Original research · 2026-07 edition

AI SEO Statistics: Auto Body Shop (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 auto body shop.

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

Is it worth fixing a deep key scratch on an older car or will it just rust anyway?
Can a body shop pull out a dent in a door without having to repaint the whole thing?
I got into a minor fender bender; should I pay out of pocket or let my insurance handle it?
How do I know if a body shop is using genuine parts instead of cheap knockoffs?
What are the warning signs of a bad repair job when I go to pick up my car?
My car's clear coat is peeling on the roof, can that be spot-treated or do I need a full respray?
Is it true that I can choose any body shop I want even if my insurance company suggests a specific one?
How much does it typically cost to fix a cracked plastic bumper on a mid-sized SUV?
Show all 15 questions
I need to return my leased car in two weeks and there's a dent in the fender, what's the fastest way to get it fixed?
Do body shops give free estimates if I just drive by, or do I need to make an appointment?
What's the average turnaround time for a shop to replace a side mirror and fix a scraped door?
If my frame is slightly bent from a collision, is the car ever really safe to drive again after it's straightened?
Why is there such a huge price difference between three different quotes I got for the same dent?
Does a body shop usually handle the communication with my insurance adjuster or do I have to do it?
Can I get a discount at a body shop if I pay in cash instead of going through insurance?

Model by model

22-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 body shop buyers.

Behavior rates across 15 auto body shop buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional73%47%47%40%
Suggests DIY first13%7%7%93%
Names specific providers0%7%0%93%
Gives price or cost info20%27%40%60%
Tells to check reviews7%0%7%87%
Tells to verify credentials7%7%0%87%
Mentions case studies / portfolio7%0%0%93%
Mentions local proximity20%20%7%80%
Gives selection criteria47%33%13%40%
Warns about red flags13%13%13%73%
Asks a clarifying question73%87%0%7%
Recommends multiple quotes20%40%0%53%

By model

How each assistant handled Auto Body Shop questions.

Reading the 45 answers model by model shows how differently the three assistants treat the same auto body shop 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% (Claude), a 27-point gap on an identical question set.

Across the 15 auto body shop answers it produced, ChatGPT recommended hiring a professional in 73.3% of them and suggested a DIY approach first 13.3% of the time. It named a specific provider in 0% of answers (about 0 distinct providers per answer) and included price or cost information 20% of the time. ChatGPT asked a clarifying question before answering in 73.3% of cases, warned about red flags or scams in 13.3%, and told the buyer to verify credentials in 6.7%, averaging 427 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 6.7%, 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 20%.

Across the 15 auto body shop answers it produced, Claude recommended hiring a professional in 46.7% of them and suggested a DIY approach first 6.7% of the time. It named a specific provider in 6.7% of answers (about 0.2 distinct providers per answer) and included price or cost information 26.7% of the time. Claude asked a clarifying question before answering in 86.7% of cases, warned about red flags or scams in 13.3%, and told the buyer to verify credentials in 6.7%, averaging 288 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 33.3% of its answers and a recommendation to gather multiple quotes in 40%.

Across the 15 auto body shop answers it produced, Gemini recommended hiring a professional in 46.7% of them and suggested a DIY approach first 6.7% of the time. It named a specific provider in 0% of answers (about 0 distinct providers per answer) and included price or cost information 40% 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 0%, averaging 286 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 6.7%; 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 an auto body shop buyer to a professional (73.3%) and Claude the least (46.7%). ChatGPT produced the longest answers, at 427 words on average. Specific providers were named most often by Claude (6.7%) — even there, roughly one answer in 15 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 21.9 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant an auto body shop buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 86.7% (Claude) — a 87-point spread.
  • Recommends multiple quotes: from 0% (Gemini) to 40% (Claude) — a 40-point spread.
  • Gives selection criteria: from 13.3% (Gemini) to 46.7% (ChatGPT) — a 33-point spread.
  • Recommends hiring a professional: from 46.7% (Claude) to 73.3% (ChatGPT) — a 27-point spread.
  • Gives price or cost information: from 20% (ChatGPT) to 40% (Gemini) — a 20-point spread.

The widest single gap — asks a clarifying question, 87 points — means an auto body 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 body shop market.

Where they agree

The points of near-consensus in Auto Body Shop.

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

  • Warns about red flags or scams: 13.3% across all three models.
  • Suggests a DIY approach first: 6.7%–13.3% across all three (a 7-point spread).
  • Names a specific provider: 0%–6.7% across all three (a 7-point spread).
  • Tells the buyer to check reviews: 0%–6.7% across all three (a 7-point spread).

Measured question by question, the three assistants coded a response the same way most consistently on "suggests a DIY approach first" (identical coding in 93.3% of questions) and least consistently on "asks a clarifying question" (6.7%).

Every behavior, measured

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

The behaviors AI models reproduce most often for auto body shop are recommends hiring a professional (55.6% on average), asks a clarifying question (53.3%) and gives selection criteria (31.1%); the rarest are mentions case studies or portfolio (2.2%), names a specific provider (2.2%) and tells the buyer to verify credentials (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:

  • Recommends hiring a professional: 55.6% on average (ChatGPT 73.3%, Claude 46.7%, Gemini 46.7%) — a 27-point spread.
  • Asks a clarifying question: 53.3% on average (ChatGPT 73.3%, Claude 86.7%, Gemini 0%) — a 87-point spread.
  • Gives selection criteria: 31.1% on average (ChatGPT 46.7%, Claude 33.3%, Gemini 13.3%) — a 33-point spread.
  • Gives price or cost information: 28.9% on average (ChatGPT 20%, Claude 26.7%, Gemini 40%) — a 20-point spread.
  • Recommends multiple quotes: 20% on average (ChatGPT 20%, Claude 40%, Gemini 0%) — a 40-point spread.
  • Mentions local proximity: 15.6% on average (ChatGPT 20%, Claude 20%, Gemini 6.7%) — a 13-point spread.
  • Warns about red flags or scams: 13.3% on average (ChatGPT 13.3%, Claude 13.3%, Gemini 13.3%).
  • Suggests a DIY approach first: 8.9% on average (ChatGPT 13.3%, Claude 6.7%, Gemini 6.7%) — a 7-point spread.
  • Tells the buyer to check reviews: 4.5% on average (ChatGPT 6.7%, Claude 0%, Gemini 6.7%) — a 7-point spread.
  • Tells the buyer to verify credentials: 4.5% on average (ChatGPT 6.7%, Claude 6.7%, Gemini 0%) — a 7-point spread.
  • Names a specific provider: 2.2% on average (ChatGPT 0%, Claude 6.7%, Gemini 0%) — 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 body shop buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the auto body shop buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 4.5% of answers on average. Verifying credentials or certifications appeared in 4.5%. Warning about red flags or scams appeared in 13.3%.

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 20%. The single least-reproduced protective signal for auto body shop is "tells the buyer to check reviews" at 4.5% 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 Body Shop providers?

For service providers the decisive question is whether these systems name anyone at all. Across 45 auto body shop answers, a specific provider was named in 2.2% of responses on average — roughly 0.1 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for auto body shop: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 15 Auto Body Shop questions cover.

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