Original research · 2026-07 edition

AI SEO Statistics: Painter (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 painter.

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

How much should I expect to pay per square foot for interior painting in a high cost of living area?
Is it better to hire a solo painter or a large painting company for a small one-bedroom apartment?
What are the warning signs that a painting quote is way too low?
My exterior paint is bubbling in small patches; does the whole house need a scrape and repaint or can I just spot fix it?
I am moving in 10 days and need the whole interior painted—is that even realistic for a professional crew?
What specific questions should I ask a painter to make sure they are actually licensed and insured?
Is it cheaper to buy the paint myself and just pay for labor, or let the contractor handle the materials?
How do I know if my old walls have lead paint before I hire someone to sand them down?
Show all 15 questions
What is the difference in durability between a $3000 exterior paint job and a $7000 one?
Can a professional painter fix deep cracks in my plaster walls, or do I need a drywall specialist first?
Do painters usually move the furniture and take down the curtains, or am I expected to have the room empty?
I want to paint my dark wood kitchen cabinets white; will a professional be able to hide the grain completely?
Should I wait until the humidity drops in the fall to have my house painted, or does it not matter with modern paints?
What is the standard payment schedule for a residential painting project—how much should I pay upfront?
If I hire a painter to do my ceiling, will they include the crown molding in the price or is that an extra charge?

Model by model

24-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 painter buyers.

Behavior rates across 15 painter buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional60%60%53%53%
Suggests DIY first13%13%13%100%
Names specific providers0%0%13%87%
Gives price or cost info7%27%47%53%
Tells to check reviews20%20%0%73%
Tells to verify credentials27%27%20%80%
Mentions case studies / portfolio20%20%0%67%
Mentions local proximity20%33%0%67%
Gives selection criteria33%73%40%40%
Warns about red flags7%33%27%47%
Asks a clarifying question40%60%0%40%
Recommends multiple quotes7%40%0%60%

By model

How each assistant handled Painter questions.

Reading the 45 answers model by model shows how differently the three assistants treat the same painter questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 60% (ChatGPT) down to 53.3% (Gemini), a 7-point gap on an identical question set.

Across the 15 painter answers it produced, ChatGPT recommended hiring a professional in 60% 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 6.7% of the time. ChatGPT asked a clarifying question before answering in 40% of cases, warned about red flags or scams in 6.7%, and told the buyer to verify credentials in 26.7%, averaging 485 words per answer. On the remaining cues it told the buyer to check reviews in 20%, pointed to case studies or a portfolio in 20%, and framed the choice around local proximity in 20%; a selection-criteria checklist appeared in 33.3% of its answers and a recommendation to gather multiple quotes in 6.7%.

Across the 15 painter answers it produced, Claude recommended hiring a professional in 60% 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 26.7% of the time. Claude asked a clarifying question before answering in 60% of cases, warned about red flags or scams in 33.3%, and told the buyer to verify credentials in 26.7%, averaging 293 words per answer. On the remaining cues it told the buyer to check reviews in 20%, pointed to case studies or a portfolio in 20%, and framed the choice around local proximity in 33.3%; a selection-criteria checklist appeared in 73.3% of its answers and a recommendation to gather multiple quotes in 40%.

Across the 15 painter 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 13.3% of answers (about 0.4 distinct providers per answer) and included price or cost information 46.7% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 26.7%, and told the buyer to verify credentials in 20%, averaging 293 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 0%.

Taken together, ChatGPT is the assistant most likely to route a painter buyer to a professional (60%) and Gemini the least (53.3%). ChatGPT produced the longest answers, at 485 words on average. Specific providers were named most often by Gemini (13.3%) — even there, roughly one answer in 8 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 24.1 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a painter buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 60% (Claude) — a 60-point spread.
  • Gives price or cost information: from 6.7% (ChatGPT) to 46.7% (Gemini) — a 40-point spread.
  • Gives selection criteria: from 33.3% (ChatGPT) to 73.3% (Claude) — a 40-point spread.
  • Recommends multiple quotes: from 0% (Gemini) to 40% (Claude) — a 40-point spread.
  • Mentions local proximity: from 0% (Gemini) to 33.3% (Claude) — a 33-point spread.

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

Where they agree

The points of near-consensus in Painter.

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

  • Suggests a DIY approach first: 13.3% across all three models.
  • Recommends hiring a professional: 53.3%–60% across all three (a 7-point spread).
  • Tells the buyer to verify credentials: 20%–26.7% across all three (a 7-point spread).
  • Names a specific provider: 0%–13.3% 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 100% of questions) and least consistently on "asks a clarifying question" (40%).

Every behavior, measured

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

The behaviors AI models reproduce most often for painter are recommends hiring a professional (57.8% on average), gives selection criteria (48.9%) and asks a clarifying question (33.3%); the rarest are names a specific provider (4.4%), mentions case studies or portfolio (13.3%) and tells the buyer to check reviews (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:

  • Recommends hiring a professional: 57.8% on average (ChatGPT 60%, Claude 60%, Gemini 53.3%) — a 7-point spread.
  • Gives selection criteria: 48.9% on average (ChatGPT 33.3%, Claude 73.3%, Gemini 40%) — a 40-point spread.
  • Asks a clarifying question: 33.3% on average (ChatGPT 40%, Claude 60%, Gemini 0%) — a 60-point spread.
  • Gives price or cost information: 26.7% on average (ChatGPT 6.7%, Claude 26.7%, Gemini 46.7%) — a 40-point spread.
  • Tells the buyer to verify credentials: 24.5% on average (ChatGPT 26.7%, Claude 26.7%, Gemini 20%) — a 7-point spread.
  • Warns about red flags or scams: 22.2% on average (ChatGPT 6.7%, Claude 33.3%, Gemini 26.7%) — a 27-point spread.
  • Mentions local proximity: 17.8% on average (ChatGPT 20%, Claude 33.3%, Gemini 0%) — a 33-point spread.
  • Recommends multiple quotes: 15.6% on average (ChatGPT 6.7%, Claude 40%, Gemini 0%) — a 40-point spread.
  • Suggests a DIY approach first: 13.3% on average (ChatGPT 13.3%, Claude 13.3%, Gemini 13.3%).
  • Tells the buyer to check reviews: 13.3% on average (ChatGPT 20%, Claude 20%, Gemini 0%) — a 20-point spread.
  • Mentions case studies or portfolio: 13.3% on average (ChatGPT 20%, Claude 20%, Gemini 0%) — a 20-point spread.
  • Names a specific provider: 4.4% on average (ChatGPT 0%, Claude 0%, Gemini 13.3%) — a 13-point spread.

Trust signals

How well the models protect the painter buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the painter 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 24.5%. Warning about red flags or scams appeared in 22.2%.

On structuring the decision, a selection-criteria checklist showed up in 48.9% of answers on average and a recommendation to gather multiple quotes in 15.6%. The single least-reproduced protective signal for painter is "tells the buyer to check reviews" at 13.3% 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 Painter providers?

For service providers the decisive question is whether these systems name anyone at all. Across 45 painter answers, a specific provider was named in 4.4% of responses on average — roughly 0.1 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for painter: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 15 Painter questions cover.

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