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llms.txt GeneratorHigh-divergence prompts make up 14.6% of the study
14.6% of prompts fall into the high-divergence bucket in the model divergence benchmark.
A meaningful minority of prompts split the models sharply.
The numbers behind it.
- High divergence rate
- Prompts in the 0-25% agreement bucket.
- 14.6%
The evidence.
| Measure | Result |
|---|---|
| High divergence rate Prompts in the 0-25% agreement bucket. | 14.6% |
Population and method
Built from 797,644 valid comparisons across 44,088 reports and 8 models, covering 6,439,133 model responses in the observed window.
Where the evidence stops
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”.
What to do with this
These are often the highest-opportunity prompts for competitive share shifts.
Cite this fact.
Source study: Model Divergence“14.6% of prompts fall into the high-divergence bucket in the model divergence benchmark.”
Same Question, Different AI, Different Answers - Trakkr Research · High-divergence prompts make up 14.6% of the study
Same Question, Different AI, Different Answers · Trakkr Research · Updated 11 March 2026