AI SEO Statistics: European Auto Repair (2026-07 edition)

Reading the 45 answers model by model shows how differently the three assistants treat the same european auto repair 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 40% (Gemini), a 47-point gap on an identical question set.

Across the 15 european auto repair answers it produced, ChatGPT recommended hiring a professional in 86.7% of them and suggested a DIY approach first 26.7% 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 13.3% of the time. ChatGPT asked a clarifying question before answering in 46.7% of cases, warned about red flags or scams in 6.7%, and told the buyer to verify credentials in 13.3%, averaging 492 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 20% of its answers and a recommendation to gather multiple quotes in 6.7%.

Across the 15 european auto repair answers it produced, Claude recommended hiring a professional in 53.3% 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 13.3% of the time. Claude asked a clarifying question before answering in 53.3% of cases, warned about red flags or scams in 33.3%, and told the buyer to verify credentials in 6.7%, averaging 296 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 0%; a selection-criteria checklist appeared in 33.3% of its answers and a recommendation to gather multiple quotes in 13.3%.

Across the 15 european auto repair answers it produced, Gemini 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 6.7% of answers (about 0.5 distinct providers per answer) and included price or cost information 13.3% of the time. Gemini asked a clarifying question before answering in 6.7% of cases, warned about red flags or scams in 6.7%, and told the buyer to verify credentials in 6.7%, averaging 225 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 0%; a selection-criteria checklist appeared in 26.7% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route an european auto repair buyer to a professional (86.7%) and Gemini the least (40%). ChatGPT produced the longest answers, at 492 words on average. Specific providers were named most often by ChatGPT (6.7%) — even there, roughly one answer in 15 carried a name.

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

• Gives price or cost information: 13.3% across all three models.
• Mentions case studies or portfolio: 0% across all three models.
• Tells the buyer to verify credentials: 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).

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%).

Beyond whether to hire, the rubric codes how carefully each assistant protects the european auto repair buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 6.7% of answers on average. Verifying credentials or certifications appeared in 8.9%. Warning about red flags or scams appeared in 15.6%.

On structuring the decision, a selection-criteria checklist showed up in 26.7% of answers on average and a recommendation to gather multiple quotes in 6.7%. The single least-reproduced protective signal for european auto repair is "tells the buyer to check reviews" at 6.7% on average — the clearest opening for content that supplies it, since the models are not yet reliably surfacing that guidance on their own.

The 15 questions behind every percentage on this page were drawn from real european auto repair (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 european auto repair 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.