AI SEO Statistics: Food Products (2026-07 edition)
15 questions · 45/45 expected AI responses · 3 models · measured 2026-07-05
The question bank
The questions we tested — a frozen buyer-intent benchmark for food products.
The question set was curated from a predefined buyer-intent taxonomy and held constant for this edition. Each model received the same wording. These are the prompts behind every percentage on this page.
Show all 15 questions
Model by model
24.4% question-level model disagreement.
This rate is the average pairwise disagreement between binary behavior codes across questions and behaviors. It is not the gap between the highest and lowest aggregated model percentages.
Behavior prevalence across 15 food products benchmark questions, 2026-07 edition. Last column: equal-model mean.
| Behavior | ChatGPT | Claude | Gemini | Equal-model mean |
|---|---|---|---|---|
| Recommends hiring a professional | 26.7% | 33.3% | 33.3% | 31.1% |
| Suggests DIY first | 26.7% | 20% | 6.7% | 17.8% |
| Names specific providers | 33.3% | 46.7% | 66.7% | 48.9% |
| Gives price or cost info | 6.7% | 33.3% | 46.7% | 28.9% |
| Tells to check reviews | 33.3% | 40% | 0% | 24.4% |
| Tells to verify credentials | 33.3% | 40% | 13.3% | 28.9% |
| Mentions case studies / portfolio | 0% | 0% | 0% | 0% |
| Mentions local proximity | 26.7% | 33.3% | 13.3% | 24.4% |
| Gives selection criteria | 26.7% | 73.3% | 40% | 46.7% |
| Warns about red flags | 20% | 33.3% | 20% | 24.4% |
| Asks a clarifying question | 26.7% | 80% | 0% | 35.6% |
| Recommends multiple quotes | 0% | 0% | 0% | 0% |
Question-level agreement
How often all measured models received the same binary code.
Agreement is calculated question by question for each behavior. A high value can coexist with a low behavior prevalence; it means the models usually agreed on whether the behavior appeared.
All-model binary agreement by behavior across 15 benchmark questions.
| Behavior | All-model agreement |
|---|---|
| Recommends hiring a professional | 60% |
| Suggests DIY first | 66.7% |
| Names specific providers | 60% |
| Gives price or cost info | 53.3% |
| Tells to check reviews | 53.3% |
| Tells to verify credentials | 66.7% |
| Mentions case studies / portfolio | 100% |
| Mentions local proximity | 66.7% |
| Gives selection criteria | 40% |
| Warns about red flags | 73.3% |
| Asks a clarifying question | 20% |
| Recommends multiple quotes | 100% |
By model
How each assistant handled Food Products questions.
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.
Where they disagree
The behaviors where the choice of model changes the answer.
Question-level model disagreement is 24.4% — the average pairwise rate at which models received different binary codes across questions and behaviors. The observed rate spreads below are a separate measure showing where which assistant a food products buyer happens to ask matters most:
- Asks a clarifying question: from 0% (Gemini) to 80% (Claude) — a 80-point spread.
- Gives selection criteria: from 26.7% (ChatGPT) to 73.3% (Claude) — a 47-point spread.
- Gives price or cost information: from 6.7% (ChatGPT) to 46.7% (Gemini) — a 40-point spread.
- Tells the buyer to check reviews: from 0% (Gemini) to 40% (Claude) — a 40-point spread.
- Names a specific provider: from 33.3% (ChatGPT) to 66.7% (Gemini) — a 33-point spread.
The widest single gap — asks a clarifying question, 80 points — means a food products 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 food products market.
Where they agree
The points of near-consensus in Food Products.
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%).
Every behavior, measured
All twelve coded behaviors for Food Products, averaged across the three models.
The behaviors AI models reproduce most often for food products are names a specific provider (48.9% on average), gives selection criteria (46.7%) and asks a clarifying question (35.6%); the rarest are recommends multiple quotes (0%), mentions case studies or portfolio (0%) and suggests a DIY approach first (17.8%). 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:
- Names a specific provider: 48.9% on average (ChatGPT 33.3%, Claude 46.7%, Gemini 66.7%) — a 33-point spread.
- Gives selection criteria: 46.7% on average (ChatGPT 26.7%, Claude 73.3%, Gemini 40%) — a 47-point spread.
- Asks a clarifying question: 35.6% on average (ChatGPT 26.7%, Claude 80%, Gemini 0%) — a 80-point spread.
- Recommends hiring a professional: 31.1% on average (ChatGPT 26.7%, Claude 33.3%, Gemini 33.3%) — a 7-point spread.
- Gives price or cost information: 28.9% on average (ChatGPT 6.7%, Claude 33.3%, Gemini 46.7%) — a 40-point spread.
- Tells the buyer to verify credentials: 28.9% on average (ChatGPT 33.3%, Claude 40%, Gemini 13.3%) — a 27-point spread.
- Tells the buyer to check reviews: 24.4% on average (ChatGPT 33.3%, Claude 40%, Gemini 0%) — a 40-point spread.
- Mentions local proximity: 24.4% on average (ChatGPT 26.7%, Claude 33.3%, Gemini 13.3%) — a 20-point spread.
- Warns about red flags or scams: 24.4% on average (ChatGPT 20%, Claude 33.3%, Gemini 20%) — a 13-point spread.
- Suggests a DIY approach first: 17.8% on average (ChatGPT 26.7%, Claude 20%, Gemini 6.7%) — a 20-point spread.
- Mentions case studies or portfolio: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).
- Recommends multiple quotes: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).
Trust signals
How well the models protect the food products buyer.
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.
Referral behavior
Do AI models name Food Products providers?
For service providers the decisive question is whether these systems name anyone at all. Across 45 food products answers, a specific provider was named in 48.9% of responses on average — roughly 1.9 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for food products: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
The question set
What these 15 Food Products questions cover.
The 15 questions behind every percentage on this page form a frozen food products (ecommerce / online retail; buyer hiring decisions for this specific service) buyer-intent benchmark. 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.
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 2026-07-05, the figures describe this specific food products 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 frozen benchmark questions, one expected response per model per question (ChatGPT API (gpt-5-mini), Claude API (claude-sonnet-5), Gemini API (gemini-3-flash-preview)), collected 2026-07-05 and coded against a fixed 12-behavior rubric. The pipeline validates the schema, recomputes aggregates and reports consistency issues. AI outputs vary with model version, location and time, so the figures describe this edition's exact sample and measurement window. Read the full methodology →
Citation
Cite this edition.
Authority Specialist. “AI SEO Statistics: Food Products (2026-07 edition).” AuthoritySpecialist.com. https://authorityspecialist.com/research/ai-seo-statistics/services/ecommerce/food-products