AI SEO Statistics: Auto Parts (2026-07 edition)
15 questions · 45/45 expected AI responses · 3 models · measured 2026-07-05
The question bank
The questions we tested — a frozen buyer-intent benchmark for auto parts.
The question set was curated from a predefined buyer-intent taxonomy and held constant for this edition. Each model received the same wording. These are the prompts behind every percentage on this page.
Show all 15 questions
Model by model
19.6% question-level model disagreement.
This rate is the average pairwise disagreement between binary behavior codes across questions and behaviors. It is not the gap between the highest and lowest aggregated model percentages.
Behavior prevalence across 15 auto parts benchmark questions, 2026-07 edition. Last column: equal-model mean.
| Behavior | ChatGPT | Claude | Gemini | Equal-model mean |
|---|---|---|---|---|
| Recommends hiring a professional | 53.3% | 33.3% | 20% | 35.5% |
| Suggests DIY first | 33.3% | 26.7% | 20% | 26.7% |
| Names specific providers | 13.3% | 26.7% | 40% | 26.7% |
| Gives price or cost info | 33.3% | 33.3% | 53.3% | 40% |
| Tells to check reviews | 26.7% | 13.3% | 0% | 13.3% |
| Tells to verify credentials | 13.3% | 6.7% | 0% | 6.7% |
| Mentions case studies / portfolio | 0% | 0% | 0% | 0% |
| Mentions local proximity | 13.3% | 6.7% | 13.3% | 11.1% |
| Gives selection criteria | 33.3% | 53.3% | 20% | 35.5% |
| Warns about red flags | 6.7% | 13.3% | 6.7% | 8.9% |
| Asks a clarifying question | 73.3% | 40% | 0% | 37.8% |
| Recommends multiple quotes | 13.3% | 13.3% | 0% | 8.9% |
Question-level agreement
How often all measured models received the same binary code.
Agreement is calculated question by question for each behavior. A high value can coexist with a low behavior prevalence; it means the models usually agreed on whether the behavior appeared.
All-model binary agreement by behavior across 15 benchmark questions.
| Behavior | All-model agreement |
|---|---|
| Recommends hiring a professional | 66.7% |
| Suggests DIY first | 66.7% |
| Names specific providers | 53.3% |
| Gives price or cost info | 80% |
| Tells to check reviews | 73.3% |
| Tells to verify credentials | 86.7% |
| Mentions case studies / portfolio | 100% |
| Mentions local proximity | 86.7% |
| Gives selection criteria | 53.3% |
| Warns about red flags | 80% |
| Asks a clarifying question | 20% |
| Recommends multiple quotes | 80% |
By model
How each assistant handled Auto Parts questions.
Reading the 45 answers model by model shows how differently the three assistants treat the same auto parts questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 53.3% (ChatGPT) down to 20% (Gemini), a 33-point gap on an identical question set.
Across the 15 auto parts answers it produced, ChatGPT recommended hiring a professional in 53.3% of them and suggested a DIY approach first 33.3% of the time. It named a specific provider in 13.3% of answers (about 0.5 distinct providers per answer) and included price or cost information 33.3% of the time. ChatGPT asked a clarifying question before answering in 73.3% of cases, warned about red flags or scams in 6.7%, and told the buyer to verify credentials in 13.3%, averaging 476 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 0%, and framed the choice around local proximity in 13.3%; a selection-criteria checklist appeared in 33.3% of its answers and a recommendation to gather multiple quotes in 13.3%.
Across the 15 auto parts answers it produced, Claude recommended hiring a professional in 33.3% of them and suggested a DIY approach first 26.7% of the time. It named a specific provider in 26.7% of answers (about 0.8 distinct providers per answer) and included price or cost information 33.3% of the time. Claude asked a clarifying question before answering in 40% of cases, warned about red flags or scams in 13.3%, and told the buyer to verify credentials in 6.7%, averaging 300 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 6.7%; a selection-criteria checklist appeared in 53.3% of its answers and a recommendation to gather multiple quotes in 13.3%.
Across the 15 auto parts answers it produced, Gemini recommended hiring a professional in 20% of them and suggested a DIY approach first 20% of the time. It named a specific provider in 40% of answers (about 1.2 distinct providers per answer) and included price or cost information 53.3% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 6.7%, and told the buyer to verify credentials in 0%, averaging 258 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 13.3%; a selection-criteria checklist appeared in 20% of its answers and a recommendation to gather multiple quotes in 0%.
Taken together, ChatGPT is the assistant most likely to route an auto parts buyer to a professional (53.3%) and Gemini the least (20%). ChatGPT produced the longest answers, at 476 words on average. Specific providers were named most often by Gemini (40%) — even there, roughly one answer in 3 carried a name.
Where they disagree
The behaviors where the choice of model changes the answer.
Question-level model disagreement is 19.6% — the average pairwise rate at which models received different binary codes across questions and behaviors. The observed rate spreads below are a separate measure showing where which assistant an auto parts buyer happens to ask matters most:
- Asks a clarifying question: from 0% (Gemini) to 73.3% (ChatGPT) — a 73-point spread.
- Recommends hiring a professional: from 20% (Gemini) to 53.3% (ChatGPT) — a 33-point spread.
- Gives selection criteria: from 20% (Gemini) to 53.3% (Claude) — a 33-point spread.
- Names a specific provider: from 13.3% (ChatGPT) to 40% (Gemini) — a 27-point spread.
- Tells the buyer to check reviews: from 0% (Gemini) to 26.7% (ChatGPT) — a 27-point spread.
The widest single gap — asks a clarifying question, 73 points — means an auto parts 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 parts market.
Where they agree
The points of near-consensus in Auto Parts.
On other behaviors the three models move almost in lockstep — the points of near-consensus for auto parts, where all three landed within a few points of each other:
- Mentions case studies or portfolio: 0% across all three models.
- Mentions local proximity: 6.7%–13.3% across all three (a 7-point spread).
- Warns about red flags or scams: 6.7%–13.3% across all three (a 7-point spread).
- Suggests a DIY approach first: 20%–33.3% 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" (20%).
Every behavior, measured
All twelve coded behaviors for Auto Parts, averaged across the three models.
The behaviors AI models reproduce most often for auto parts are gives price or cost information (40% on average), asks a clarifying question (37.8%) and recommends hiring a professional (35.5%); the rarest are mentions case studies or portfolio (0%), tells the buyer to verify credentials (6.7%) and recommends multiple quotes (8.9%). 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:
- Gives price or cost information: 40% on average (ChatGPT 33.3%, Claude 33.3%, Gemini 53.3%) — a 20-point spread.
- Asks a clarifying question: 37.8% on average (ChatGPT 73.3%, Claude 40%, Gemini 0%) — a 73-point spread.
- Recommends hiring a professional: 35.5% on average (ChatGPT 53.3%, Claude 33.3%, Gemini 20%) — a 33-point spread.
- Gives selection criteria: 35.5% on average (ChatGPT 33.3%, Claude 53.3%, Gemini 20%) — a 33-point spread.
- Suggests a DIY approach first: 26.7% on average (ChatGPT 33.3%, Claude 26.7%, Gemini 20%) — a 13-point spread.
- Names a specific provider: 26.7% on average (ChatGPT 13.3%, Claude 26.7%, Gemini 40%) — a 27-point spread.
- Tells the buyer to check reviews: 13.3% on average (ChatGPT 26.7%, Claude 13.3%, Gemini 0%) — a 27-point spread.
- Mentions local proximity: 11.1% on average (ChatGPT 13.3%, Claude 6.7%, Gemini 13.3%) — a 7-point spread.
- Warns about red flags or scams: 8.9% on average (ChatGPT 6.7%, Claude 13.3%, Gemini 6.7%) — a 7-point spread.
- Recommends multiple quotes: 8.9% on average (ChatGPT 13.3%, Claude 13.3%, Gemini 0%) — a 13-point spread.
- Tells the buyer to verify credentials: 6.7% on average (ChatGPT 13.3%, Claude 6.7%, Gemini 0%) — 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 auto parts buyer.
Beyond whether to hire, the rubric codes how carefully each assistant protects the auto parts 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 6.7%. Warning about red flags or scams appeared in 8.9%.
On structuring the decision, a selection-criteria checklist showed up in 35.5% of answers on average and a recommendation to gather multiple quotes in 8.9%. The single least-reproduced protective signal for auto parts is "tells the buyer to verify credentials" 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.
Referral behavior
Do AI models name Auto Parts providers?
For service providers the decisive question is whether these systems name anyone at all. Across 45 auto parts answers, a specific provider was named in 26.7% of responses on average — roughly 0.8 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for auto parts: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
The question set
What these 15 Auto Parts questions cover.
The 15 questions behind every percentage on this page form a frozen auto parts (automotive services; buyer hiring decisions for this specific service) buyer-intent benchmark. Each was put to all 3 models once, with identical wording, so the rates above describe how the assistants handled this exact auto parts 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 2026-07-05, the figures describe this specific auto parts 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 frozen benchmark questions, one expected response per model per question (ChatGPT API (gpt-5-mini), Claude API (claude-sonnet-5), Gemini API (gemini-3-flash-preview)), collected 2026-07-05 and coded against a fixed 12-behavior rubric. The pipeline validates the schema, recomputes aggregates and reports consistency issues. AI outputs vary with model version, location and time, so the figures describe this edition's exact sample and measurement window. Read the full methodology →
Citation
Cite this edition.
Authority Specialist. “AI SEO Statistics: Auto Parts (2026-07 edition).” AuthoritySpecialist.com. https://authorityspecialist.com/research/ai-seo-statistics/services/automotive/auto-parts