Original research · 2026-07 edition

AI SEO Statistics: Debt Counseling (2026-07 edition)

15 questions · 45 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 debt counseling.

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

I'm over $30,000 in credit card debt and barely making minimum payments; should I look for a debt counselor or consider bankruptcy?
What is the actual difference between a non-profit credit counseling agency and a for-profit debt settlement company?
Will working with a debt counselor hurt my credit score more than just continuing to miss payments?
Can a debt counselor help me lower my interest rates even if my accounts aren't in collections yet?
I’ve seen ads for debt relief, but how do I verify if a counselor is actually accredited by the NFCC or FCAA?
How much of a monthly service fee is considered normal for a debt management plan?
I'm worried about my privacy; what specific information does a debt counselor share with my bank or my employer?
If I hire a debt counselor, do they take over my bank account or do I still pay my bills myself?
Show all 15 questions
Are there specific debt counseling services for people struggling with both high student loans and medical bills?
What questions should I ask during a first consultation to make sure the counselor isn't just trying to sell me a high-interest loan?
Can I still qualify for a mortgage in a few years if I enter a debt management program today?
Is it possible to negotiate a lower payoff amount with my creditors on my own, or do counselors have special access I don't?
I just received a wage garnishment notice; is it too late to start working with a debt counseling service to stop it?
What happens to my credit cards if I sign up for counseling; do I have to close every single account immediately?
Are there any local debt counseling offices in my area that offer in-person sessions instead of just doing everything over the phone?

Model by model

33-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 debt counseling buyers.

Behavior rates across 15 debt counseling buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional73%80%40%47%
Suggests DIY first27%7%7%67%
Names specific providers40%7%27%47%
Gives price or cost info13%47%20%47%
Tells to check reviews0%20%0%80%
Tells to verify credentials40%60%33%33%
Mentions case studies / portfolio0%0%0%100%
Mentions local proximity27%27%20%47%
Gives selection criteria53%60%27%20%
Warns about red flags33%47%27%33%
Asks a clarifying question60%60%7%20%
Recommends multiple quotes13%13%0%73%

By model

How each assistant handled Debt Counseling questions.

Reading the 45 answers model by model shows how differently the three assistants treat the same debt counseling questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 80% (Claude) down to 40% (Gemini), a 40-point gap on an identical question set.

Across the 15 debt counseling answers it produced, ChatGPT recommended hiring a professional in 73.3% of them and suggested a DIY approach first 26.7% of the time. It named a specific provider in 40% 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 60% of cases, warned about red flags or scams in 33.3%, and told the buyer to verify credentials in 40%, averaging 537 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 26.7%; a selection-criteria checklist appeared in 53.3% of its answers and a recommendation to gather multiple quotes in 13.3%.

Across the 15 debt counseling answers it produced, Claude recommended hiring a professional in 80% of them and suggested a DIY approach first 6.7% of the time. It named a specific provider in 6.7% of answers (about 0.1 distinct providers per answer) and included price or cost information 46.7% of the time. Claude asked a clarifying question before answering in 60% of cases, warned about red flags or scams in 46.7%, and told the buyer to verify credentials in 60%, averaging 292 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 26.7%; a selection-criteria checklist appeared in 60% of its answers and a recommendation to gather multiple quotes in 13.3%.

Across the 15 debt counseling answers it produced, Gemini recommended hiring a professional in 40% of them and suggested a DIY approach first 6.7% of the time. It named a specific provider in 26.7% of answers (about 0.7 distinct providers per answer) and included price or cost information 20% of the time. Gemini asked a clarifying question before answering in 6.7% of cases, warned about red flags or scams in 26.7%, and told the buyer to verify credentials in 33.3%, averaging 250 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 20%; a selection-criteria checklist appeared in 26.7% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, Claude is the assistant most likely to route a debt counseling buyer to a professional (80%) and Gemini the least (40%). ChatGPT produced the longest answers, at 537 words on average. Specific providers were named most often by ChatGPT (40%) — even there, roughly one answer in 3 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 32.6 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a debt counseling buyer happens to ask matters most:

  • Asks a clarifying question: from 6.7% (Gemini) to 60% (ChatGPT) — a 53-point spread.
  • Recommends hiring a professional: from 40% (Gemini) to 80% (Claude) — a 40-point spread.
  • Gives price or cost information: from 13.3% (ChatGPT) to 46.7% (Claude) — a 33-point spread.
  • Names a specific provider: from 6.7% (Claude) to 40% (ChatGPT) — a 33-point spread.
  • Gives selection criteria: from 26.7% (Gemini) to 60% (Claude) — a 33-point spread.

The widest single gap — asks a clarifying question, 53 points — means a debt counseling 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 debt counseling market.

Where they agree

The points of near-consensus in Debt Counseling.

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

  • Mentions case studies or portfolio: 0% across all three models.
  • Mentions local proximity: 20%–26.7% across all three (a 7-point spread).
  • Recommends multiple quotes: 0%–13.3% across all three (a 13-point spread).
  • Suggests a DIY approach first: 6.7%–26.7% across all three (a 20-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" (20%).

Every behavior, measured

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

The behaviors AI models reproduce most often for debt counseling are recommends hiring a professional (64.4% on average), gives selection criteria (46.7%) and tells the buyer to verify credentials (44.4%); the rarest are mentions case studies or portfolio (0%), tells the buyer to check reviews (6.7%) and recommends multiple quotes (8.9%). Each figure below is the share of a model's 15 answers in which the behavior appeared at least once, averaged across the 3 models with the full per-model range in parentheses:

  • Recommends hiring a professional: 64.4% on average (ChatGPT 73.3%, Claude 80%, Gemini 40%) — a 40-point spread.
  • Gives selection criteria: 46.7% on average (ChatGPT 53.3%, Claude 60%, Gemini 26.7%) — a 33-point spread.
  • Tells the buyer to verify credentials: 44.4% on average (ChatGPT 40%, Claude 60%, Gemini 33.3%) — a 27-point spread.
  • Asks a clarifying question: 42.2% on average (ChatGPT 60%, Claude 60%, Gemini 6.7%) — a 53-point spread.
  • Warns about red flags or scams: 35.6% on average (ChatGPT 33.3%, Claude 46.7%, Gemini 26.7%) — a 20-point spread.
  • Gives price or cost information: 26.7% on average (ChatGPT 13.3%, Claude 46.7%, Gemini 20%) — a 33-point spread.
  • Names a specific provider: 24.5% on average (ChatGPT 40%, Claude 6.7%, Gemini 26.7%) — a 33-point spread.
  • Mentions local proximity: 24.5% on average (ChatGPT 26.7%, Claude 26.7%, Gemini 20%) — a 7-point spread.
  • Suggests a DIY approach first: 13.4% on average (ChatGPT 26.7%, Claude 6.7%, Gemini 6.7%) — a 20-point spread.
  • Recommends multiple quotes: 8.9% on average (ChatGPT 13.3%, Claude 13.3%, Gemini 0%) — a 13-point spread.
  • Tells the buyer to check reviews: 6.7% on average (ChatGPT 0%, Claude 20%, Gemini 0%) — a 20-point spread.
  • Mentions case studies or portfolio: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).

Trust signals

How well the models protect the debt counseling buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the debt counseling 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 44.4%. Warning about red flags or scams appeared in 35.6%.

On structuring the decision, a selection-criteria checklist showed up in 46.7% of answers on average and a recommendation to gather multiple quotes in 8.9%. The single least-reproduced protective signal for debt counseling 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.

Referral behavior

Do AI models name Debt Counseling providers?

For service providers the decisive question is whether these systems name anyone at all. Across 45 debt counseling answers, a specific provider was named in 24.5% of responses on average — roughly 0.6 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for debt counseling: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 15 Debt Counseling questions cover.

The 15 questions behind every percentage on this page were drawn from real debt counseling (financial 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 debt counseling 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 15 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 debt counseling 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.

15 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 →