Same Question, Different AI, Different Answers | Trakkr Research

Agreement rates, disagreement by query type, and model-pair overlap. Answer pages, reference facts, and live trackers drawn from this study.

Methodology: Derived from Same Question, Different AI, Different Answers and updated March 11, 2026.

What this hub contains

Agreement rates, disagreement by query type, and model-pair overlap. Answer pages, reference facts, and live trackers drawn from this study.

Answer Pages

Narrow questions answered directly from the study.

• Do AI models recommend the same brands? - Not usually. Average agreement across the study is only 43.3%, and only 4.0% of prompts produced perfect agreement across all models tested.
• How often is there perfect consensus across models? - Rarely. Only 4.0% of prompts produced unanimous agreement across all 8 models in the study.
• How much do models disagree on brand recommendations? - A lot. 14.6% of prompts fall into the high-divergence bucket, and average agreement is still only 43.3% even when measured across a large, cleaned comparison set.
• Which query types produce the most consensus? - Comparison queries produce the most consensus in the study, averaging 50.4% agreement. More open-ended general and best-of prompts are less stable.
• Are general and best-of prompts more volatile than comparisons? - Yes. Comparison prompts average 50.4% agreement, while general prompts average 42.2% and best-of prompts carry a 14.8% high-divergence rate.
• What does an average top-three overlap of 2.8 mean? - It means models overlap meaningfully but not completely. On average, the top-three recommendation sets share 2.8 entries, which still leaves enough room for important ranking and inclusion differences.
• Should you use one model as a proxy for all AI visibility? - No. With only 43.3% average agreement and 4.0% perfect consensus, one model is an unreliable proxy for the wider AI market.
• Why do models disagree so much even on common categories? - Because they prioritize different evidence sets, training priors, and retrieval habits. The output looks like one market, but the study shows 8 distinct recommendation systems with only partial overlap.
• What is the operational cost of model divergence? - The cost is that one visibility report cannot stand in for the whole market. A brand may gain or lose share on one model without seeing the same move elsewhere.
• Which metrics best summarize cross-model disagreement? - The clearest summary metrics are average agreement, perfect agreement, and the share of high-divergence prompts. In this study those land at 43.3%, 4.0%, and 14.6% respectively.
• What should brands do when models disagree? - Brands should treat divergence as the default condition. That means tracking multiple models, watching query classes separately, and using cross-model data to find where visibility is actually portable.
• Why are comparison queries the most stable query class? - Because they constrain the answer space more than open-ended best-of or general prompts. In the study, comparison queries reached 50.4% average agreement, the highest of the tracked query families.

Reference Facts

Short, quotable claims with metrics and methodology context.

• Average cross-model agreement is only 43.3% - Agreement is meaningfully below the level most teams assume.
• Only 4.0% of prompts produce perfect consensus - An analysis of eight major artificial intelligence models including OpenAI, Anthropic, Gemini, Grok, Deepseek, Meta, Perplexity, and Google AI Overviews reveals significant variance in output generation. When presented with identical prompts, the models generated unanimous responses in only a marginal fraction of cases.
• More than 700,000 valid comparisons power the study - This is a large comparison set, not a handful of anecdotal prompts.
• High-divergence prompts make up 14.6% of the study - A meaningful minority of prompts split the models sharply.
• Comparison prompts are the most stable query class - The study 'Same Question, Different AI, Different Answers' evaluated response stability across multiple AI models and identified comparison prompts as the most consistent query class.
• General prompts are less stable than comparisons - The study Same Question, Different AI, Different Answers evaluated the stability of general prompts compared to comparative prompts across multiple artificial intelligence models.
• Best-of prompts carry a high-divergence tail - The study Same Question, Different AI, Different Answers evaluated model consistency and found that best-of prompts frequently split models.
• Average top-three overlap is 2.8 - The 'Same Question, Different AI, Different Answers' study evaluates the consistency of AI model outputs by measuring the average overlap among the top three results generated by different models.

Trackers

Live benchmark views built from the study’s most reusable dimensions.

• Agreement by query class - Cross-model agreement benchmark across major prompt families.
• Cross-model consensus benchmark - Top-line agreement metrics across the full 8-model comparison set.