Original research · 2026-07 edition

AI SEO Statistics: Handyman (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 handyman.

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

My kitchen cabinet door fell off its hinges, can a handyman fix that or do I need a cabinet maker?
Is it safe to replace a ceiling fan myself or should I hire a pro for the wiring?
What specific licenses should a handyman have in my state for minor electrical or plumbing work?
What is the average hourly rate for a handyman in a mid-sized city right now?
Should I hire a specialized plumber or a general handyman to fix a leaky faucet and a running toilet?
How do I find a reliable handyman who is actually willing to show up for small one-hour jobs?
What are some red flags that a handyman might be unqualified for a structural repair project?
I have a hole in my drywall and guests coming tomorrow, how fast can I realistically get someone to patch it?
Show all 15 questions
I have a list of 10 small tasks like hanging pictures and fixing floorboards; is it better to pay hourly or get a flat quote?
Does a handyman's general liability insurance cover damage if they accidentally break a window while working?
Am I expected to buy the replacement parts myself before the handyman arrives, or do they usually source them?
Will a handyman typically bring their own tall ladder for gutter cleaning or do I need to provide the equipment?
Is it standard practice for a handyman to ask for a cash deposit before starting a small home repair?
Can a handyman help with smart home setups like installing a Ring doorbell and setting up a Nest thermostat?
What is the best way to handle it if the repair a handyman did fails just a week after they finished?

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 handyman buyers.

Behavior rates across 15 handyman buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional80%60%53%73%
Suggests DIY first13%20%13%93%
Names specific providers7%13%13%87%
Gives price or cost info20%20%47%47%
Tells to check reviews20%20%7%67%
Tells to verify credentials40%27%7%60%
Mentions case studies / portfolio13%7%0%87%
Mentions local proximity40%13%13%60%
Gives selection criteria47%40%33%53%
Warns about red flags20%13%13%93%
Asks a clarifying question53%33%0%40%
Recommends multiple quotes20%13%0%73%

By model

How each assistant handled Handyman questions.

Reading the 45 answers model by model shows how differently the three assistants treat the same handyman 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% (Gemini), a 27-point gap on an identical question set.

Across the 15 handyman 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 0.1 distinct providers per answer) and included price or cost information 20% of the time. ChatGPT asked a clarifying question before answering in 53.3% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 40%, averaging 426 words per answer. On the remaining cues it told the buyer to check reviews in 20%, pointed to case studies or a portfolio in 13.3%, and framed the choice around local proximity in 40%; a selection-criteria checklist appeared in 46.7% of its answers and a recommendation to gather multiple quotes in 20%.

Across the 15 handyman answers it produced, Claude recommended hiring a professional in 60% of them and suggested a DIY approach first 20% 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 20% of the time. Claude asked a clarifying question before answering in 33.3% of cases, warned about red flags or scams in 13.3%, and told the buyer to verify credentials in 26.7%, averaging 281 words per answer. On the remaining cues it told the buyer to check reviews in 20%, pointed to case studies or a portfolio in 6.7%, and framed the choice around local proximity in 13.3%; a selection-criteria checklist appeared in 40% of its answers and a recommendation to gather multiple quotes in 13.3%.

Across the 15 handyman 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 0.3 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 13.3%, and told the buyer to verify credentials in 6.7%, averaging 298 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 13.3%; a selection-criteria checklist appeared in 33.3% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a handyman buyer to a professional (80%) and Gemini the least (53.3%). ChatGPT produced the longest answers, at 426 words on average. Specific providers were named most often by Claude (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 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 handyman buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 53.3% (ChatGPT) — a 53-point spread.
  • Tells the buyer to verify credentials: from 6.7% (Gemini) to 40% (ChatGPT) — a 33-point spread.
  • Recommends hiring a professional: from 53.3% (Gemini) to 80% (ChatGPT) — a 27-point spread.
  • Gives price or cost information: from 20% (ChatGPT) to 46.7% (Gemini) — a 27-point spread.
  • Mentions local proximity: from 13.3% (Claude) to 40% (ChatGPT) — a 27-point spread.

The widest single gap — asks a clarifying question, 53 points — means a handyman 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 handyman market.

Where they agree

The points of near-consensus in Handyman.

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

  • Names a specific provider: 6.7%–13.3% across all three (a 7-point spread).
  • Suggests a DIY approach first: 13.3%–20% across all three (a 7-point spread).
  • Warns about red flags or scams: 13.3%–20% across all three (a 7-point spread).
  • Tells the buyer to check reviews: 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" (40%).

Every behavior, measured

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

The behaviors AI models reproduce most often for handyman are recommends hiring a professional (64.4% on average), gives selection criteria (40%) and gives price or cost information (28.9%); the rarest are mentions case studies or portfolio (6.7%), recommends multiple quotes (11.1%) and names a specific provider (11.1%). 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: 64.4% on average (ChatGPT 80%, Claude 60%, Gemini 53.3%) — a 27-point spread.
  • Gives selection criteria: 40% on average (ChatGPT 46.7%, Claude 40%, Gemini 33.3%) — a 13-point spread.
  • Gives price or cost information: 28.9% on average (ChatGPT 20%, Claude 20%, Gemini 46.7%) — a 27-point spread.
  • Asks a clarifying question: 28.9% on average (ChatGPT 53.3%, Claude 33.3%, Gemini 0%) — a 53-point spread.
  • Tells the buyer to verify credentials: 24.5% on average (ChatGPT 40%, Claude 26.7%, Gemini 6.7%) — a 33-point spread.
  • Mentions local proximity: 22.2% on average (ChatGPT 40%, Claude 13.3%, Gemini 13.3%) — a 27-point spread.
  • Tells the buyer to check reviews: 15.6% on average (ChatGPT 20%, Claude 20%, Gemini 6.7%) — a 13-point spread.
  • Suggests a DIY approach first: 15.5% on average (ChatGPT 13.3%, Claude 20%, Gemini 13.3%) — a 7-point spread.
  • Warns about red flags or scams: 15.5% on average (ChatGPT 20%, Claude 13.3%, Gemini 13.3%) — a 7-point spread.
  • Names a specific provider: 11.1% on average (ChatGPT 6.7%, Claude 13.3%, Gemini 13.3%) — a 7-point spread.
  • Recommends multiple quotes: 11.1% on average (ChatGPT 20%, Claude 13.3%, Gemini 0%) — a 20-point spread.
  • Mentions case studies or portfolio: 6.7% on average (ChatGPT 13.3%, Claude 6.7%, Gemini 0%) — a 13-point spread.

Trust signals

How well the models protect the handyman buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the handyman buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 15.6% of answers on average. Verifying credentials or certifications appeared in 24.5%. Warning about red flags or scams appeared in 15.5%.

On structuring the decision, a selection-criteria checklist showed up in 40% of answers on average and a recommendation to gather multiple quotes in 11.1%. The single least-reproduced protective signal for handyman is "recommends multiple quotes" at 11.1% 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 Handyman providers?

For service providers the decisive question is whether these systems name anyone at all. Across 45 handyman answers, a specific provider was named in 11.1% of responses on average — roughly 0.3 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for handyman: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 15 Handyman questions cover.

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