Original research · 2026-07 edition

AI SEO Statistics: Fast Food Restaurants (2026-07 edition)

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

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The question bank

The questions we tested — sampled from real buyer journeys in fast food restaurants.

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

What's the best fast food place for a family of five on a $30 budget?
Which drive-thru usually has the fastest service during the lunch rush?
Are there any fast food chains that use fresh, never-frozen beef for their burgers?
I need a high-protein meal under 600 calories from a drive-thru, any suggestions?
Which fast food app has the best rewards or freebies for new users right now?
Is it actually cheaper to buy a 20-piece nugget or two 10-pieces at most places?
Which chains offer the best vegetarian or plant-based burgers that actually taste good?
What are the red flags I should look for in a fast food restaurant's dining area?
Show all 40 questions
Who has the most consistent breakfast menu that stays open past 10:30 AM?
I'm on a road trip; which major chains are known for having the cleanest restrooms?
Which fast food joint has the best value menu for a quick afternoon snack?
Are there any fast food places that don't use seed oils for their fries?
Which burger chain is safest for someone with a severe peanut allergy?
Is it worth paying extra for a premium burger at a fast food place or is the basic one better?
Who has the best deals for feeding a large group of people at the last minute?
Which chicken sandwich is actually the spiciest among the top national chains?
How do I know if a specific fast food location is franchised or corporate-owned?
What's the healthiest thing I can order at a typical taco drive-thru?
Which fast food places are currently offering kids eat free nights or discounts?
I'm craving a milkshake; who has the thickest ones that won't melt in five minutes?
Which chains have the most reliable mobile ordering apps that actually have the food ready?
Is there a big difference in quality between the major pizza chains for a quick carryout?
What are some secret menu items that are actually worth the hassle of ordering?
Which fast food companies have the best reputation for treating their staff well?
I have a gluten sensitivity; which fast food places have the safest cross-contamination protocols?
Who offers the best quality coffee if I don't want to go to a dedicated coffee shop?
Which fast food breakfast burrito is the most filling for under five dollars?
Are there any drive-thrus that offer healthy sides like fruit or salad instead of fries?
Who has the best loyalty program if I eat out three or four times a week?
Which fast food place is the most kid-friendly for a quick toddler's birthday lunch?
How can I tell if the oil used for frying at a restaurant is fresh or needs changing?
Which burger chain has the best bang for your buck in terms of protein per dollar?
Is it better to use a third-party delivery app or the restaurant's own app for fast food?
Who has the best seasonal menu items or limited-time offers available right now?
What's the best way to get a refund if my drive-thru order is missing items?
Which fast food places have the best outdoor seating or play areas for children?
I'm looking for a late-night bite; which places are usually open 24 hours in suburban areas?
Which fast food chains have moved to the most sustainable or compostable packaging?
What's the best fast food option for someone who is strictly watching their sodium intake?
Are there any buy one get one deals happening at major burger chains this week?

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 fast food restaurants buyers.

Behavior rates across 40 fast food restaurants buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional5%5%3%95%
Suggests DIY first8%8%0%90%
Names specific providers70%85%90%68%
Gives price or cost info10%20%25%73%
Tells to check reviews13%20%0%75%
Tells to verify credentials3%3%0%95%
Mentions case studies / portfolio0%0%0%100%
Mentions local proximity63%73%15%23%
Gives selection criteria43%45%18%53%
Warns about red flags5%8%3%93%
Asks a clarifying question70%88%0%10%
Recommends multiple quotes0%0%0%100%

By model

How each assistant handled Fast Food Restaurants questions.

Reading the 120 answers model by model shows how differently the three assistants treat the same fast food restaurants questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 5% (ChatGPT) down to 2.5% (Gemini), a 3-point gap on an identical question set.

Across the 40 fast food restaurants answers it produced, ChatGPT recommended hiring a professional in 5% of them and suggested a DIY approach first 7.5% of the time. It named a specific provider in 70% of answers (about 4.8 distinct providers per answer) and included price or cost information 10% of the time. ChatGPT asked a clarifying question before answering in 70% of cases, warned about red flags or scams in 5%, and told the buyer to verify credentials in 2.5%, averaging 339 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 0%, and framed the choice around local proximity in 62.5%; a selection-criteria checklist appeared in 42.5% of its answers and a recommendation to gather multiple quotes in 0%.

Across the 40 fast food restaurants answers it produced, Claude recommended hiring a professional in 5% of them and suggested a DIY approach first 7.5% of the time. It named a specific provider in 85% of answers (about 5.6 distinct providers per answer) and included price or cost information 20% of the time. Claude asked a clarifying question before answering in 87.5% of cases, warned about red flags or scams in 7.5%, and told the buyer to verify credentials in 2.5%, averaging 242 words per answer. On the remaining cues it told the buyer to check reviews in 20%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 72.5%; a selection-criteria checklist appeared in 45% of its answers and a recommendation to gather multiple quotes in 0%.

Across the 40 fast food restaurants answers it produced, Gemini recommended hiring a professional in 2.5% of them and suggested a DIY approach first 0% of the time. It named a specific provider in 90% of answers (about 2.6 distinct providers per answer) and included price or cost information 25% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 2.5%, and told the buyer to verify credentials in 0%, averaging 175 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 15%; a selection-criteria checklist appeared in 17.5% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a fast food restaurants buyer to a professional (5%) and Gemini the least (2.5%). ChatGPT produced the longest answers, at 339 words on average. Specific providers were named most often by Gemini (90%) — 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 fast food restaurants buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 87.5% (Claude) — a 88-point spread.
  • Mentions local proximity: from 15% (Gemini) to 72.5% (Claude) — a 58-point spread.
  • Gives selection criteria: from 17.5% (Gemini) to 45% (Claude) — a 28-point spread.
  • Names a specific provider: from 70% (ChatGPT) to 90% (Gemini) — a 20-point spread.
  • Tells the buyer to check reviews: from 0% (Gemini) to 20% (Claude) — a 20-point spread.

The widest single gap — asks a clarifying question, 88 points — means a fast food restaurants 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 fast food restaurants market.

Where they agree

The points of near-consensus in Fast Food Restaurants.

On other behaviors the three models move almost in lockstep — the points of near-consensus for fast food restaurants, 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: 2.5%–5% across all three (a 3-point spread).
  • Tells the buyer to verify credentials: 0%–2.5% across all three (a 3-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" (10%).

Every behavior, measured

All twelve coded behaviors for Fast Food Restaurants, averaged across the three models.

The behaviors AI models reproduce most often for fast food restaurants are names a specific provider (81.7% on average), asks a clarifying question (52.5%) and mentions local proximity (50%); the rarest are recommends multiple quotes (0%), mentions case studies or portfolio (0%) and tells the buyer to verify credentials (1.7%). 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: 81.7% on average (ChatGPT 70%, Claude 85%, Gemini 90%) — a 20-point spread.
  • Asks a clarifying question: 52.5% on average (ChatGPT 70%, Claude 87.5%, Gemini 0%) — a 88-point spread.
  • Mentions local proximity: 50% on average (ChatGPT 62.5%, Claude 72.5%, Gemini 15%) — a 58-point spread.
  • Gives selection criteria: 35% on average (ChatGPT 42.5%, Claude 45%, Gemini 17.5%) — a 28-point spread.
  • Gives price or cost information: 18.3% on average (ChatGPT 10%, Claude 20%, Gemini 25%) — a 15-point spread.
  • Tells the buyer to check reviews: 10.8% on average (ChatGPT 12.5%, Claude 20%, Gemini 0%) — a 20-point spread.
  • Suggests a DIY approach first: 5% on average (ChatGPT 7.5%, Claude 7.5%, Gemini 0%) — a 8-point spread.
  • Warns about red flags or scams: 5% on average (ChatGPT 5%, Claude 7.5%, Gemini 2.5%) — a 5-point spread.
  • Recommends hiring a professional: 4.2% on average (ChatGPT 5%, Claude 5%, Gemini 2.5%) — a 3-point spread.
  • Tells the buyer to verify credentials: 1.7% on average (ChatGPT 2.5%, Claude 2.5%, Gemini 0%) — a 3-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 fast food restaurants buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the fast food restaurants buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 10.8% of answers on average. Verifying credentials or certifications appeared in 1.7%. Warning about red flags or scams appeared in 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 0%. The single least-reproduced protective signal for fast food restaurants 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 Fast Food Restaurants providers?

For service providers the decisive question is whether these systems name anyone at all. Across 120 fast food restaurants answers, a specific provider was named in 81.7% of responses on average — roughly 4.3 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for fast food restaurants: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 40 Fast Food Restaurants questions cover.

The 40 questions behind every percentage on this page were drawn from real fast food restaurants (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 fast food restaurants 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 fast food restaurants 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 →