ChatGPT vs Claude vs Perplexity vs Gemini: how brand visibility differs
The four major AI engines do not work the same way. ChatGPT owns referral traffic, Gemini owns reach, Perplexity owns research, Claude owns the B2B technical audience. The per-engine playbook.
Originally published April 13, 2026
Brand visibility is not the same work across the four major AI engines. ChatGPT owns referral traffic. Gemini and AI Overviews own reach. Perplexity owns research queries. Claude owns the B2B technical audience. Each has a different user base, a different citation mechanism, and a different set of interventions that move the metric. Optimizing equally for all four spreads budget too thin to move any single metric. This is the side-by-side we use internally to scope new GEO engagements.
Soar is a community marketing agency that has run 4,200+ community campaigns across 280+ brands since 2017. Most of those programs now connect into AI visibility work, which is why we run this comparison in the first kickoff call: most of the budget should ride on one or two engines, and the right two depend almost entirely on who the buyer is.
ChatGPT: the traffic leader
ChatGPT is the category king by a wide margin. As of February 2026, it has around 900 million weekly active users (TechCrunch), roughly 50 million paying subscribers, and 87.4 percent of all AI referral traffic per Passionfruit's 2025 ten-industry study (Passionfruit). If you only have resources to optimize for one engine, it is ChatGPT, and it is not close.
The citation mix is pre-training memorization plus Bing-driven real-time search. ChatGPT Search launched October 31, 2024, and OpenAI has confirmed Bing is a primary input. GPT-4o's cutoff is October 2023; GPT-5.x reaches August 2025. Three levers in priority order: fix Bing visibility first (invisible in Bing means invisible in ChatGPT Search), seed the corpora that feed future training runs (Reddit, Wikipedia, YouTube, Stack Overflow, editorial press), and make sure GPTBot and OAI-SearchBot are allowed in robots.txt. Fixing the crawlability layer alone often unlocks 20 to 40 percent mention-rate improvement in two to three weeks. The tactical detail is in how to get your brand mentioned in ChatGPT answers.
Claude: the B2B technical audience
Claude is smaller in raw users but dominates technical B2B buyers, senior engineers, and research-heavy users. Business of Apps reports roughly 7.38 million monthly app users at the end of 2025 (Business of Apps). Anthropic's revenue tells the real story: $14 billion annualized by February 2026, up from $1 billion at the start of 2025. That is hockey-stick expansion driven almost entirely by API-first technical customers and enterprise contracts.
Claude uses pre-training plus three dedicated crawlers: ClaudeBot for training, Claude-User for user-initiated fetches, and Claude-SearchBot for Anthropic's in-product search index. Claude's web search tool is available on Opus 4.6 and Sonnet 4.6. Make all three crawlers welcome, publish clean technical documentation with code examples, and seed your brand on the sources Claude's training pipeline weights highly: GitHub, Stack Overflow, developer subreddits, and technical editorial. Claude under-cites brands that exist almost entirely in consumer press. The step-by-step is in get cited by Claude for brand queries.
Perplexity: the research engine
Perplexity is the smallest of the four but has the most research-heavy user base. Business of Apps reports roughly 45 million active users and ~780 million monthly queries end of 2025 (Business of Apps). The audience is analysts, due-diligence teams, and product managers gathering sources. Low tolerance for marketing copy, high tolerance for dense technical material.
Perplexity runs a three-layer retrieval pipeline: initial retrieval, authority-and-credibility ranking, and an XGBoost reranker for entity queries. Source credibility rests on four signals: trustworthiness, authority, corroboration, and provenance. The company manually boosts GitHub, Amazon, LinkedIn, and Reddit as source domains. Build real presences on those four. Make your own site technically credible with author bylines, publication dates, and citations to primary sources, because Perplexity's credibility ranker reads those as signal. Brochure-style content ranks lower than content that reads like a primary source. The tactical guide is in rank in Perplexity answers.
Gemini and Google AI Overviews: the reach leader
Gemini powers Google's flagship app plus AI Overviews and AI Mode inside Search. The numbers are enormous. The Gemini app alone reached 750 million monthly active users as of February 2026 (TechCrunch). AI Overviews has roughly 2 billion monthly users worldwide. AI Mode adds another 100 million in the US and India. Sistrix tracking shows AIO now triggers on roughly 20 percent of German-language and 18 percent of UK-language keywords (Sistrix).
Seer Interactive's 2025 study of 25 million organic impressions found that when an AIO is present, organic CTR drops 61 percent and paid CTR drops 68 percent. When you are cited inside the AIO, you get 35 percent more organic clicks and 91 percent more paid clicks than non-cited results on the same query (Seer). AI Overviews pulls sources from Google's existing index, weighted by E-E-A-T. Google's own documentation says there is no special schema required. Rank well in classical Google, improve E-E-A-T, get author bylines and publication dates on every article. Visibility inside AIO is downstream of traditional search equity. Walkthrough: how to appear in Google AI Overviews.
Side-by-side comparison
The four engines do similar work for users and very different work for the brands trying to be cited inside them. The table below is what we hand to clients in week one of a scoping engagement.
| Engine | Scale | Citation mechanism | Best-fit audience | Biggest blocker |
|---|---|---|---|---|
| ChatGPT | 900M weekly active users | Bing index plus training data | Consumer and B2B breadth | Not indexed by Bing |
| Claude | ~7.38M monthly app users | 3 crawlers plus live web search tool | Developers, researchers, enterprise | ClaudeBot blocked by robots.txt |
| Perplexity | ~45M active users, 780M queries/month | 3-layer retrieval, XGBoost reranker | Technical researchers, power users | Low authority domain score |
| Google AI Overviews | ~2B monthly users | Google index weighted by E-E-A-T | Mass consumer and informational queries | Not ranking organically for the query |
A few cross-engine reference points worth keeping handy when budget conversations come up:
Primary citation mechanism: ChatGPT pre-training plus Bing; Claude pre-training plus three internal crawlers; Perplexity real-time retrieval with authority ranking; Gemini and AIO Google's existing index weighted by E-E-A-T.
Fastest intervention: ChatGPT Bing SEO plus Reddit; Claude robots.txt hygiene plus GitHub; Perplexity Reddit, GitHub, LinkedIn, Amazon; Gemini classical Google SEO plus E-E-A-T.
Share of AI referral traffic: ChatGPT 87.4 percent; the other three combined split the remaining 12.6 percent.
Hardest to optimize cold: Perplexity, because authority signals compound over months; easiest is Gemini and AIO if you already rank in Google.
Who should prioritize which engine
The split is almost always driven by who the buyer is, not by the engine's overall popularity. Three rules of thumb hold across most of the engagements we run.
Prioritize Claude and Perplexity. Engineers, product managers, and analysts reach for the tool that hands them dense technical answers with citations. A B2B SaaS brand invisible in Perplexity is invisible to the audience that runs vendor research, even if ChatGPT rankings look fine.
B2B technicalPrioritize ChatGPT and AI Overviews. That is where the volume lives. A DTC brand or media company ignoring AIO is watching classical Google traffic erode without a replacement and watching ChatGPT referral data climb without anyone in their category showing up in it.
Consumer reachChatGPT plus one other. If you serve engineers, add Claude. If you serve researchers, add Perplexity. If you serve consumers, add Gemini. Equal weighting across all four is the slowest way to make measurable progress on any single one.
Mid-market hybridFor the pillar framework, see the 2026 guide to Generative Engine Optimization. For the technical layer, how LLMs decide what to cite.
The B2B vs consumer divide
B2B brands over-index on ChatGPT because it is the biggest name, then wonder why Perplexity share-of-voice is terrible. Their buyers are not asking ChatGPT for due diligence. They are asking Perplexity, Claude, and Gemini's Deep Research. ChatGPT dominates consumer volume, but a senior buyer doing vendor research is as likely to be inside Perplexity as inside ChatGPT, and Claude's share of that audience keeps climbing.
Consumer brands make the inverse mistake. They over-index on Claude or Perplexity because the names sound modern, then miss that their actual buyers never open either product. If the end user is not a researcher, GEO budget belongs in ChatGPT and AIO first. The fix is the same in both cases: route the budget to the engine your buyer actually uses, not to the engine that gets the most press.
How to scope an engagement that hits multiple engines
The fastest way to commit a six-figure mistake is to lock the budget before doing the prioritization. We start every engagement with three concrete inputs, in this order. First, the buyer audit: who is the actual end user, and which engines do they use weekly? Second, the prompt audit: what are the 20 prompts that decide a deal in your category, and which engines fire on them today? Third, the citation baseline: where does your brand currently show up inside those prompts, and where do competitors show up where you do not?
The output is a weighted plan. A typical B2B SaaS scope lands at 60 percent Claude and Perplexity, 30 percent ChatGPT, 10 percent Gemini and AIO. A typical DTC brand lands at 60 percent ChatGPT and Gemini, 30 percent monitoring on Perplexity and Claude, 10 percent owned-search infrastructure. The wrong-engine cost is real: brands routinely spend a full quarter optimizing for an engine their buyers do not use, then start over.
Frequently asked questions
Which AI engine should a B2B SaaS brand optimize for first?
For most B2B SaaS brands, Claude and Perplexity sit ahead of ChatGPT for vendor-research queries. Buyers are technical, they want sources, and both engines weight authority signals heavily. ChatGPT still matters, but it is usually the second-priority engine because the buyer rarely uses it for purchase research.
Is it worth optimizing for Gemini if I already rank in Google?
Yes, but the work is different from classical SEO. Gemini and AI Overviews pull from Google's index, so traditional ranking is a prerequisite. The added layer is passage-level optimization: short H2 and H3 answers under specific sub-questions, named authors with bylines, and quarterly content refreshes. That is what controls whether you get quoted inside the AIO, not just ranked below it.
How much budget should go to ChatGPT vs the other three?
For most consumer brands, 50 to 70 percent of GEO budget should ride on ChatGPT and Gemini and AIO combined, because that is where 90+ percent of AI referral traffic and reach live. For B2B technical brands, ChatGPT can drop to 20 to 30 percent and Claude plus Perplexity take the rest. The split should always be driven by who the buyer is.
What is the fastest way to improve visibility across all four engines at once?
Seeding content on Reddit. Reddit is one of the largest single training sources for ChatGPT, Claude, and Gemini, and Perplexity manually boosts it. A well-placed thread compounds across all four engines from a single program. The deeper rationale is in how Reddit became the biggest single source of LLM citations.
Do I need different content for each engine?
You need different distribution and crawler hygiene per engine, but the core content can be shared. ChatGPT and Gemini want long-form answers indexed in Bing and Google. Claude wants clean technical documentation. Perplexity wants citable primary sources with bylines. A single well-structured pillar page can feed all four if the supporting distribution (Reddit, GitHub, LinkedIn, named author pages) is in place.
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Conclusion
The four major engines are not interchangeable. They have different audiences, different citation mechanisms, and different intervention costs. Spreading a budget equally across all four is the fastest way to make no measurable progress on any of them. Pick the one or two engines your actual buyers use, go deep, measure weekly, and let the rest run as monitoring until the prioritized engines are working.
