AI SEO Statistics: Landscape Lighting (2026-07 edition)

Reading the 120 answers model by model shows how differently the three assistants treat the same landscape lighting questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 62.5% (ChatGPT) down to 7.5% (Gemini), a 55-point gap on an identical question set.

Across the 40 landscape lighting answers it produced, ChatGPT recommended hiring a professional in 62.5% of them and suggested a DIY approach first 32.5% of the time. It named a specific provider in 5% of answers (about 0.2 distinct providers per answer) and included price or cost information 17.5% of the time. ChatGPT asked a clarifying question before answering in 82.5% of cases, warned about red flags or scams in 10%, and told the buyer to verify credentials in 12.5%, averaging 528 words per answer. On the remaining cues it told the buyer to check reviews in 12.5%, pointed to case studies or a portfolio in 10%, and framed the choice around local proximity in 20%; a selection-criteria checklist appeared in 37.5% of its answers and a recommendation to gather multiple quotes in 7.5%.

Across the 40 landscape lighting answers it produced, Claude recommended hiring a professional in 17.5% of them and suggested a DIY approach first 22.5% of the time. It named a specific provider in 10% of answers (about 0.4 distinct providers per answer) and included price or cost information 17.5% of the time. Claude asked a clarifying question before answering in 77.5% of cases, warned about red flags or scams in 7.5%, and told the buyer to verify credentials in 2.5%, averaging 285 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 5%, and framed the choice around local proximity in 10%; a selection-criteria checklist appeared in 42.5% of its answers and a recommendation to gather multiple quotes in 5%.

Across the 40 landscape lighting answers it produced, Gemini recommended hiring a professional in 7.5% of them and suggested a DIY approach first 17.5% of the time. It named a specific provider in 7.5% of answers (about 0.1 distinct providers per answer) and included price or cost information 15% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 5%, and told the buyer to verify credentials in 0%, averaging 272 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 2.5%, and framed the choice around local proximity in 0%; a selection-criteria checklist appeared in 25% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a landscape lighting buyer to a professional (62.5%) and Gemini the least (7.5%). ChatGPT produced the longest answers, at 528 words on average. Specific providers were named most often by Claude (10%) — even there, roughly one answer in 10 carried a name.

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

• Gives price or cost information: 15%–17.5% across all three (a 3-point spread).
• Names a specific provider: 5%–10% across all three (a 5-point spread).
• Warns about red flags or scams: 5%–10% across all three (a 5-point spread).
• Mentions case studies or portfolio: 2.5%–10% across all three (a 8-point spread).

Measured question by question, the three assistants coded a response the same way most consistently on "warns about red flags or scams" (identical coding in 95% of questions) and least consistently on "asks a clarifying question" (0%).

Beyond whether to hire, the rubric codes how carefully each assistant protects the landscape lighting buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 4.2% of answers on average. Verifying credentials or certifications appeared in 5%. Warning about red flags or scams appeared in 7.5%.

On structuring the decision, a selection-criteria checklist showed up in 35% of answers on average and a recommendation to gather multiple quotes in 4.2%. The single least-reproduced protective signal for landscape lighting is "tells the buyer to check reviews" at 4.2% on average — the clearest opening for content that supplies it, since the models are not yet reliably surfacing that guidance on their own.

The 40 questions behind every percentage on this page were drawn from real landscape lighting (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 landscape lighting 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.