gives price or cost info33%13%47%60%
Tells to check reviews13%33%0%67%
Tells to verify credentials0%13%0%87%
Mentions case studies / portfolio0%0%0%100%
Mentions local proximity27%33%20%33%
Gives selection criteria40%80%47%40%
Warns about red flags27%33%20%80%
Asks a clarifying question33%60%0%33%
Recommends multiple quotes7%7%0%87%

By model

How each assistant handled Gym questions.

Reading the 45 answers model by model shows how differently the three assistants treat the same gym questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 40% (ChatGPT) down to 26.7% (Claude), a 13-point gap on an identical question set.

Across the 15 gym answers it produced, ChatGPT 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 20% of answers (about 1 distinct providers per answer) and included price or cost information 33.3% of the time. ChatGPT asked a clarifying question before answering in 33.3% of cases, warned about red flags or scams in 26.7%, and told the buyer to verify credentials in 0%, averaging 532 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 40% of its answers and a recommendation to gather multiple quotes in 6.7%.

Across the 15 gym answers it produced, Claude recommended hiring a professional in 26.7% of them and suggested a DIY approach first 13.3% of the time. It named a specific provider in 33.3% of answers (about 1.2 distinct providers per answer) and included price or cost information 13.3% of the time. Claude asked a clarifying question before answering in 60% of cases, warned about red flags or scams in 33.3%, and told the buyer to verify credentials in 13.3%, averaging 282 words per answer. On the remaining cues it told the buyer to check reviews in 33.3%, 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 80% of its answers and a recommendation to gather multiple quotes in 6.7%.

Across the 15 gym answers it produced, Gemini recommended hiring a professional in 40% of them and suggested a DIY approach first 6.7% 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 46.7% 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 0%, averaging 228 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 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 gym buyer to a professional (40%) and Claude the least (26.7%). ChatGPT produced the longest answers, at 532 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 22.6 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a gym buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 60% (Claude) — a 60-point spread.
  • Gives selection criteria: from 40% (ChatGPT) to 80% (Claude) — a 40-point spread.
  • Gives price or cost information: from 13.3% (Claude) to 46.7% (Gemini) — a 33-point spread.
  • Tells the buyer to check reviews: from 0% (Gemini) to 33.3% (Claude) — a 33-point spread.
  • Names a specific provider: from 20% (ChatGPT) to 40% (Gemini) — a 20-point spread.

The widest single gap — asks a clarifying question, 60 points — means a gym 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 gym market.

Where they agree

The points of near-consensus in Gym.

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

  • Mentions case studies or portfolio: 0% across all three models.
  • Suggests a DIY approach first: 6.7%–13.3% across all three (a 7-point spread).
  • Recommends multiple quotes: 0%–6.7% across all three (a 7-point spread).
  • Recommends hiring a professional: 26.7%–40% 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" (33.3%).

Every behavior, measured

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

The behaviors AI models reproduce most often for gym are gives selection criteria (55.6% on average), recommends hiring a professional (35.6%) and names a specific provider (31.1%); the rarest are mentions case studies or portfolio (0%), tells the buyer to verify credentials (4.4%) and recommends multiple quotes (4.5%). 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: 55.6% on average (ChatGPT 40%, Claude 80%, Gemini 46.7%) — a 40-point spread.
  • Recommends hiring a professional: 35.6% on average (ChatGPT 40%, Claude 26.7%, Gemini 40%) — a 13-point spread.
  • Names a specific provider: 31.1% on average (ChatGPT 20%, Claude 33.3%, Gemini 40%) — a 20-point spread.
  • Gives price or cost information: 31.1% on average (ChatGPT 33.3%, Claude 13.3%, Gemini 46.7%) — a 33-point spread.
  • Asks a clarifying question: 31.1% on average (ChatGPT 33.3%, Claude 60%, Gemini 0%) — a 60-point spread.
  • Mentions local proximity: 26.7% on average (ChatGPT 26.7%, Claude 33.3%, Gemini 20%) — a 13-point spread.
  • Warns about red flags or scams: 26.7% on average (ChatGPT 26.7%, Claude 33.3%, Gemini 20%) — a 13-point spread.
  • Tells the buyer to check reviews: 15.5% on average (ChatGPT 13.3%, Claude 33.3%, Gemini 0%) — a 33-point spread.
  • Suggests a DIY approach first: 11.1% on average (ChatGPT 13.3%, Claude 13.3%, Gemini 6.7%) — a 7-point spread.
  • Recommends multiple quotes: 4.5% on average (ChatGPT 6.7%, Claude 6.7%, Gemini 0%) — a 7-point spread.
  • Tells the buyer to verify credentials: 4.4% on average (ChatGPT 0%, Claude 13.3%, Gemini 0%) — a 13-point spread.
  • Mentions case studies or portfolio: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).

Trust signals

How well the models protect the gym buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the gym buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 15.5% of answers on average. Verifying credentials or certifications appeared in 4.4%. Warning about red flags or scams appeared in 26.7%.

On structuring the decision, a selection-criteria checklist showed up in 55.6% of answers on average and a recommendation to gather multiple quotes in 4.5%. The single least-reproduced protective signal for gym is "tells the buyer to verify credentials" at 4.4% 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 Gym providers?

For service providers the decisive question is whether these systems name anyone at all. Across 45 gym answers, a specific provider was named in 31.1% of responses on average — roughly 1.2 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for gym: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 15 Gym questions cover.

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