Do ChatGPT and Google pick the same brand in head-to-head comparisons?

ChatGPT Search and Google AI Mode picked the same winner in 375 of 426 matched comparisons, or 88.0282%. They picked different winners in 51 comparisons, or 11.9718%.

Broad brand-set disagreement does not imply that the engines usually reverse an explicit head-to-head decision. Audit selection and criterion-level winner agreement as separate measures. BrightEdge reported query-level brand-set disagreement across three AI platforms. This study narrows the measurement to the winner after the prompt, run, cleaned brand pair, and criterion all match.

At least one winner difference appeared in 50 of 386 answer pairs with a matched comparison, or 12.9534%. The other 336 answer pairs had no winner difference. The median answer pair supplied one matched comparison, and the maximum was three.

The comparison-level and answer-pair rates tell the same story because most answer pairs supplied one eligible decision. Teams should still keep the prompt, run, pair, and criterion attached to each result.

The engines picked different feature winners in 12 of 57 comparisons, or 21.0526%. The rate was 12 of 71 for performance, or 16.9014%; 12 of 107 for ease of use, or 11.2150%; and 13 of 171 for price, or 7.6023%.

A cross-engine audit should preserve the criterion. The aggregate rate can hide a 13.4503-point difference between feature and price decisions. The result does not show why an engine chose a winner.

Features supplied 27,609 of 67,223 cleaned six-criterion statements, or 41.0708%. Price supplied 12,914, or 19.2107%; ease of use 10,861, or 16.1567%; performance 10,124, or 15.0603%; support 3,628, or 5.3970%; and security and privacy 2,087, or 3.1046%.

The matched denominator reflects where both engines made the same kind of explicit comparison. It is not a balanced experiment with equal criterion volume.

Atlassian and Linear appeared together in 366 cleaned comparison statements across 353 AI answers and 76 organic prompts. DraftKings and FanDuel followed with 311 statements across 258 answers and 74 prompts. The table lists the ten highest-volume cleaned pairs.

Use the leaderboard to find comparison language worth reviewing. It ranks observed comparison volume, not brand quality, winner agreement, or buyer demand.

Among classified industries with at least 20 matched comparisons, Internet Services had four different winners in 36 comparisons, or 11.1111%. Information Technology had three of 31, or 9.6774%; Software had four of 48, or 8.3333%; and Data and Analytics had zero of 41.

Use industry cuts to prioritize review, not to infer an engine rule. These are descriptive slices of the observed prompt corpus, and each displayed sample remains small.

The main rate was 51 of 426, or 11.9718%. Exact brand identities returned 44 of 404, or 10.8911%. A confidence floor of 0.90 returned 18 of 206, or 8.7379%. Keeping original criterion labels separate returned 11 of 124, or 8.8710%.

Brand-family consolidation, confidence, and criterion grouping change the eligible sample but do not reverse the conclusion that the engines usually pick the same explicit winner.

The observed cut contained 174,449 directional comparison statements. Cleaning and grouping produced 67,223 statements across the six public criteria and 40,855 unambiguous answer-level verdicts. Exactly 426 comparisons then matched across engine, prompt, run, cleaned brand pair, and criterion.

The narrow denominator is the point of the design. It prevents broad query-level disagreement, unmatched criteria, ambiguous language, duplicate answer cells, and product identity noise from being presented as winner reversal. It also means the headline should not be generalized to every AI answer.

The engines picked the same explicit winner in 375 of 426 matched comparisons. Feature winner disagreement was 21.0526%, compared with 7.6023% for price.

Track brand inclusion, recommendation position, and explicit comparison winners separately. Compare the same prompt on both engines. Store the brand pair and criterion with each decision. Review feature comparisons first, then check the source and factual basis before changing positioning. Repeat the fixed-window method next quarter before calling a difference movement.