Original research · 2026-07 edition

AI SEO Statistics: Food Delivery Service (2026-07 edition)

40 questions · 120 AI responses · 3 models · measured 2026-07-06

See how AI talks about YOUR businessFull data table

The question bank

The questions we tested — sampled from real buyer journeys in food delivery service.

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

What's the most reliable app for getting hot food delivered on a rainy night?
Is it cheaper to pay for a monthly delivery subscription or just pay the individual fees?
How do I know if a delivery service actually pays their drivers a fair wage?
I'm hosting 10 people for a game night; should I use a standard delivery app or call a catering service?
What are the red flags I should look for in a delivery driver's profile or behavior?
Why is the menu price on the delivery app higher than the price at the actual restaurant?
Which delivery service has the best customer support if my order arrives completely wrong?
Is it worth ordering ice cream for delivery, or will it always arrive melted?
Show all 40 questions
How much should I tip a delivery driver if I live in a high-rise apartment with a difficult parking situation?
Are there any delivery services that specialize in healthy or diet-specific meals like keto or vegan?
What's the fastest way to get a refund when a delivery person drops off the wrong bag?
Can I schedule a food delivery 24 hours in advance for a business lunch?
Which apps allow me to track the driver's exact location in real-time?
Is it better to order directly from a restaurant's website or use a third-party delivery platform?
What are some ways to avoid hidden service fees when ordering dinner for the family?
I need a late-night meal at 3 AM; which delivery services are still active in most suburban areas?
Does the quality of the food suffer significantly when it's transported in a delivery bag for 20 minutes?
Are there any delivery apps that offer a no-contact option that actually works?
How do I compare the delivery radius of different apps to see which one reaches my remote house?
What should I do if my delivery driver is making multiple stops and my food is getting cold?
Are there delivery services that don't charge a small order fee for single-person meals?
Which platform has the most accurate estimated time of arrival?
Is it possible to order from two different restaurants in the same delivery order to save on fees?
How do I handle a situation where the delivery app says the food was delivered but it's not at my door?
What's the etiquette for tipping on a delivery when the service fee is already seven dollars?
Are there any eco-friendly delivery services that use bikes or electric vehicles?
Which apps have the best rewards programs for someone who orders out three times a week?
How can I tell if a restaurant on a delivery app is a ghost kitchen rather than a real storefront?
Is there a way to request that the driver doesn't use plastic cutlery or extra napkins?
What's the best way to get a large office order delivered exactly at noon without it being late?
Do any delivery services offer insurance or a guarantee for food temperature?
Why do some restaurants disappear from delivery apps during peak dinner hours?
Can I use a delivery service to send a meal as a gift to someone in a different city?
What are the pros and cons of using a local independent delivery service versus a national giant?
How do I verify that my food container hasn't been tampered with during the delivery process?
Which delivery apps are most likely to have exclusive deals or buy-one-get-one offers?
Is there a significant difference in delivery speed between using a car versus a scooter in a crowded city?
How do I report a driver who was unprofessional or didn't follow delivery instructions?
Are there any delivery apps that allow for cash payments upon arrival?
What's the best strategy for getting a delivery fee waived on my first order?

Model by model

18-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 food delivery service buyers.

Behavior rates across 40 food delivery service buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional30%28%30%80%
Suggests DIY first28%10%10%78%
Names specific providers45%70%83%48%
Gives price or cost info18%20%35%75%
Tells to check reviews10%15%5%85%
Tells to verify credentials5%0%0%95%
Mentions case studies / portfolio3%0%0%98%
Mentions local proximity40%38%30%45%
Gives selection criteria25%40%28%60%
Warns about red flags8%5%8%93%
Asks a clarifying question58%58%0%28%
Recommends multiple quotes5%5%0%90%

By model

How each assistant handled Food Delivery Service questions.

Reading the 120 answers model by model shows how differently the three assistants treat the same food delivery service questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 30% (ChatGPT) down to 27.5% (Claude), a 3-point gap on an identical question set.

Across the 40 food delivery service answers it produced, ChatGPT recommended hiring a professional in 30% of them and suggested a DIY approach first 27.5% of the time. It named a specific provider in 45% of answers (about 3.8 distinct providers per answer) and included price or cost information 17.5% of the time. ChatGPT asked a clarifying question before answering in 57.5% of cases, warned about red flags or scams in 7.5%, and told the buyer to verify credentials in 5%, averaging 391 words per answer. On the remaining cues it told the buyer to check reviews in 10%, pointed to case studies or a portfolio in 2.5%, and framed the choice around local proximity in 40%; a selection-criteria checklist appeared in 25% of its answers and a recommendation to gather multiple quotes in 5%.

Across the 40 food delivery service answers it produced, Claude recommended hiring a professional in 27.5% of them and suggested a DIY approach first 10% of the time. It named a specific provider in 70% of answers (about 3.7 distinct providers per answer) and included price or cost information 20% of the time. Claude asked a clarifying question before answering in 57.5% of cases, warned about red flags or scams in 5%, and told the buyer to verify credentials in 0%, averaging 254 words per answer. On the remaining cues it told the buyer to check reviews in 15%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 37.5%; a selection-criteria checklist appeared in 40% of its answers and a recommendation to gather multiple quotes in 5%.

Across the 40 food delivery service answers it produced, Gemini recommended hiring a professional in 30% of them and suggested a DIY approach first 10% of the time. It named a specific provider in 82.5% of answers (about 3.6 distinct providers per answer) and included price or cost information 35% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 7.5%, and told the buyer to verify credentials in 0%, averaging 258 words per answer. On the remaining cues it told the buyer to check reviews in 5%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 30%; a selection-criteria checklist appeared in 27.5% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a food delivery service buyer to a professional (30%) and Claude the least (27.5%). ChatGPT produced the longest answers, at 391 words on average. Specific providers were named most often by Gemini (82.5%) — even there, roughly one answer in 1 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 18.2 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a food delivery service buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 57.5% (ChatGPT) — a 58-point spread.
  • Names a specific provider: from 45% (ChatGPT) to 82.5% (Gemini) — a 38-point spread.
  • Suggests a DIY approach first: from 10% (Claude) to 27.5% (ChatGPT) — a 18-point spread.
  • Gives price or cost information: from 17.5% (ChatGPT) to 35% (Gemini) — a 18-point spread.
  • Gives selection criteria: from 25% (ChatGPT) to 40% (Claude) — a 15-point spread.

The widest single gap — asks a clarifying question, 58 points — means a food delivery service 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 delivery service market.

Where they agree

The points of near-consensus in Food Delivery Service.

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

  • Recommends hiring a professional: 27.5%–30% across all three (a 3-point spread).
  • Mentions case studies or portfolio: 0%–2.5% across all three (a 3-point spread).
  • Warns about red flags or scams: 5%–7.5% across all three (a 3-point spread).
  • Tells the buyer to verify credentials: 0%–5% across all three (a 5-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 97.5% of questions) and least consistently on "asks a clarifying question" (27.5%).

Every behavior, measured

All twelve coded behaviors for Food Delivery Service, averaged across the three models.

The behaviors AI models reproduce most often for food delivery service are names a specific provider (65.8% on average), asks a clarifying question (38.3%) and mentions local proximity (35.8%); the rarest are mentions case studies or portfolio (0.8%), tells the buyer to verify credentials (1.7%) and recommends multiple quotes (3.3%). Each figure below is the share of a model's 40 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: 65.8% on average (ChatGPT 45%, Claude 70%, Gemini 82.5%) — a 38-point spread.
  • Asks a clarifying question: 38.3% on average (ChatGPT 57.5%, Claude 57.5%, Gemini 0%) — a 58-point spread.
  • Mentions local proximity: 35.8% on average (ChatGPT 40%, Claude 37.5%, Gemini 30%) — a 10-point spread.
  • Gives selection criteria: 30.8% on average (ChatGPT 25%, Claude 40%, Gemini 27.5%) — a 15-point spread.
  • Recommends hiring a professional: 29.2% on average (ChatGPT 30%, Claude 27.5%, Gemini 30%) — a 3-point spread.
  • Gives price or cost information: 24.2% on average (ChatGPT 17.5%, Claude 20%, Gemini 35%) — a 18-point spread.
  • Suggests a DIY approach first: 15.8% on average (ChatGPT 27.5%, Claude 10%, Gemini 10%) — a 18-point spread.
  • Tells the buyer to check reviews: 10% on average (ChatGPT 10%, Claude 15%, Gemini 5%) — a 10-point spread.
  • Warns about red flags or scams: 6.7% on average (ChatGPT 7.5%, Claude 5%, Gemini 7.5%) — a 3-point spread.
  • Recommends multiple quotes: 3.3% on average (ChatGPT 5%, Claude 5%, Gemini 0%) — a 5-point spread.
  • Tells the buyer to verify credentials: 1.7% on average (ChatGPT 5%, Claude 0%, Gemini 0%) — a 5-point spread.
  • Mentions case studies or portfolio: 0.8% on average (ChatGPT 2.5%, Claude 0%, Gemini 0%) — a 3-point spread.

Trust signals

How well the models protect the food delivery service buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the food delivery service buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 10% of answers on average. Verifying credentials or certifications appeared in 1.7%. Warning about red flags or scams appeared in 6.7%.

On structuring the decision, a selection-criteria checklist showed up in 30.8% of answers on average and a recommendation to gather multiple quotes in 3.3%. The single least-reproduced protective signal for food delivery service is "tells the buyer to verify credentials" at 1.7% 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 Food Delivery Service providers?

For service providers the decisive question is whether these systems name anyone at all. Across 120 food delivery service answers, a specific provider was named in 65.8% of responses on average — roughly 3.7 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for food delivery service: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 40 Food Delivery Service questions cover.

The 40 questions behind every percentage on this page were drawn from real food delivery service (hospitality; 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 delivery service 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 40 answers in which the behavior appeared at least once — not a confidence score. Because each model answered every question exactly once on 2026-07-06, the figures describe this specific food delivery service 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.

40 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 2026-07-06, 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 →