AI SEO Statistics: Automotive (2026-07 edition)

Reading the 120 answers model by model shows how differently the three assistants treat the same automotive questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 77.5% (ChatGPT) down to 37.5% (Gemini), a 40-point gap on an identical question set.

Across the 40 automotive answers it produced, ChatGPT recommended hiring a professional in 77.5% of them and suggested a DIY approach first 22.5% of the time. It named a specific provider in 7.5% of answers (about 0.3 distinct providers per answer) and included price or cost information 45% of the time. ChatGPT asked a clarifying question before answering in 57.5% of cases, warned about red flags or scams in 7.5%, and told the buyer to verify credentials in 20%, averaging 412 words per answer. On the remaining cues it told the buyer to check reviews in 17.5%, pointed to case studies or a portfolio in 7.5%, and framed the choice around local proximity in 27.5%; a selection-criteria checklist appeared in 35% of its answers and a recommendation to gather multiple quotes in 17.5%.

Across the 40 automotive answers it produced, Claude recommended hiring a professional in 72.5% of them and suggested a DIY approach first 27.5% of the time. It named a specific provider in 20% of answers (about 0.6 distinct providers per answer) and included price or cost information 47.5% of the time. Claude asked a clarifying question before answering in 62.5% of cases, warned about red flags or scams in 12.5%, and told the buyer to verify credentials in 5%, averaging 277 words per answer. On the remaining cues it told the buyer to check reviews in 15%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 30%; a selection-criteria checklist appeared in 30% of its answers and a recommendation to gather multiple quotes in 32.5%.

Across the 40 automotive answers it produced, Gemini recommended hiring a professional in 37.5% of them and suggested a DIY approach first 12.5% of the time. It named a specific provider in 15% of answers (about 0.7 distinct providers per answer) and included price or cost information 42.5% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 5%, and told the buyer to verify credentials in 2.5%, averaging 271 words per answer. On the remaining cues it told the buyer to check reviews in 5%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 17.5%; a selection-criteria checklist appeared in 17.5% of its answers and a recommendation to gather multiple quotes in 5%.

Taken together, ChatGPT is the assistant most likely to route an automotive buyer to a professional (77.5%) and Gemini the least (37.5%). ChatGPT produced the longest answers, at 412 words on average. Specific providers were named most often by Claude (20%) — even there, roughly one answer in 5 carried a name.

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

• Gives price or cost information: 42.5%–47.5% across all three (a 5-point spread).
• Mentions case studies or portfolio: 0%–7.5% across all three (a 8-point spread).
• Warns about red flags or scams: 5%–12.5% across all three (a 8-point spread).
• Names a specific provider: 7.5%–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 92.5% of questions) and least consistently on "asks a clarifying question" (22.5%).

Beyond whether to hire, the rubric codes how carefully each assistant protects the automotive buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 12.5% of answers on average. Verifying credentials or certifications appeared in 9.2%. Warning about red flags or scams appeared in 8.3%.

On structuring the decision, a selection-criteria checklist showed up in 27.5% of answers on average and a recommendation to gather multiple quotes in 18.3%. The single least-reproduced protective signal for automotive is "warns about red flags or scams" at 8.3% 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 automotive services (auto repair, body shops, dealerships) buyer journeys, expanded from 4 seed prompts. Each was put to all 3 models once, with identical wording, so the rates above describe how the assistants handled this exact automotive 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.