Original research · 2026-07 edition

AI SEO Statistics: Charter (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 charter.

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

How much does it typically cost to charter a private jet for a cross-country trip with 6 people?
What's the difference between a broker and a direct operator when booking a luxury yacht?
I need to transport a sports team of 30 people; is a charter bus or a regional jet more cost-effective?
What are the specific safety ratings I should look for when hiring a private flight crew?
Can I bring my large dog in the cabin if I charter a plane for a move?
What exactly is an empty leg flight and how do I find one for a significant discount?
Do I need a special license to charter a catamaran for a week if I want to steer it myself?
What are the most common hidden fees found in luxury bus charter agreements?
Show all 40 questions
Is it possible to book a private jet for a same-day emergency medical transport on short notice?
How far in advance should I book a yacht for a peak summer holiday in the Mediterranean?
What happens to my deposit if the weather is too bad on the day of my boat charter?
Are high-end meals and drinks usually included in the base price of a private jet charter?
What is the standard cancellation policy for professional jet charter services?
How do I verify the insurance coverage of a charter company before I sign a contract?
Comparing a mid-size jet vs a heavy jet for a non-stop flight from London to New York with 8 passengers.
Can I request a specific pilot or captain if I have used a service before and liked them?
What are the typical luggage weight limits for a light jet charter compared to commercial?
Is it cheaper to charter a coach bus for a wedding weekend or just rent several large SUVs?
What are the red flags to watch out for when a charter price quote seems suspiciously low?
How does the billing work for jet fuel surcharges and landing fees in a private flight?
I'm planning a corporate retreat; what amenities should I expect on a high-end executive motorcoach?
Can a private jet land at smaller municipal airports that commercial airlines don't service?
What is the appropriate protocol for tipping the crew on a week-long crewed yacht charter?
Do charter companies provide car seats for infants or do I need to bring my own for the flight?
How do I know if a charter broker is Wyvern or ARGUS certified and why does it matter?
What is the minimum age requirement for passengers on a private helicopter charter?
Can I fully customize the catering menu for a 50-person corporate boat event?
How does a wet lease differ from a standard on-demand charter agreement?
What is the average hourly rate for a turboprop plane versus a light jet for short hops?
Is there a legitimate way to split the cost of a charter with other travelers I don't know personally?
What are the boarding and security procedures like at a private aviation terminal?
How much time before the scheduled takeoff do I actually need to arrive for a chartered flight?
Can I charter a helicopter for a 30-minute hop to avoid heavy city traffic?
What kind of high-speed WiFi connectivity is available on long-range private jets?
Are there specific eco-friendly or carbon-offset charter options available for corporate travel?
What specific documentation do I need for international customs when traveling on a chartered vessel?
How do I handle a dispute if the charter vehicle provided doesn't match the photos in the brochure?
Is smoking or vaping permitted on a private chartered aircraft if the owner allows it?
What are the primary benefits of buying a jet card versus doing on-demand chartering?
I need a charter bus with full ADA accessibility; how common is that in the professional transport industry?

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 charter buyers.

Behavior rates across 40 charter buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional58%40%20%45%
Suggests DIY first8%10%5%90%
Names specific providers13%23%23%78%
Gives price or cost info25%38%30%73%
Tells to check reviews15%13%8%85%
Tells to verify credentials23%18%15%80%
Mentions case studies / portfolio0%0%0%100%
Mentions local proximity13%18%5%75%
Gives selection criteria40%45%23%48%
Warns about red flags10%10%13%93%
Asks a clarifying question63%70%0%18%
Recommends multiple quotes8%5%0%88%

By model

How each assistant handled Charter questions.

Reading the 120 answers model by model shows how differently the three assistants treat the same charter questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 57.5% (ChatGPT) down to 20% (Gemini), a 38-point gap on an identical question set.

Across the 40 charter answers it produced, ChatGPT recommended hiring a professional in 57.5% of them and suggested a DIY approach first 7.5% of the time. It named a specific provider in 12.5% of answers (about 0.6 distinct providers per answer) and included price or cost information 25% of the time. ChatGPT asked a clarifying question before answering in 62.5% of cases, warned about red flags or scams in 10%, and told the buyer to verify credentials in 22.5%, averaging 510 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 12.5%; a selection-criteria checklist appeared in 40% of its answers and a recommendation to gather multiple quotes in 7.5%.

Across the 40 charter answers it produced, Claude recommended hiring a professional in 40% of them and suggested a DIY approach first 10% of the time. It named a specific provider in 22.5% of answers (about 0.8 distinct providers per answer) and included price or cost information 37.5% of the time. Claude asked a clarifying question before answering in 70% of cases, warned about red flags or scams in 10%, and told the buyer to verify credentials in 17.5%, averaging 281 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 17.5%; a selection-criteria checklist appeared in 45% of its answers and a recommendation to gather multiple quotes in 5%.

Across the 40 charter answers it produced, Gemini recommended hiring a professional in 20% of them and suggested a DIY approach first 5% of the time. It named a specific provider in 22.5% of answers (about 0.8 distinct providers per answer) and included price or cost information 30% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 12.5%, and told the buyer to verify credentials in 15%, averaging 252 words per answer. On the remaining cues it told the buyer to check reviews in 7.5%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 5%; a selection-criteria checklist appeared in 22.5% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a charter buyer to a professional (57.5%) and Gemini the least (20%). ChatGPT produced the longest answers, at 510 words on average. Specific providers were named most often by Claude (22.5%) — even there, roughly one answer in 4 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 18.3 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a charter buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 70% (Claude) — a 70-point spread.
  • Recommends hiring a professional: from 20% (Gemini) to 57.5% (ChatGPT) — a 38-point spread.
  • Gives selection criteria: from 22.5% (Gemini) to 45% (Claude) — a 23-point spread.
  • Gives price or cost information: from 25% (ChatGPT) to 37.5% (Claude) — a 13-point spread.
  • Mentions local proximity: from 5% (Gemini) to 17.5% (Claude) — a 13-point spread.

The widest single gap — asks a clarifying question, 70 points — means a charter 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 charter market.

Where they agree

The points of near-consensus in Charter.

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

  • Mentions case studies or portfolio: 0% across all three models.
  • Warns about red flags or scams: 10%–12.5% across all three (a 3-point spread).
  • Suggests a DIY approach first: 5%–10% across all three (a 5-point spread).
  • Tells the buyer to check reviews: 7.5%–15% across all three (a 8-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" (17.5%).

Every behavior, measured

All twelve coded behaviors for Charter, averaged across the three models.

The behaviors AI models reproduce most often for charter are asks a clarifying question (44.2% on average), recommends hiring a professional (39.2%) and gives selection criteria (35.8%); the rarest are mentions case studies or portfolio (0%), recommends multiple quotes (4.2%) and suggests a DIY approach first (7.5%). 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:

  • Asks a clarifying question: 44.2% on average (ChatGPT 62.5%, Claude 70%, Gemini 0%) — a 70-point spread.
  • Recommends hiring a professional: 39.2% on average (ChatGPT 57.5%, Claude 40%, Gemini 20%) — a 38-point spread.
  • Gives selection criteria: 35.8% on average (ChatGPT 40%, Claude 45%, Gemini 22.5%) — a 23-point spread.
  • Gives price or cost information: 30.8% on average (ChatGPT 25%, Claude 37.5%, Gemini 30%) — a 13-point spread.
  • Names a specific provider: 19.2% on average (ChatGPT 12.5%, Claude 22.5%, Gemini 22.5%) — a 10-point spread.
  • Tells the buyer to verify credentials: 18.3% on average (ChatGPT 22.5%, Claude 17.5%, Gemini 15%) — a 8-point spread.
  • Tells the buyer to check reviews: 11.7% on average (ChatGPT 15%, Claude 12.5%, Gemini 7.5%) — a 8-point spread.
  • Mentions local proximity: 11.7% on average (ChatGPT 12.5%, Claude 17.5%, Gemini 5%) — a 13-point spread.
  • Warns about red flags or scams: 10.8% on average (ChatGPT 10%, Claude 10%, Gemini 12.5%) — a 3-point spread.
  • Suggests a DIY approach first: 7.5% on average (ChatGPT 7.5%, Claude 10%, Gemini 5%) — a 5-point spread.
  • Recommends multiple quotes: 4.2% on average (ChatGPT 7.5%, Claude 5%, Gemini 0%) — a 8-point spread.
  • Mentions case studies or portfolio: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).

Trust signals

How well the models protect the charter buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the charter buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 11.7% of answers on average. Verifying credentials or certifications appeared in 18.3%. Warning about red flags or scams appeared in 10.8%.

On structuring the decision, a selection-criteria checklist showed up in 35.8% of answers on average and a recommendation to gather multiple quotes in 4.2%. The single least-reproduced protective signal for charter is "recommends multiple quotes" 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.

Referral behavior

Do AI models name Charter providers?

For service providers the decisive question is whether these systems name anyone at all. Across 120 charter answers, a specific provider was named in 19.2% of responses on average — roughly 0.7 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for charter: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 40 Charter questions cover.

The 40 questions behind every percentage on this page were drawn from real charter (professional 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 charter 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 charter 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 →