AI SEO Statistics: Food Products (2026-07 edition)

Reading the 45 answers model by model shows how differently the three assistants treat the same food products questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 33.3% (Claude) down to 26.7% (ChatGPT), a 7-point gap on an identical question set.

Across the 15 food products answers it produced, ChatGPT recommended hiring a professional in 26.7% of them and suggested a DIY approach first 26.7% of the time. It named a specific provider in 33.3% of answers (about 1.7 distinct providers per answer) and included price or cost information 6.7% of the time. ChatGPT asked a clarifying question before answering in 26.7% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 33.3%, averaging 577 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 0%, and framed the choice around local proximity in 26.7%; a selection-criteria checklist appeared in 26.7% of its answers and a recommendation to gather multiple quotes in 0%.

Across the 15 food products answers it produced, Claude recommended hiring a professional in 33.3% of them and suggested a DIY approach first 20% of the time. It named a specific provider in 46.7% of answers (about 2.4 distinct providers per answer) and included price or cost information 33.3% of the time. Claude asked a clarifying question before answering in 80% of cases, warned about red flags or scams in 33.3%, and told the buyer to verify credentials in 40%, averaging 296 words per answer. On the remaining cues it told the buyer to check reviews in 40%, pointed to case studies or a portfolio in 0%, 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 0%.

Across the 15 food products answers it produced, Gemini recommended hiring a professional in 33.3% of them and suggested a DIY approach first 6.7% of the time. It named a specific provider in 66.7% of answers (about 1.6 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 20%, and told the buyer to verify credentials in 13.3%, averaging 222 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 13.3%; a selection-criteria checklist appeared in 40% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, Claude is the assistant most likely to route a food products buyer to a professional (33.3%) and ChatGPT the least (26.7%). ChatGPT produced the longest answers, at 577 words on average. Specific providers were named most often by Gemini (66.7%) — even there, roughly one answer in 1 carried a name.

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

• Mentions case studies or portfolio: 0% across all three models.
• Recommends multiple quotes: 0% across all three models.
• Recommends hiring a professional: 26.7%–33.3% across all three (a 7-point spread).
• Warns about red flags or scams: 20%–33.3% across all three (a 13-point spread).

Measured question by question, the three assistants coded a response the same way most consistently on "mentions case studies or portfolio" (identical coding in 100% of questions) and least consistently on "asks a clarifying question" (20%).

Beyond whether to hire, the rubric codes how carefully each assistant protects the food products buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 24.4% of answers on average. Verifying credentials or certifications appeared in 28.9%. Warning about red flags or scams appeared in 24.4%.

On structuring the decision, a selection-criteria checklist showed up in 46.7% of answers on average and a recommendation to gather multiple quotes in 0%. The single least-reproduced protective signal for food products is "recommends multiple quotes" at 0% on average — the clearest opening for content that supplies it, since the models are not yet reliably surfacing that guidance on their own.

The 15 questions behind every percentage on this page were drawn from real food products (ecommerce / online retail; 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 food products 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.