Original research · 2026-07 edition

AI SEO Statistics: Car Dealership (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 car dealership.

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

I need a bigger car for a growing family, what are the best mid-size SUVs that actually fit three car seats across the back?
Is it better to lease a new hybrid or just buy a used one if I plan on driving it for at least five years?
What are the specific warning signs that a used car dealership is trying to hide a vehicle's flood damage or accident history?
How do I know if a dealer markup is actually negotiable or if it's a firm price in the current market?
I'm torn between a certified pre-owned sedan and a brand-new base model; which one holds its value better over time?
How can I check which local dealerships have a specific model in stock without giving them my phone number and getting spammed?
My car just died and I need a replacement by the weekend, what's the fastest way to get a car loan approved without getting ripped off on the rate?
Can I get a decent interest rate at a dealership if my credit score is around 620, or should I secure my own financing first?
Show all 15 questions
Is it worth paying the higher labor rates at a dealership service center for a routine brake job compared to a local independent mechanic?
How do I negotiate a better price for my trade-in when the dealer's initial offer is way lower than the online estimates I saw?
What are the standard documentation fees in my area and which 'add-on' fees on the sales contract are actually optional?
The finance manager is really pushing the extended warranty and gap insurance, are those actually worth the monthly cost?
I want to switch to an electric vehicle, what specific questions should I ask the salesperson about battery degradation and home charging setup?
Is it a massive headache to buy a car from a dealership in a different state to save a few thousand dollars, and how does the tax work?
What should I look for in a dealership's reputation beyond just the star rating on Google to make sure they have a good service department?

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 car dealership buyers.

Behavior rates across 15 car dealership buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional33%20%20%60%
Suggests DIY first47%33%33%67%
Names specific providers33%40%47%60%
Gives price or cost info13%20%33%53%
Tells to check reviews20%13%7%87%
Tells to verify credentials20%13%7%87%
Mentions case studies / portfolio0%0%0%100%
Mentions local proximity20%20%33%53%
Gives selection criteria33%47%20%60%
Warns about red flags33%27%27%67%
Asks a clarifying question33%47%0%40%
Recommends multiple quotes27%13%13%60%

By model

How each assistant handled Car Dealership questions.

Reading the 45 answers model by model shows how differently the three assistants treat the same car dealership questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 33.3% (ChatGPT) down to 20% (Claude), a 13-point gap on an identical question set.

Across the 15 car dealership answers it produced, ChatGPT recommended hiring a professional in 33.3% of them and suggested a DIY approach first 46.7% of the time. It named a specific provider in 33.3% of answers (about 1.5 distinct providers per answer) and included price or cost information 13.3% of the time. ChatGPT asked a clarifying question before answering in 33.3% of cases, warned about red flags or scams in 33.3%, and told the buyer to verify credentials in 20%, averaging 625 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 20%; a selection-criteria checklist appeared in 33.3% of its answers and a recommendation to gather multiple quotes in 26.7%.

Across the 15 car dealership answers it produced, Claude recommended hiring a professional in 20% of them and suggested a DIY approach first 33.3% of the time. It named a specific provider in 40% of answers (about 1.7 distinct providers per answer) and included price or cost information 20% of the time. Claude 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 13.3%, averaging 322 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 20%; a selection-criteria checklist appeared in 46.7% of its answers and a recommendation to gather multiple quotes in 13.3%.

Across the 15 car dealership answers it produced, Gemini recommended hiring a professional in 20% of them and suggested a DIY approach first 33.3% of the time. It named a specific provider in 46.7% of answers (about 1.3 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 26.7%, and told the buyer to verify credentials in 6.7%, averaging 251 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 33.3%; a selection-criteria checklist appeared in 20% of its answers and a recommendation to gather multiple quotes in 13.3%.

Taken together, ChatGPT is the assistant most likely to route a car dealership buyer to a professional (33.3%) and Claude the least (20%). ChatGPT produced the longest answers, at 625 words on average. Specific providers were named most often by Gemini (46.7%) — even there, roughly one answer in 2 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 car dealership buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 46.7% (Claude) — a 47-point spread.
  • Gives selection criteria: from 20% (Gemini) to 46.7% (Claude) — a 27-point spread.
  • Gives price or cost information: from 13.3% (ChatGPT) to 33.3% (Gemini) — a 20-point spread.
  • Suggests a DIY approach first: from 33.3% (Claude) to 46.7% (ChatGPT) — a 13-point spread.
  • Names a specific provider: from 33.3% (ChatGPT) to 46.7% (Gemini) — a 13-point spread.

The widest single gap — asks a clarifying question, 47 points — means a car dealership 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 car dealership market.

Where they agree

The points of near-consensus in Car Dealership.

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

  • Mentions case studies or portfolio: 0% across all three models.
  • Warns about red flags or scams: 26.7%–33.3% across all three (a 7-point spread).
  • Recommends hiring a professional: 20%–33.3% across all three (a 13-point spread).
  • Tells the buyer to check reviews: 6.7%–20% 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" (40%).

Every behavior, measured

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

The behaviors AI models reproduce most often for car dealership are names a specific provider (40% on average), suggests a DIY approach first (37.8%) and gives selection criteria (33.3%); the rarest are mentions case studies or portfolio (0%), tells the buyer to verify credentials (13.3%) and tells the buyer to check reviews (13.3%). 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:

  • Names a specific provider: 40% on average (ChatGPT 33.3%, Claude 40%, Gemini 46.7%) — a 13-point spread.
  • Suggests a DIY approach first: 37.8% on average (ChatGPT 46.7%, Claude 33.3%, Gemini 33.3%) — a 13-point spread.
  • Gives selection criteria: 33.3% on average (ChatGPT 33.3%, Claude 46.7%, Gemini 20%) — a 27-point spread.
  • Warns about red flags or scams: 28.9% on average (ChatGPT 33.3%, Claude 26.7%, Gemini 26.7%) — a 7-point spread.
  • Asks a clarifying question: 26.7% on average (ChatGPT 33.3%, Claude 46.7%, Gemini 0%) — a 47-point spread.
  • Recommends hiring a professional: 24.4% on average (ChatGPT 33.3%, Claude 20%, Gemini 20%) — a 13-point spread.
  • Mentions local proximity: 24.4% on average (ChatGPT 20%, Claude 20%, Gemini 33.3%) — a 13-point spread.
  • Gives price or cost information: 22.2% on average (ChatGPT 13.3%, Claude 20%, Gemini 33.3%) — a 20-point spread.
  • Recommends multiple quotes: 17.8% on average (ChatGPT 26.7%, Claude 13.3%, Gemini 13.3%) — a 13-point spread.
  • Tells the buyer to check reviews: 13.3% on average (ChatGPT 20%, Claude 13.3%, Gemini 6.7%) — a 13-point spread.
  • Tells the buyer to verify credentials: 13.3% on average (ChatGPT 20%, Claude 13.3%, Gemini 6.7%) — 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 car dealership buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the car dealership buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 13.3% of answers on average. Verifying credentials or certifications appeared in 13.3%. Warning about red flags or scams appeared in 28.9%.

On structuring the decision, a selection-criteria checklist showed up in 33.3% of answers on average and a recommendation to gather multiple quotes in 17.8%. The single least-reproduced protective signal for car dealership is "tells the buyer to check reviews" at 13.3% 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 Car Dealership providers?

For service providers the decisive question is whether these systems name anyone at all. Across 45 car dealership answers, a specific provider was named in 40% of responses on average — roughly 1.5 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for car dealership: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 15 Car Dealership questions cover.

The 15 questions behind every percentage on this page were drawn from real car dealership (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 car dealership 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 car dealership 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 →