Original research · 2026-07 edition

AI SEO Statistics: Motorcycle Dealer (2026-07 edition)

15 questions · 45 AI responses · 3 models · measured 2026-07-04

The question bank

The questions we tested — sampled from real buyer journeys in motorcycle dealer.

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

I'm looking for my first bike for a short commute, should I start with a 300cc or go straight to a 500cc?
What are the typical dealer fees I should expect to pay on top of the MSRP for a new motorcycle?
Is it safer to buy a used bike from a dealership instead of a private seller if I don't know how to wrench on it?
How does motorcycle financing work for someone with a credit score in the mid-600s?
Can I trade in an old car at a motorcycle dealership or do they only accept other bikes?
What specific maintenance records should a reputable dealer provide for a pre-owned adventure bike?
Are there certain months of the year when dealers are more likely to offer deep discounts on last year's models?
I want to test ride a heavy cruiser, do most shops require me to have my motorcycle endorsement already?
Show all 15 questions
What are the red flags I should look for when a dealer claims a bike is certified pre-owned?
How much of a down payment is usually required to get a decent interest rate on a $15,000 touring bike?
Do motorcycle dealerships typically handle all the DMV registration and titling paperwork for out-of-state buyers?
I need a reliable commuter bike by next week, what's the fastest way to get through the paperwork and take it home?
Is it actually worth paying for the dealer's extended warranty on a used Japanese sportbike?
What questions should I ask the salesperson to figure out if they're just pushing inventory or actually know about bike ergonomics?
Will most dealers allow me to bring an independent mechanic to inspect a used bike before I sign the papers?

Model by model

20-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 motorcycle dealer buyers.

Behavior rates across 15 motorcycle dealer buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional47%33%13%47%
Suggests DIY first20%20%0%67%
Names specific providers13%33%27%73%
Gives price or cost info20%13%13%67%
Tells to check reviews7%0%0%93%
Tells to verify credentials7%7%0%93%
Mentions case studies / portfolio0%0%0%100%
Mentions local proximity7%13%13%80%
Gives selection criteria40%33%20%53%
Warns about red flags33%40%13%53%
Asks a clarifying question33%40%0%47%
Recommends multiple quotes33%13%7%60%

By model

How each assistant handled Motorcycle Dealer questions.

Reading the 45 answers model by model shows how differently the three assistants treat the same motorcycle dealer questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 46.7% (ChatGPT) down to 13.3% (Gemini), a 33-point gap on an identical question set.

Across the 15 motorcycle dealer answers it produced, ChatGPT recommended hiring a professional in 46.7% of them and suggested a DIY approach first 20% of the time. It named a specific provider in 13.3% of answers (about 0.2 distinct providers per answer) and included price or cost information 20% 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 6.7%, averaging 495 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 40% of its answers and a recommendation to gather multiple quotes in 33.3%.

Across the 15 motorcycle dealer answers it produced, Claude recommended hiring a professional in 33.3% of them and suggested a DIY approach first 20% of the time. It named a specific provider in 33.3% of answers (about 0.7 distinct providers per answer) and included price or cost information 13.3% of the time. Claude asked a clarifying question before answering in 40% of cases, warned about red flags or scams in 40%, and told the buyer to verify credentials in 6.7%, averaging 290 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 33.3% of its answers and a recommendation to gather multiple quotes in 13.3%.

Across the 15 motorcycle dealer answers it produced, Gemini recommended hiring a professional in 13.3% of them and suggested a DIY approach first 0% of the time. It named a specific provider in 26.7% 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 0% of cases, warned about red flags or scams in 13.3%, and told the buyer to verify credentials in 0%, averaging 221 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 6.7%.

Taken together, ChatGPT is the assistant most likely to route a motorcycle dealer buyer to a professional (46.7%) and Gemini the least (13.3%). ChatGPT produced the longest answers, at 495 words on average. Specific providers were named most often by Claude (33.3%) — even there, roughly one answer in 3 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 20.4 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a motorcycle dealer buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 40% (Claude) — a 40-point spread.
  • Recommends hiring a professional: from 13.3% (Gemini) to 46.7% (ChatGPT) — a 33-point spread.
  • Warns about red flags or scams: from 13.3% (Gemini) to 40% (Claude) — a 27-point spread.
  • Recommends multiple quotes: from 6.7% (Gemini) to 33.3% (ChatGPT) — a 27-point spread.
  • Suggests a DIY approach first: from 0% (Gemini) to 20% (ChatGPT) — a 20-point spread.

The widest single gap — asks a clarifying question, 40 points — means a motorcycle dealer 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 motorcycle dealer market.

Where they agree

The points of near-consensus in Motorcycle Dealer.

On other behaviors the three models move almost in lockstep — the points of near-consensus for motorcycle dealer, 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).
  • Gives price or cost information: 13.3%–20% 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 "mentions case studies or portfolio" (identical coding in 100% of questions) and least consistently on "asks a clarifying question" (46.7%).

Every behavior, measured

All twelve coded behaviors for Motorcycle Dealer, averaged across the three models.

The behaviors AI models reproduce most often for motorcycle dealer are recommends hiring a professional (31.1% on average), gives selection criteria (31.1%) and warns about red flags or scams (28.9%); the rarest are mentions case studies or portfolio (0%), tells the buyer to check reviews (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: 31.1% on average (ChatGPT 46.7%, Claude 33.3%, Gemini 13.3%) — a 33-point spread.
  • Gives selection criteria: 31.1% on average (ChatGPT 40%, Claude 33.3%, Gemini 20%) — a 20-point spread.
  • Warns about red flags or scams: 28.9% on average (ChatGPT 33.3%, Claude 40%, Gemini 13.3%) — a 27-point spread.
  • Names a specific provider: 24.4% on average (ChatGPT 13.3%, Claude 33.3%, Gemini 26.7%) — a 20-point spread.
  • Asks a clarifying question: 24.4% on average (ChatGPT 33.3%, Claude 40%, Gemini 0%) — a 40-point spread.
  • Recommends multiple quotes: 17.8% on average (ChatGPT 33.3%, Claude 13.3%, Gemini 6.7%) — a 27-point spread.
  • Gives price or cost information: 15.5% on average (ChatGPT 20%, Claude 13.3%, Gemini 13.3%) — a 7-point spread.
  • Suggests a DIY approach first: 13.3% on average (ChatGPT 20%, Claude 20%, Gemini 0%) — a 20-point spread.
  • Mentions local proximity: 11.1% on average (ChatGPT 6.7%, Claude 13.3%, Gemini 13.3%) — 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.
  • Tells the buyer to check reviews: 2.2% on average (ChatGPT 6.7%, Claude 0%, Gemini 0%) — a 7-point spread.
  • Mentions case studies or portfolio: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).

Trust signals

How well the models protect the motorcycle dealer buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the motorcycle dealer buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 2.2% of answers on average. Verifying credentials or certifications appeared in 4.5%. Warning about red flags or scams appeared in 28.9%.

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 17.8%. The single least-reproduced protective signal for motorcycle dealer is "tells the buyer to check reviews" at 2.2% 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 Motorcycle Dealer providers?

For service providers the decisive question is whether these systems name anyone at all. Across 45 motorcycle dealer answers, a specific provider was named in 24.4% of responses on average — roughly 0.5 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for motorcycle dealer: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 15 Motorcycle Dealer questions cover.

The 15 questions behind every percentage on this page were drawn from real motorcycle dealer (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 motorcycle dealer 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 motorcycle dealer 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 →