ChatGPT vs Gemini vs Perplexity vs Claude: Who Names Brands?

They behave like four different channels. On the same 35 unbranded buyer questions, Gemini named at least one tracked brand in 56% of answers and ChatGPT in 31%. Perplexity cited 14 sources per answer, ChatGPT about 2. A small brand that Perplexity named in 17% of answers was never named by Claude.

If you report "AI visibility" as one number, you're averaging four engines that disagree. This comparison shows how each one answers buyer questions, so you know what a change on each engine means.

Method: 35 unbranded and 15 branded buyer prompts × 4 engines × 2 runs on 2026-10-20, web search on, captured by BugViso's AI Visibility Tracker. The tracked brand is ours (bugviso.com, a young website-audit tool) against five established, anonymised competitors (A–E). Unless stated otherwise, tables use the 280 unbranded answers (70 per engine).

The Four Engines at a Glance

Query counts and latency are over all 100 answers per engine, branded prompts included.

Each engine has its own profile:

• ChatGPT gives short answers with very few sources and the fewest brand names.
• Gemini writes long, list-heavy answers and names the most brands.
• Perplexity gives short answers that rest on a very wide set of sources.
• Claude writes long answers that name many brands, and is the most likely to give no citation at all.

One Prompt, Four Answers

The profiles are easiest to see on a single question. We asked: "Which website audit tools let unused scans roll over to the next month?" Rollover is a real feature of our plans, so this is a prompt the brand should win. Here's how each engine opened its first-run answer (other vendors masked):

The same facts sat on the same public pricing page for all four engines. Two engines found it and led with it. Gemini framed the answer around the incumbents. Claude concluded the feature barely exists. That's the comparison in miniature: the engine, not the page, decided whether a real differentiator reached the buyer.

Dimension 1: How Many Brands Each Engine Recommends

A buyer question like "which website audit tools let unused scans roll over" can be answered with a list of vendors or with an explanation of how quotas work. The engines split sharply here.

Gemini and Claude recommend: half their answers name vendors, often several. ChatGPT and Perplexity explain: most of their answers describe what to look for and name vendors less often.

This matters for how you read visibility rates. A 10% mention rate on ChatGPT, which names few brands, is worth more competitively than 10% on Gemini, which names many. That's why we report share of voice next to mention rate. Our AI share of voice guide covers the arithmetic.

Dimension 2: Big Brands vs Small Brands

This is where the comparison gets uncomfortable for any challenger brand. Here is how often each engine named our brand and the most-named competitor on each engine:

On Perplexity, our young brand was named more often than any established competitor. That's the only engine where it led share of voice (25.0%). On Claude, it was never named in 70 unbranded answers. At a 95% Wilson interval that means a true rate of at most 5.2%.

Why the gap? The retrieval data gives a plausible explanation:

• Perplexity reads widely. It cited 365 distinct domains across 70 unbranded answers. A wide net reaches the niche pages, like our own pricing and feature pages, that describe a small brand's specific strengths.
• Gemini and Claude lean on what's established. They name many brands per answer, but the names come from comparison articles and the vendors those articles cover. Established vendors fill the list before a newcomer gets a slot.
• ChatGPT sits in between. It cites few sources, but 18% of its unbranded citations pointed to vendor sites, so a clear product page can earn one of its two slots.

💡 Challenger-brand rule: if you're new in a category, expect Perplexity to notice you first and Claude last. Plan third-party coverage (comparison pages, reviews, guides) for the engines that recommend from lists.

Dimension 3: Sources and Citations

Each engine cites a different slice of the web. Over the same prompts, their cited domains overlap by only 9–16% (full source comparison).

Two engines lean on vendor sites (ChatGPT, Perplexity). The other two lean on third-party pages (Gemini, Claude).

When an engine did name our brand in an unbranded answer, it cited our site every time (21 of 21). Being named and being cited go together on all four engines.

Dimension 4: Tone and Accuracy

When the brand was named (branded prompts included), the tracker scored sentiment 0–100 with a verbatim quote as evidence. It also checked each factual claim against the brand's own site.

Gemini was the warmest. Perplexity was the most accurate, which fits an engine that reads widely and cites closely. Accuracy is high everywhere, but every error was about pricing, plans or quotas. We cover that in the AI brand accuracy audit.

Dimension 5: Cost and Speed of Measuring Each Engine

If you track engines through their APIs, they also differ in cost. These are the costs BugViso's tracker recorded for one 50-prompt run on 2026-10-20, including search fees:

Gemini's cost is mostly search: its grounding is billed per search query (Gemini API pricing). Prices change, so treat this table as a dated snapshot.

Profile the Engines on Your Own Data

To compare engines for your own category, export answers as CSV (engine, answer, cited_urls). Then run this standard-library script with your brand and the competitors you care about:

#!/usr/bin/env python3
"""Profile how each AI engine answers: length, citations, brands named per answer.

Usage: python3 engine_profile.py answers.csv --brand "Acme" --competitor "Globex" ...
answers.csv columns: engine, answer, cited_urls (space-separated URLs).
"""
import argparse, csv, re, statistics
from collections import defaultdict
from urllib.parse import urlparse

def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("csv"); ap.add_argument("--brand", required=True)
    ap.add_argument("--competitor", action="append", default=[])
    a = ap.parse_args()
    names = [a.brand, *a.competitor]
    rx = {n: re.compile(r"(?<![\w-])" + re.escape(n) + r"(?![\w-])", re.I) for n in names}
    rows = defaultdict(list)
    with open(a.csv, newline="", encoding="utf-8") as f:
        for r in csv.DictReader(f):
            urls = r["cited_urls"].split()
            rows[r["engine"]].append({
                "words": len(r["answer"].split()),
                "cites": len(urls),
                "domains": {(urlparse(u).hostname or "").removeprefix("www.") for u in urls},
                "named": sum(1 for n in names if rx[n].search(r["answer"])),
            })
    print(f"{'engine':<12}{'answers':>8}{'median words':>14}{'mean cites':>12}{'0 cites':>9}"
          f"{'domains':>9}{'brands/answer':>15}")
    for e, rs in sorted(rows.items()):
        doms = set().union(*(x["domains"] for x in rs)) - {""}
        print(f"{e:<12}{len(rs):>8}{statistics.median(x['words'] for x in rs):>14.0f}"
              f"{statistics.mean(x['cites'] for x in rs):>12.1f}{sum(1 for x in rs if not x['cites']):>9}"
              f"{len(doms):>9}{statistics.mean(x['named'] for x in rs):>15.2f}")

if __name__ == "__main__":
    main()

python3 engine_profile.py unbranded.csv --brand "BugViso" \
  --competitor "Competitor A" --competitor "Competitor B" --competitor "Competitor C" \
  --competitor "Competitor D" --competitor "Competitor E"

Our 280 unbranded answers produced:

engine       answers  median words  mean cites  0 cites  domains  brands/answer
chatgpt           70           154         2.2        3       83           0.54
claude            70           376         4.3       17      194           1.40
gemini            70           548         4.7       11      203           1.43
perplexity        70           152        14.1        4      365           0.69

str.removeprefix needs Python 3.9 or newer. Filter out branded prompts before you run it, or every engine will look like it recommends you.

Decision Framework: Which Engine to Prioritise

There's no single "most important" engine. The right priority depends on where you stand:

Whichever you choose, track all four. In our data, the engine with the best result for the brand (Perplexity) and the worst (Claude) disagreed completely, and a single-engine view would have shown one of two opposite stories. For the strategy behind each engine, see how AI answer engines pick sources.

How BugViso Compares the Engines for You

BugViso's AI Visibility Tracker asks every engine the same prompt set on every run and keeps the engines separate in every metric:

• Five engines with web search on: ChatGPT, Gemini, Perplexity, Claude and Google AI Overviews, queried through their official APIs (Google AI Overviews through a SERP API; Perplexity through its API platform). Each brand's country and language are passed through.
• Per-engine leaderboard and scorecard. Mention rate, prompt coverage, citation coverage, share of voice and accuracy are reported per engine. The headline index averages engines, so the one with the most answers can't dominate.
• Segments by engine, intent and topic. Segments with fewer than 20 answers are flagged low confidence, so a Claude result built on a handful of answers isn't sold as a trend.
• Per-engine sources and perception. Reports show which sources each engine cited and how each one describes the brand, with quotes.

The engine-by-engine view is on the AI visibility tracker page.

Misconceptions About the Engines

"They all use the same web, so results should match." Their cited domains overlap by 9–16%. They read different pages, through different query rewriting and different retrieval stacks.

"Longer answers mention more brands, so Gemini is the easiest to win." Gemini names more brands, but the slots go to established vendors: our brand appeared in 4.3% of Gemini answers vs 42.9% for the leader.

"No citation means no web search." Claude gave 17 uncited unbranded answers out of 70, but search was available on every call. Crawler access is a separate question again: OpenAI, for example, documents distinct crawlers for search and training. An uncited answer can still be shaped by what the engine read.

"Consumer apps answer exactly like the APIs." They don't: consumer apps add memory, personalisation and location. API measurement is a consistent proxy, not a copy of one user's screen.

FAQ

Which AI engine mentions brands the most?

In our data, Gemini: 56% of its unbranded answers named at least one tracked brand, with 1.43 brands per answer on average. Claude was close at 50%. ChatGPT named a tracked brand in only 31% of answers.

Is Perplexity better than ChatGPT for small brands?

For our brand it was: Perplexity named it in 17.1% of unbranded answers, ChatGPT in 8.6%. Perplexity cited 365 domains across 70 answers versus 83 for ChatGPT, and that wider reading list is a likely reason.

Why doesn't Claude mention my brand?

Claude named our brand in none of 70 unbranded answers, while naming established competitors in up to 38.6%. It recommends from comparison sources where incumbents dominate. Third-party coverage is the usual fix.

Which AI engine cites the most sources?

Perplexity, by far: 14.1 citations per unbranded answer, from 365 distinct domains. ChatGPT cited 2.2 per answer and Claude 4.3; Gemini cited 4.7.

Should I track every AI engine or just ChatGPT?

Track all of them. In our test, the four engines gave our brand mention rates from 0% to 17.1%. A ChatGPT-only view (8.6%) would have hidden both the best and the worst channel.

Conclusion

ChatGPT, Gemini, Perplexity and Claude recommend brands in measurably different ways, so the only honest visibility number is one reported per engine, which is how BugViso's AI visibility tracking scores every run.