Original research · 2026-07 edition

AI SEO Statistics: Auto Repair Shop (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 auto repair shop.

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

My car is making a high-pitched squealing noise when I start it in the morning, does that sound like a belt issue or something more expensive?
I'm trying to decide if I should replace my own spark plugs; how difficult is it for someone with basic tools?
What's a fair price to pay for a brake pad and rotor replacement on a mid-sized sedan in 2024?
What specific questions should I ask a mechanic to make sure they are qualified to work on hybrid battery systems?
Is it better to go to a specialized muffler shop or just a regular local garage for a loud exhaust leak?
My check engine light is blinking and the car is shaking, is it safe to drive it 5 miles to the nearest shop or do I need a tow?
What are the red flags I should look for when I walk into an auto repair shop for the first time?
My car has 120,000 miles and needs a $3,000 transmission repair; is it smarter to fix it or just trade it in?
Show all 15 questions
How can I verify if a mechanic is actually ASE certified like they claim on their website?
Why is the labor estimate so much higher at the dealership compared to the independent shop down the street?
Does getting my routine maintenance done at a local shop instead of the dealer affect my car's resale value or warranty?
I need an affordable mechanic who can do a pre-purchase inspection on a used truck I'm looking at this weekend.
If a shop tells me I need a fuel system cleaning during a standard oil change, is that a necessary service or a common upsell?
What's the difference between a wheel alignment and an unbalanced tire, and how do I know which one I actually need?
How do I handle a situation where a mechanic fixed one thing but now something else isn't working correctly?

Model by model

21-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 auto repair shop buyers.

Behavior rates across 15 auto repair shop buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional87%53%33%47%
Suggests DIY first33%7%13%73%
Names specific providers7%20%20%73%
Gives price or cost info27%40%40%53%
Tells to check reviews27%20%7%80%
Tells to verify credentials27%20%13%87%
Mentions case studies / portfolio7%0%0%93%
Mentions local proximity13%13%20%73%
Gives selection criteria40%53%33%80%
Warns about red flags27%40%27%73%
Asks a clarifying question47%47%7%27%
Recommends multiple quotes13%33%7%67%

By model

How each assistant handled Auto Repair Shop questions.

Reading the 45 answers model by model shows how differently the three assistants treat the same auto repair shop questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 86.7% (ChatGPT) down to 33.3% (Gemini), a 53-point gap on an identical question set.

Across the 15 auto repair shop answers it produced, ChatGPT recommended hiring a professional in 86.7% 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 26.7% of the time. ChatGPT asked a clarifying question before answering in 46.7% of cases, warned about red flags or scams in 26.7%, and told the buyer to verify credentials in 26.7%, averaging 472 words per answer. On the remaining cues it told the buyer to check reviews in 26.7%, pointed to case studies or a portfolio in 6.7%, and framed the choice around local proximity in 13.3%; a selection-criteria checklist appeared in 40% of its answers and a recommendation to gather multiple quotes in 13.3%.

Across the 15 auto repair shop answers it produced, Claude recommended hiring a professional in 53.3% of them and suggested a DIY approach first 6.7% of the time. It named a specific provider in 20% of answers (about 0.3 distinct providers per answer) and included price or cost information 40% of the time. Claude asked a clarifying question before answering in 46.7% of cases, warned about red flags or scams in 40%, and told the buyer to verify credentials in 20%, averaging 294 words per answer. On the remaining cues it told the buyer to check reviews in 20%, 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 53.3% of its answers and a recommendation to gather multiple quotes in 33.3%.

Across the 15 auto repair shop answers it produced, Gemini recommended hiring a professional in 33.3% of them and suggested a DIY approach first 13.3% of the time. It named a specific provider in 20% of answers (about 0.2 distinct providers per answer) and included price or cost information 40% of the time. Gemini asked a clarifying question before answering in 6.7% of cases, warned about red flags or scams in 26.7%, and told the buyer to verify credentials in 13.3%, averaging 277 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 20%; a selection-criteria checklist appeared in 33.3% of its answers and a recommendation to gather multiple quotes in 6.7%.

Taken together, ChatGPT is the assistant most likely to route an auto repair shop buyer to a professional (86.7%) and Gemini the least (33.3%). ChatGPT produced the longest answers, at 472 words on average. Specific providers were named most often by Claude (20%) — even there, roughly one answer in 5 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 20.7 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant an auto repair shop buyer happens to ask matters most:

  • Recommends hiring a professional: from 33.3% (Gemini) to 86.7% (ChatGPT) — a 53-point spread.
  • Asks a clarifying question: from 6.7% (Gemini) to 46.7% (ChatGPT) — a 40-point spread.
  • Suggests a DIY approach first: from 6.7% (Claude) to 33.3% (ChatGPT) — a 27-point spread.
  • Recommends multiple quotes: from 6.7% (Gemini) to 33.3% (Claude) — a 27-point spread.
  • Tells the buyer to check reviews: from 6.7% (Gemini) to 26.7% (ChatGPT) — a 20-point spread.

The widest single gap — recommends hiring a professional, 53 points — means an auto repair shop 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 auto repair shop market.

Where they agree

The points of near-consensus in Auto Repair Shop.

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

  • Mentions case studies or portfolio: 0%–6.7% across all three (a 7-point spread).
  • Mentions local proximity: 13.3%–20% across all three (a 7-point spread).
  • Names a specific provider: 6.7%–20% across all three (a 13-point spread).
  • Gives price or cost information: 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 93.3% of questions) and least consistently on "asks a clarifying question" (26.7%).

Every behavior, measured

All twelve coded behaviors for Auto Repair Shop, averaged across the three models.

The behaviors AI models reproduce most often for auto repair shop are recommends hiring a professional (57.8% on average), gives selection criteria (42.2%) and gives price or cost information (35.6%); the rarest are mentions case studies or portfolio (2.2%), mentions local proximity (15.5%) and names a specific provider (15.6%). 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: 57.8% on average (ChatGPT 86.7%, Claude 53.3%, Gemini 33.3%) — a 53-point spread.
  • Gives selection criteria: 42.2% on average (ChatGPT 40%, Claude 53.3%, Gemini 33.3%) — a 20-point spread.
  • Gives price or cost information: 35.6% on average (ChatGPT 26.7%, Claude 40%, Gemini 40%) — a 13-point spread.
  • Asks a clarifying question: 33.4% on average (ChatGPT 46.7%, Claude 46.7%, Gemini 6.7%) — a 40-point spread.
  • Warns about red flags or scams: 31.1% on average (ChatGPT 26.7%, Claude 40%, Gemini 26.7%) — a 13-point spread.
  • Tells the buyer to verify credentials: 20% on average (ChatGPT 26.7%, Claude 20%, Gemini 13.3%) — a 13-point spread.
  • Suggests a DIY approach first: 17.8% on average (ChatGPT 33.3%, Claude 6.7%, Gemini 13.3%) — a 27-point spread.
  • Tells the buyer to check reviews: 17.8% on average (ChatGPT 26.7%, Claude 20%, Gemini 6.7%) — a 20-point spread.
  • Recommends multiple quotes: 17.8% on average (ChatGPT 13.3%, Claude 33.3%, Gemini 6.7%) — a 27-point spread.
  • Names a specific provider: 15.6% on average (ChatGPT 6.7%, Claude 20%, Gemini 20%) — a 13-point spread.
  • Mentions local proximity: 15.5% on average (ChatGPT 13.3%, Claude 13.3%, Gemini 20%) — a 7-point spread.
  • Mentions case studies or portfolio: 2.2% on average (ChatGPT 6.7%, Claude 0%, Gemini 0%) — a 7-point spread.

Trust signals

How well the models protect the auto repair shop buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the auto repair shop buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 17.8% of answers on average. Verifying credentials or certifications appeared in 20%. Warning about red flags or scams appeared in 31.1%.

On structuring the decision, a selection-criteria checklist showed up in 42.2% of answers on average and a recommendation to gather multiple quotes in 17.8%. The single least-reproduced protective signal for auto repair shop is "tells the buyer to check reviews" at 17.8% 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 Auto Repair Shop providers?

For service providers the decisive question is whether these systems name anyone at all. Across 45 auto repair shop answers, a specific provider was named in 15.6% of responses on average — roughly 0.2 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for auto repair shop: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 15 Auto Repair Shop questions cover.

The 15 questions behind every percentage on this page were drawn from real auto repair shop (automotive 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 auto repair shop 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 auto repair shop 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 →