Original research · 2026-07 edition

AI SEO Statistics: Contractor (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 contractor.

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

I have a crack in my foundation that's about a quarter-inch wide, should I call a structural engineer or just a general handyman?
Is it cheaper to buy the tile and flooring materials myself before hiring a contractor, or do they get better bulk discounts?
What specific questions should I ask during a reference check to make sure a renovation company actually finishes on schedule?
My guest bathroom has a small mold spot on the ceiling; does this require a full remediation specialist or can a regular painter fix it?
I'm getting three wildly different quotes for a roof replacement, how do I tell which one is the most realistic and not a lowball offer?
What are the must-have clauses in a home improvement contract to protect me if the work is subpar or incomplete?
Is it worth paying a premium for a design-build firm versus hiring an architect and a separate builder for a home addition?
How much should I expect to pay as a down payment for a $20,000 landscaping project without putting myself at risk?
Show all 15 questions
Are there any specific permits I need to personally verify before letting someone start finishing my basement?
What are the warning signs that a contractor is overbooked and might delay my project for months after I sign?
I need to move a load-bearing wall to open up my kitchen, what kind of professional certification should the person doing the work have?
Can I reasonably live in my house during a full kitchen and master bath remodel or should I budget for a short-term rental?
How do I verify if a contractor's liability insurance is actually active and covers my specific type of residential project?
What's the typical lead time right now for getting a reliable HVAC technician out for a non-emergency system replacement?
If a contractor asks to be paid entirely in cash for a significant discount, what are the legal and insurance risks for me as the homeowner?

Model by model

29-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 contractor buyers.

Behavior rates across 15 contractor buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional80%53%53%47%
Suggests DIY first13%7%13%93%
Names specific providers7%0%0%93%
Gives price or cost info27%27%20%60%
Tells to check reviews33%27%0%60%
Tells to verify credentials47%40%13%47%
Mentions case studies / portfolio40%0%0%60%
Mentions local proximity47%27%0%47%
Gives selection criteria80%80%40%40%
Warns about red flags40%53%33%47%
Asks a clarifying question60%60%0%7%
Recommends multiple quotes20%20%7%73%

By model

How each assistant handled Contractor questions.

Reading the 45 answers model by model shows how differently the three assistants treat the same contractor questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 80% (ChatGPT) down to 53.3% (Claude), a 27-point gap on an identical question set.

Across the 15 contractor answers it produced, ChatGPT recommended hiring a professional in 80% 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 distinct providers per answer) and included price or cost information 26.7% of the time. ChatGPT asked a clarifying question before answering in 60% of cases, warned about red flags or scams in 40%, and told the buyer to verify credentials in 46.7%, averaging 616 words per answer. On the remaining cues it told the buyer to check reviews in 33.3%, pointed to case studies or a portfolio in 40%, and framed the choice around local proximity in 46.7%; a selection-criteria checklist appeared in 80% of its answers and a recommendation to gather multiple quotes in 20%.

Across the 15 contractor answers it produced, Claude recommended hiring a professional in 53.3% of them and suggested a DIY approach first 6.7% of the time. It named a specific provider in 0% of answers (about 0 distinct providers per answer) and included price or cost information 26.7% of the time. Claude asked a clarifying question before answering in 60% of cases, warned about red flags or scams in 53.3%, and told the buyer to verify credentials in 40%, averaging 316 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 26.7%; a selection-criteria checklist appeared in 80% of its answers and a recommendation to gather multiple quotes in 20%.

Across the 15 contractor answers it produced, Gemini 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 20% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 33.3%, and told the buyer to verify credentials in 13.3%, averaging 252 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 40% of its answers and a recommendation to gather multiple quotes in 6.7%.

Taken together, ChatGPT is the assistant most likely to route a contractor buyer to a professional (80%) and Claude the least (53.3%). ChatGPT produced the longest answers, at 616 words on average. Specific providers were named most often by ChatGPT (6.7%) — even there, roughly one answer in 15 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 29.3 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a contractor buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 60% (ChatGPT) — a 60-point spread.
  • Mentions local proximity: from 0% (Gemini) to 46.7% (ChatGPT) — a 47-point spread.
  • Mentions case studies or portfolio: from 0% (Claude) to 40% (ChatGPT) — a 40-point spread.
  • Gives selection criteria: from 40% (Gemini) to 80% (ChatGPT) — a 40-point spread.
  • Tells the buyer to verify credentials: from 13.3% (Gemini) to 46.7% (ChatGPT) — a 33-point spread.

The widest single gap — asks a clarifying question, 60 points — means a contractor 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 contractor market.

Where they agree

The points of near-consensus in Contractor.

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

  • Suggests a DIY approach first: 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).
  • Gives price or cost information: 20%–26.7% across all three (a 7-point spread).
  • Recommends multiple quotes: 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 "suggests a DIY approach first" (identical coding in 93.3% of questions) and least consistently on "asks a clarifying question" (6.7%).

Every behavior, measured

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

The behaviors AI models reproduce most often for contractor are gives selection criteria (66.7% on average), recommends hiring a professional (62.2%) and warns about red flags or scams (42.2%); the rarest are names a specific provider (2.2%), suggests a DIY approach first (11.1%) and mentions case studies or portfolio (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:

  • Gives selection criteria: 66.7% on average (ChatGPT 80%, Claude 80%, Gemini 40%) — a 40-point spread.
  • Recommends hiring a professional: 62.2% on average (ChatGPT 80%, Claude 53.3%, Gemini 53.3%) — a 27-point spread.
  • Warns about red flags or scams: 42.2% on average (ChatGPT 40%, Claude 53.3%, Gemini 33.3%) — a 20-point spread.
  • Asks a clarifying question: 40% on average (ChatGPT 60%, Claude 60%, Gemini 0%) — a 60-point spread.
  • Tells the buyer to verify credentials: 33.3% on average (ChatGPT 46.7%, Claude 40%, Gemini 13.3%) — a 33-point spread.
  • Gives price or cost information: 24.5% on average (ChatGPT 26.7%, Claude 26.7%, Gemini 20%) — a 7-point spread.
  • Mentions local proximity: 24.5% on average (ChatGPT 46.7%, Claude 26.7%, Gemini 0%) — a 47-point spread.
  • Tells the buyer to check reviews: 20% on average (ChatGPT 33.3%, Claude 26.7%, Gemini 0%) — a 33-point spread.
  • Recommends multiple quotes: 15.6% on average (ChatGPT 20%, Claude 20%, Gemini 6.7%) — a 13-point spread.
  • Mentions case studies or portfolio: 13.3% on average (ChatGPT 40%, Claude 0%, Gemini 0%) — a 40-point spread.
  • Suggests a DIY approach first: 11.1% on average (ChatGPT 13.3%, Claude 6.7%, Gemini 13.3%) — a 7-point spread.
  • Names a specific provider: 2.2% on average (ChatGPT 6.7%, Claude 0%, Gemini 0%) — a 7-point spread.

Trust signals

How well the models protect the contractor buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the contractor buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 20% of answers on average. Verifying credentials or certifications appeared in 33.3%. Warning about red flags or scams appeared in 42.2%.

On structuring the decision, a selection-criteria checklist showed up in 66.7% of answers on average and a recommendation to gather multiple quotes in 15.6%. The single least-reproduced protective signal for contractor is "recommends multiple quotes" at 15.6% 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 Contractor providers?

For service providers the decisive question is whether these systems name anyone at all. Across 45 contractor answers, a specific provider was named in 2.2% of responses on average — roughly 0 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for contractor: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 15 Contractor questions cover.

The 15 questions behind every percentage on this page were drawn from real contractor (home 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 contractor 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 contractor 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 →