Original research · 2026-07 edition

AI SEO Statistics: Packaging (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 packaging.

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

What are the most durable packaging options for heavy industrial machinery parts?
How do I find a packaging manufacturer that specializes in sustainable, plastic-free materials?
Is it cheaper to buy wholesale stock boxes or get custom-sized ones made for my product?
What should I look for in a packaging supplier's ISO certification to ensure quality?
How much does a custom die-cut tool typically cost for a new box design?
My current boxes are crushing during stacking; do I need a higher ECT rating or double-wall corrugated?
Can you explain the pros and cons of offset printing vs. digital printing for bulk retail packaging?
What questions should I ask a packaging manufacturer to ensure they can handle a sudden 50% increase in order volume?
Show all 40 questions
I'm looking for a manufacturer in the Midwest to reduce shipping costs; how do I vet their lead times?
What are the red flags when reviewing a quote from a new packaging vendor?
How do I calculate the total cost of ownership for custom packaging including storage and assembly?
We're launching a subscription box; what's the best material to keep the weight down while looking premium?
Does anyone still do small-batch custom corrugated runs or is the MOQ usually over 1,000?
How do I know if a packaging company is actually eco-friendly or just greenwashing their materials?
What are the typical lead times for a first-time order of custom-printed folding cartons?
Can a packaging manufacturer help with the structural engineering of a box to minimize material waste?
What's the difference between a broker and a direct manufacturer when sourcing industrial packaging?
We have a lot of product breakage in overseas shipping; what protective inserts do you recommend?
How do fluctuations in paper pulp prices affect my long-term contract with a box manufacturer?
What are the standard payment terms in the packaging industry for a mid-sized B2B buyer?
I need a food-grade packaging supplier; what specific FDA compliance documents should I request?
Is it worth paying for a prototype or physical sample before committing to a 5,000-unit production run?
What are the most common hidden fees in a packaging manufacturing contract?
How do I transition from hand-taping boxes to an automated packaging line with a supplier's help?
What's the best way to compare quotes from three different packaging companies that use different terminology?
Can I get custom packaging that is both waterproof and fully recyclable?
What are the signs that my packaging supplier is secretly outsourcing my order to a third party?
How does the weight of the packaging material affect my overall freight costs for heavy goods?
I have a limited budget for a product launch; where can I compromise on packaging quality without hurting the brand?
What's the impact of choosing gloss vs. matte finish on the scuff-resistance of the outer print?
Are there packaging manufacturers that offer inventory management or JIT delivery to save on warehouse space?
How do I verify a supplier's claims about the percentage of post-consumer recycled content in their board?
What's the risk of using a cheaper overseas packaging manufacturer versus a local domestic one?
Why is my current supplier charging me a set-up fee every single time I reorder the same design?
What kind of drop testing or ISTA certification should I require for high-value electronics packaging?
How do I find a packaging partner that can handle both the primary and secondary packaging for a medical device?
What are the latest trends in frustration-free packaging for e-commerce brands looking to reduce returns?
If I provide my own artwork, what specific file formats and bleed specs do packaging printers usually need?
How do I negotiate better rates with my current box supplier if my volume has doubled this year?
What specific metrics should be included in a Service Level Agreement for a long-term packaging partnership?

Model by model

19-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 packaging buyers.

Behavior rates across 40 packaging buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional43%25%15%70%
Suggests DIY first35%18%8%70%
Names specific providers10%8%10%85%
Gives price or cost info10%20%25%65%
Tells to check reviews3%5%0%95%
Tells to verify credentials25%23%15%70%
Mentions case studies / portfolio3%5%0%95%
Mentions local proximity8%15%8%75%
Gives selection criteria38%50%33%43%
Warns about red flags13%13%18%83%
Asks a clarifying question48%70%3%20%
Recommends multiple quotes5%8%5%88%

By model

How each assistant handled Packaging questions.

Reading the 120 answers model by model shows how differently the three assistants treat the same packaging questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 42.5% (ChatGPT) down to 15% (Gemini), a 28-point gap on an identical question set.

Across the 40 packaging answers it produced, ChatGPT recommended hiring a professional in 42.5% of them and suggested a DIY approach first 35% of the time. It named a specific provider in 10% of answers (about 0.2 distinct providers per answer) and included price or cost information 10% of the time. ChatGPT asked a clarifying question before answering in 47.5% of cases, warned about red flags or scams in 12.5%, and told the buyer to verify credentials in 25%, averaging 694 words per answer. On the remaining cues it told the buyer to check reviews in 2.5%, pointed to case studies or a portfolio in 2.5%, and framed the choice around local proximity in 7.5%; a selection-criteria checklist appeared in 37.5% of its answers and a recommendation to gather multiple quotes in 5%.

Across the 40 packaging answers it produced, Claude recommended hiring a professional in 25% of them and suggested a DIY approach first 17.5% of the time. It named a specific provider in 7.5% of answers (about 0.4 distinct providers per answer) and included price or cost information 20% of the time. Claude asked a clarifying question before answering in 70% of cases, warned about red flags or scams in 12.5%, and told the buyer to verify credentials in 22.5%, averaging 323 words per answer. On the remaining cues it told the buyer to check reviews in 5%, pointed to case studies or a portfolio in 5%, and framed the choice around local proximity in 15%; a selection-criteria checklist appeared in 50% of its answers and a recommendation to gather multiple quotes in 7.5%.

Across the 40 packaging answers it produced, Gemini recommended hiring a professional in 15% of them and suggested a DIY approach first 7.5% of the time. It named a specific provider in 10% of answers (about 0.3 distinct providers per answer) and included price or cost information 25% of the time. Gemini asked a clarifying question before answering in 2.5% of cases, warned about red flags or scams in 17.5%, and told the buyer to verify credentials in 15%, averaging 255 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 7.5%; a selection-criteria checklist appeared in 32.5% of its answers and a recommendation to gather multiple quotes in 5%.

Taken together, ChatGPT is the assistant most likely to route a packaging buyer to a professional (42.5%) and Gemini the least (15%). ChatGPT produced the longest answers, at 694 words on average. Specific providers were named most often by ChatGPT (10%) — even there, roughly one answer in 10 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 19 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a packaging buyer happens to ask matters most:

  • Asks a clarifying question: from 2.5% (Gemini) to 70% (Claude) — a 68-point spread.
  • Recommends hiring a professional: from 15% (Gemini) to 42.5% (ChatGPT) — a 28-point spread.
  • Suggests a DIY approach first: from 7.5% (Gemini) to 35% (ChatGPT) — a 28-point spread.
  • Gives selection criteria: from 32.5% (Gemini) to 50% (Claude) — a 18-point spread.
  • Gives price or cost information: from 10% (ChatGPT) to 25% (Gemini) — a 15-point spread.

The widest single gap — asks a clarifying question, 68 points — means a packaging 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 packaging market.

Where they agree

The points of near-consensus in Packaging.

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

  • Names a specific provider: 7.5%–10% across all three (a 3-point spread).
  • Recommends multiple quotes: 5%–7.5% across all three (a 3-point spread).
  • Tells the buyer to check reviews: 0%–5% across all three (a 5-point spread).
  • Mentions case studies or portfolio: 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 "tells the buyer to check reviews" (identical coding in 95% of questions) and least consistently on "asks a clarifying question" (20%).

Every behavior, measured

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

The behaviors AI models reproduce most often for packaging are gives selection criteria (40% on average), asks a clarifying question (40%) and recommends hiring a professional (27.5%); the rarest are mentions case studies or portfolio (2.5%), tells the buyer to check reviews (2.5%) and recommends multiple quotes (5.8%). 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:

  • Gives selection criteria: 40% on average (ChatGPT 37.5%, Claude 50%, Gemini 32.5%) — a 18-point spread.
  • Asks a clarifying question: 40% on average (ChatGPT 47.5%, Claude 70%, Gemini 2.5%) — a 68-point spread.
  • Recommends hiring a professional: 27.5% on average (ChatGPT 42.5%, Claude 25%, Gemini 15%) — a 28-point spread.
  • Tells the buyer to verify credentials: 20.8% on average (ChatGPT 25%, Claude 22.5%, Gemini 15%) — a 10-point spread.
  • Suggests a DIY approach first: 20% on average (ChatGPT 35%, Claude 17.5%, Gemini 7.5%) — a 28-point spread.
  • Gives price or cost information: 18.3% on average (ChatGPT 10%, Claude 20%, Gemini 25%) — a 15-point spread.
  • Warns about red flags or scams: 14.2% on average (ChatGPT 12.5%, Claude 12.5%, Gemini 17.5%) — a 5-point spread.
  • Mentions local proximity: 10% on average (ChatGPT 7.5%, Claude 15%, Gemini 7.5%) — a 8-point spread.
  • Names a specific provider: 9.2% on average (ChatGPT 10%, Claude 7.5%, Gemini 10%) — a 3-point spread.
  • Recommends multiple quotes: 5.8% on average (ChatGPT 5%, Claude 7.5%, Gemini 5%) — a 3-point spread.
  • Tells the buyer to check reviews: 2.5% on average (ChatGPT 2.5%, Claude 5%, Gemini 0%) — a 5-point spread.
  • Mentions case studies or portfolio: 2.5% on average (ChatGPT 2.5%, Claude 5%, Gemini 0%) — a 5-point spread.

Trust signals

How well the models protect the packaging buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the packaging buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 2.5% of answers on average. Verifying credentials or certifications appeared in 20.8%. Warning about red flags or scams appeared in 14.2%.

On structuring the decision, a selection-criteria checklist showed up in 40% of answers on average and a recommendation to gather multiple quotes in 5.8%. The single least-reproduced protective signal for packaging is "tells the buyer to check reviews" at 2.5% 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 Packaging providers?

For service providers the decisive question is whether these systems name anyone at all. Across 120 packaging answers, a specific provider was named in 9.2% of responses on average — roughly 0.3 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for packaging: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 40 Packaging questions cover.

The 40 questions behind every percentage on this page were drawn from real packaging (manufacturing / industrial B2B; 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 packaging 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 packaging 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 →