{
  "kind": "answer",
  "studySlug": "model-divergence",
  "slug": "why-are-comparison-queries-the-most-stable-query-class",
  "title": "Why are comparison queries the most stable query class?",
  "description": "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.",
  "lastUpdated": "2026-03-11",
  "lastTested": "2026-03-11",
  "sourceStudyUrl": "/trakkr-research/model-divergence",
  "sourceStudyTitle": "Same Question, Different AI, Different Answers",
  "claimIds": [
    "model-divergence:comparison-agreement",
    "model-divergence:general-agreement"
  ],
  "relatedSlugs": [
    "answer:do-ai-models-recommend-the-same-brands",
    "answer:how-often-is-there-perfect-consensus-across-models",
    "fact:high-divergence-prompts-make-up-fourteen-point-six-percent-of-the-study",
    "tracker:cross-model-consensus-tracker"
  ],
  "methodologySummary": "Built from 797,644 valid comparisons across 44,088 reports and 8 models, covering 6,439,133 model responses in the observed window.",
  "limitations": [
    "Agreement is measured across recommendation outputs, not across hidden reasoning or retrieval context.",
    "Average agreement can hide large differences between query classes and model pairs.",
    "The study measures overlap, not which answer was objectively “right”."
  ],
  "keywords": [
    "model divergence",
    "AI agreement",
    "ChatGPT vs Claude",
    "Gemini vs Perplexity",
    "comparison query stability",
    "stable AI query classes"
  ],
  "schemaHints": {
    "pageType": "Article",
    "includeDataset": true
  },
  "question": "Why are comparison queries the most stable query class?",
  "directAnswer": "Mostly, because they constrain the answer space more than open-ended best-of or general prompts. Comparison queries reached 50.4% average agreement, the highest of the tracked query families.",
  "answerSummary": "Narrower prompts reduce the number of plausible answer sets and force models closer together, meaning operators can expect more consistent visibility for comparison-based content.",
  "keyFacts": [
    {
      "label": "Comparison-query agreement",
      "value": "50.4%",
      "detail": "Comparison prompts produce the highest average agreement.",
      "claimId": "model-divergence:comparison-agreement"
    },
    {
      "label": "General-query agreement",
      "value": "42.2%",
      "detail": "General prompts are less stable across models.",
      "claimId": "model-divergence:general-agreement"
    }
  ],
  "evidenceTable": [
    {
      "label": "Comparison-query agreement",
      "value": "50.4%",
      "note": "Comparison prompts produce the highest average agreement."
    },
    {
      "label": "General-query agreement",
      "value": "42.2%",
      "note": "General prompts are less stable across models."
    }
  ],
  "whyItMatters": "Understanding query stability allows teams to allocate resources effectively, prioritizing content formats that either lock in consensus or exploit high-variance opportunities in the market.",
  "whatToDo": [
    "Track visibility across multiple models instead of using one platform as a proxy for the whole market.",
    "Prioritize query classes where disagreement is highest because that is where share can move fastest.",
    "Treat consensus as a benchmark, but treat divergence as the operating reality."
  ],
  "faqs": [
    {
      "question": "What is the average agreement rate for comparison queries?",
      "answer": "Comparison queries reached a 50.4% average agreement rate across models."
    },
    {
      "question": "How do general queries perform compared to comparison queries?",
      "answer": "General prompts are less stable across models, showing a lower agreement rate of 42.2%."
    }
  ]
}