AI SEO Statistics: Crypto (2026-07 edition)

Reading the 45 answers model by model shows how differently the three assistants treat the same crypto questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 80% (Claude) down to 66.7% (Gemini), a 13-point gap on an identical question set.

Across the 15 crypto answers it produced, ChatGPT recommended hiring a professional in 73.3% of them and suggested a DIY approach first 13.3% of the time. It named a specific provider in 26.7% of answers (about 0.9 distinct providers per answer) and included price or cost information 13.3% of the time. ChatGPT asked a clarifying question before answering in 46.7% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 26.7%, averaging 780 words per answer. On the remaining cues it told the buyer to check reviews in 6.7%, pointed to case studies or a portfolio in 20%, and framed the choice around local proximity in 0%; a selection-criteria checklist appeared in 46.7% of its answers and a recommendation to gather multiple quotes in 26.7%.

Across the 15 crypto answers it produced, Claude recommended hiring a professional in 80% of them and suggested a DIY approach first 0% of the time. It named a specific provider in 33.3% of answers (about 1.8 distinct providers per answer) and included price or cost information 20% of the time. Claude asked a clarifying question before answering in 53.3% of cases, warned about red flags or scams in 26.7%, and told the buyer to verify credentials in 20%, averaging 347 words per answer. On the remaining cues it told the buyer to check reviews in 13.3%, pointed to case studies or a portfolio in 33.3%, and framed the choice around local proximity in 0%; a selection-criteria checklist appeared in 60% of its answers and a recommendation to gather multiple quotes in 6.7%.

Across the 15 crypto answers it produced, Gemini recommended hiring a professional in 66.7% of them and suggested a DIY approach first 6.7% of the time. It named a specific provider in 46.7% of answers (about 2.5 distinct providers per answer) and included price or cost information 26.7% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 6.7%, and told the buyer to verify credentials in 0%, averaging 238 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 6.7%, and framed the choice around local proximity in 0%; a selection-criteria checklist appeared in 60% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, Claude is the assistant most likely to route a crypto buyer to a professional (80%) and Gemini the least (66.7%). ChatGPT produced the longest answers, at 780 words on average. Specific providers were named most often by Gemini (46.7%) — even there, roughly one answer in 2 carried a name.

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

• Mentions local proximity: 0% across all three models.
• Recommends hiring a professional: 66.7%–80% across all three (a 13-point spread).
• Suggests a DIY approach first: 0%–13.3% across all three (a 13-point spread).
• Tells the buyer to check reviews: 0%–13.3% across all three (a 13-point spread).

Measured question by question, the three assistants coded a response the same way most consistently on "mentions local proximity" (identical coding in 100% of questions) and least consistently on "asks a clarifying question" (40%).

Beyond whether to hire, the rubric codes how carefully each assistant protects the crypto buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 6.7% of answers on average. Verifying credentials or certifications appeared in 15.6%. Warning about red flags or scams appeared in 17.8%.

On structuring the decision, a selection-criteria checklist showed up in 55.6% of answers on average and a recommendation to gather multiple quotes in 11.1%. The single least-reproduced protective signal for crypto is "tells the buyer to check reviews" at 6.7% on average — the clearest opening for content that supplies it, since the models are not yet reliably surfacing that guidance on their own.

The 15 questions behind every percentage on this page were drawn from real crypto (technology / SaaS; 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 crypto 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.