How often the AI assistants agree on the same recommendation
A running measure of whether ChatGPT, Claude, Gemini and Perplexity converge on the same top pick — or send travellers to different places.
As of September 4, 2026, the AI assistants agreed on the #1 recommendation 16% of the time — across 2,459 questions where at least 3 of the 4 assistants gave a clear top pick, spanning 43 tracked destinations.
A "comparison" is one tracked question where at least 3 of the 4 assistants returned a clear top recommendation on their most recent run; it "agrees" when every one of them named the same place. The quorum (rather than "all 4") keeps a provider outage from zeroing the metric — it shrinks the sample instead. How we track this.
The tracked trend
Last 4 checks · agreement has declined since Aug 2026. This series is the stricter all-4 measure frozen at each check close, so it reads lower than the headline quorum figure — it's the line to watch for direction, not the absolute number.
Which assistants answered
How many of the 2459 comparable questions each assistant actually gave a top pick on.
Where the assistants disagree most
Tracked destinations with the lowest cross-model agreement (at least 4 comparable questions each).
- Ohio 0% agree
The assistants landed on the same top pick in 0 of 17 comparable questions.
- Nevada 5% agree
The assistants landed on the same top pick in 3 of 60 comparable questions.
- North Carolina 5% agree
The assistants landed on the same top pick in 3 of 60 comparable questions.
- California 7% agree
The assistants landed on the same top pick in 4 of 59 comparable questions.
- Iowa 8% agree
The assistants landed on the same top pick in 5 of 60 comparable questions.
- Mississippi 8% agree
The assistants landed on the same top pick in 5 of 60 comparable questions.
- Delaware 9% agree
The assistants landed on the same top pick in 5 of 57 comparable questions.
- Arizona 10% agree
The assistants landed on the same top pick in 6 of 60 comparable questions.
Why this matters
If you ask four assistants the same travel question and get four different answers, no single one of them is really "the" AI recommendation for that destination — and a place that tops one model's list can be invisible on another. Tracking convergence over time is how you tell a stable AI consensus from noise, and it's the number most likely to move as the models retrain and change how they search.