Original research · 2026-07 edition

AI SEO Statistics: Landscaper (2026-07 edition)

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

See how AI talks about YOUR businessFull data table

The question bank

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

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

My backyard has a steep hill that's eroding every time it rains, what kind of landscaping solutions should I look for?
Is it cheaper to install sod myself or pay a professional landscaping company to do it?
What are the typical labor rates for a crew of three landscapers for a full day of yard cleanup?
I want a low-maintenance garden that doesn't need much water, what specific plants and materials should I ask a landscaper for?
How do I know if a landscaper is actually licensed and insured, and what documents should I ask to see?
What's the average cost to put in a 20x20 paver patio with a small fire pit in the suburbs?
Is it normal for a landscaping company to ask for a 50% deposit before they even start the work?
Should I hire a landscape architect or just a regular landscaping contractor for a complete backyard redesign?
Show all 15 questions
What are some red flags to watch out for when getting quotes for a new irrigation system?
If I want my yard ready for a summer party in June, when is the latest I should sign a contract with a landscaper?
Can a landscaper help with drainage issues that are causing water to seep into my basement?
What's the difference between a hardscape specialist and a general landscaper for building a stone outdoor kitchen?
Do landscapers usually offer warranties on the trees and shrubs they plant if they die within the first year?
I have a $5,000 budget for my front yard curb appeal, what projects will give me the most bang for my buck?
How often should I have a professional landscaping crew come by for basic maintenance like weeding and mulching?

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 landscaper buyers.

Behavior rates across 15 landscaper buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional73%73%27%27%
Suggests DIY first7%7%7%100%
Names specific providers0%0%0%100%
Gives price or cost info47%33%47%73%
Tells to check reviews13%7%0%87%
Tells to verify credentials27%27%7%67%
Mentions case studies / portfolio33%20%0%60%
Mentions local proximity47%20%0%47%
Gives selection criteria40%47%20%53%
Warns about red flags20%20%7%80%
Asks a clarifying question60%67%0%27%
Recommends multiple quotes27%33%0%53%

By model

How each assistant handled Landscaper questions.

Reading the 45 answers model by model shows how differently the three assistants treat the same landscaper questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 73.3% (ChatGPT) down to 26.7% (Gemini), a 47-point gap on an identical question set.

Across the 15 landscaper answers it produced, ChatGPT recommended hiring a professional in 73.3% 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 46.7% of the time. ChatGPT asked a clarifying question before answering in 60% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 26.7%, averaging 499 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 33.3%, and framed the choice around local proximity in 46.7%; a selection-criteria checklist appeared in 40% of its answers and a recommendation to gather multiple quotes in 26.7%.

Across the 15 landscaper answers it produced, Claude recommended hiring a professional in 73.3% 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 33.3% of the time. Claude asked a clarifying question before answering in 66.7% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 26.7%, averaging 280 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 20%, and framed the choice around local proximity in 20%; a selection-criteria checklist appeared in 46.7% of its answers and a recommendation to gather multiple quotes in 33.3%.

Across the 15 landscaper answers it produced, Gemini recommended hiring a professional in 26.7% 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 46.7% 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 6.7%, averaging 262 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 20% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a landscaper buyer to a professional (73.3%) and Gemini the least (26.7%). ChatGPT produced the longest answers, at 499 words on average. No model named a specific provider in more than 0% of answers.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 23.7 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a landscaper buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 66.7% (Claude) — a 67-point spread.
  • Mentions local proximity: from 0% (Gemini) to 46.7% (ChatGPT) — a 47-point spread.
  • Recommends hiring a professional: from 26.7% (Gemini) to 73.3% (ChatGPT) — a 47-point spread.
  • Mentions case studies or portfolio: from 0% (Gemini) to 33.3% (ChatGPT) — a 33-point spread.
  • Recommends multiple quotes: from 0% (Gemini) to 33.3% (Claude) — a 33-point spread.

The widest single gap — asks a clarifying question, 67 points — means a landscaper 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 landscaper market.

Where they agree

The points of near-consensus in Landscaper.

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

  • Suggests a DIY approach first: 6.7% across all three models.
  • Names a specific provider: 0% across all three models.
  • Tells the buyer to check reviews: 0%–13.3% across all three (a 13-point spread).
  • Warns about red flags or scams: 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 100% of questions) and least consistently on "asks a clarifying question" (26.7%).

Every behavior, measured

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

The behaviors AI models reproduce most often for landscaper are recommends hiring a professional (57.8% on average), gives price or cost information (42.2%) and asks a clarifying question (42.2%); the rarest are names a specific provider (0%), tells the buyer to check reviews (6.7%) and suggests a DIY approach first (6.7%). 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 73.3%, Claude 73.3%, Gemini 26.7%) — a 47-point spread.
  • Gives price or cost information: 42.2% on average (ChatGPT 46.7%, Claude 33.3%, Gemini 46.7%) — a 13-point spread.
  • Asks a clarifying question: 42.2% on average (ChatGPT 60%, Claude 66.7%, Gemini 0%) — a 67-point spread.
  • Gives selection criteria: 35.6% on average (ChatGPT 40%, Claude 46.7%, Gemini 20%) — a 27-point spread.
  • Mentions local proximity: 22.2% on average (ChatGPT 46.7%, Claude 20%, Gemini 0%) — a 47-point spread.
  • Tells the buyer to verify credentials: 20% on average (ChatGPT 26.7%, Claude 26.7%, Gemini 6.7%) — a 20-point spread.
  • Recommends multiple quotes: 20% on average (ChatGPT 26.7%, Claude 33.3%, Gemini 0%) — a 33-point spread.
  • Mentions case studies or portfolio: 17.8% on average (ChatGPT 33.3%, Claude 20%, Gemini 0%) — a 33-point spread.
  • Warns about red flags or scams: 15.6% on average (ChatGPT 20%, Claude 20%, Gemini 6.7%) — a 13-point spread.
  • Suggests a DIY approach first: 6.7% on average (ChatGPT 6.7%, Claude 6.7%, Gemini 6.7%).
  • Tells the buyer to check reviews: 6.7% on average (ChatGPT 13.3%, Claude 6.7%, Gemini 0%) — a 13-point spread.
  • Names a specific provider: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).

Trust signals

How well the models protect the landscaper buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the landscaper buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 6.7% of answers on average. Verifying credentials or certifications appeared in 20%. Warning about red flags or scams appeared in 15.6%.

On structuring the decision, a selection-criteria checklist showed up in 35.6% of answers on average and a recommendation to gather multiple quotes in 20%. The single least-reproduced protective signal for landscaper is "tells the buyer to check reviews" 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 Landscaper providers?

For service providers the decisive question is whether these systems name anyone at all. Across 45 landscaper answers, a specific provider was named in 0% 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 landscaper: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 15 Landscaper questions cover.

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