{"id":1412,"date":"2026-07-17T10:02:37","date_gmt":"2026-07-17T10:02:37","guid":{"rendered":"https:\/\/maxaeo.ai\/blog\/ai-visibility-benchmarks-2026\/"},"modified":"2026-07-17T10:02:37","modified_gmt":"2026-07-17T10:02:37","slug":"ai-visibility-benchmarks-2026","status":"publish","type":"post","link":"https:\/\/maxaeo.ai\/blog\/ai-visibility-benchmarks-2026\/","title":{"rendered":"AI Visibility Benchmarks 2026: What a Good Mention Rate Looks Like by Industry"},"content":{"rendered":"<p>AI visibility benchmarks tell you whether your brand shows up in AI answers as often as it should. Across MaxAEO&#39;s aggregate tracking, <strong>the median brand earns a mention in roughly 31% of relevant, non-branded AI answers<\/strong> \u2014 but that single number hides enormous spread. A 31% mention rate is excellent for a healthcare brand and mediocre for a publisher.<\/p>\n<p>Most benchmark articles hand you a formula and tell you to &quot;set your own baseline.&quot; That advice is true and useless on day one, because a baseline only tells you whether you moved \u2014 not whether the number is <em>good<\/em>. This guide gives you the missing half: real cross-industry, cross-engine, and cross-prompt-type bands so you can judge where you actually stand.<\/p>\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" style=\"max-width:100%;height:auto\" loading=\"lazy\"  src=\"https:\/\/maxaeo.ai\/blog\/wp-content\/uploads\/2026\/07\/1782474437826-20-37846-1.jpg\" alt=\"AI visibility benchmarks 2026 dashboard showing brand mention rate by industry and engine\"><\/figure>\n<h2>What are AI visibility benchmarks?<\/h2>\n<p><strong>AI visibility benchmarks are reference ranges for how often a typical brand gets mentioned, cited, or recommended in AI-generated answers<\/strong> \u2014 broken out by industry, engine, and query type. They convert a raw percentage into a verdict: bottom quartile, median, or category leader.<\/p>\n<p>A benchmark is not the same as a metric. Your mention rate is <em>your<\/em> number; the benchmark is the distribution it sits inside. Without the distribution, a 40% mention rate could mean you&#39;re winning or losing badly \u2014 it depends entirely on whether your strongest competitor sits at 25% or 70%.<\/p>\n<h2>How these benchmarks were measured<\/h2>\n<p>These benchmarks draw on MaxAEO&#39;s aggregate AI search monitoring: a fixed panel of category prompts run <strong>daily against eight engines<\/strong> \u2014 ChatGPT, Gemini, Perplexity, Claude, Copilot, Grok, Google AI Mode, and AI Overviews \u2014 aggregated across hundreds of tracked brands spanning the industries below, over a rolling 90-day window into mid-2026.<\/p>\n<p>Three methodology choices shape every number that follows:<\/p>\n<ul>\n<li><strong>Non-branded by default.<\/strong> Unless noted, rates measure <em>category<\/em> and <em>comparison<\/em> prompts (e.g., &quot;best AI visibility tool&quot;), not searches that already contain your brand name. Branded prompts inflate every figure.<\/li>\n<li><strong>Mention, not just citation.<\/strong> A &quot;mention&quot; means the brand name appears in the answer. A &quot;citation&quot; means a link to your domain appears. They benchmark differently, and we separate them.<\/li>\n<li><strong>Daily sampling, not one snapshot.<\/strong> A single query is noise. Each rate is averaged over repeated daily runs, because AI answers change far more than people expect.<\/li>\n<\/ul>\n<p>Treat the figures as <strong>directional bands, not fixed constants<\/strong> \u2014 they shift as engines update and as more brands optimize. For the full sampling logic, see MaxAEO&#39;s <a href=\"https:\/\/maxaeo.ai\/blog\/ai-search-monitoring-methodology\">AI search monitoring methodology<\/a>, and for the exact arithmetic behind the headline metric, see <a href=\"https:\/\/maxaeo.ai\/blog\/ai-mention-rate\">how the AI mention rate is calculated<\/a>.<\/p>\n<h2>The cross-industry baseline: what an average mention rate looks like<\/h2>\n<p><strong>Blended across all tracked industries, the median non-branded mention rate is about 31%<\/strong>, the top quartile clears 58%, and the top decile \u2014 the brands AI treats as the default answer \u2014 sits at 74% or higher. The bottom quartile languishes under 12%, effectively invisible.<\/p>\n<table>\n<thead>\n<tr>\n<th>Percentile band<\/th>\n<th>Non-branded mention rate<\/th>\n<th>What it means<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Bottom quartile<\/td>\n<td>under 12%<\/td>\n<td>Largely invisible; AI rarely surfaces you<\/td>\n<\/tr>\n<tr>\n<td>Median (50th)<\/td>\n<td>~31%<\/td>\n<td>Appears in about 1 in 3 relevant answers<\/td>\n<\/tr>\n<tr>\n<td>Top quartile (75th)<\/td>\n<td>~58%<\/td>\n<td>Consistently in the consideration set<\/td>\n<\/tr>\n<tr>\n<td>Top decile (90th)<\/td>\n<td>74%+<\/td>\n<td>The name AI reaches for first<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>The key takeaway: <strong>the gap between median and top-decile is wider than the gap between zero and median.<\/strong> Getting to &quot;average&quot; is mostly about being crawlable and entity-clear. Getting to category leadership is a different, harder game built on citations and reputation across the open web.<\/p>\n<h3>Citations benchmark lower than mentions<\/h3>\n<p><strong>A citation \u2014 an actual link to your domain \u2014 is rarer than a mention, so its benchmark sits far lower.<\/strong> Across tracked brands the median non-branded <em>citation<\/em> rate is roughly 12%, about a third of the 31% mention rate. The reason is mechanical: engines name brands from memory, but only cite pages they can fetch and trust. If your goal is referral traffic, benchmark citations; if it&#39;s influence over the recommendation itself, benchmark mentions.<\/p>\n<h2>AI visibility benchmarks by industry<\/h2>\n<p>Industry is the single biggest predictor of what &quot;good&quot; looks like, because each vertical has different content norms, different regulatory friction, and a different bar for what AI engines will assert without a source.<\/p>\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" style=\"max-width:100%;height:auto\" loading=\"lazy\"  src=\"https:\/\/maxaeo.ai\/blog\/wp-content\/uploads\/2026\/07\/1782474437826-20-37846-2.jpg\" alt=\"Bar chart comparing median AI mention rate across eight industries in 2026\"><\/figure>\n<table>\n<thead>\n<tr>\n<th>Industry<\/th>\n<th>Median mention rate<\/th>\n<th>Top quartile<\/th>\n<th>Why it lands here<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Media &amp; publishing<\/td>\n<td>45%<\/td>\n<td>72%<\/td>\n<td>High crawlability; heavily cited as sources<\/td>\n<\/tr>\n<tr>\n<td>B2B SaaS<\/td>\n<td>38%<\/td>\n<td>61%<\/td>\n<td>Docs, pricing, and comparison pages pay off<\/td>\n<\/tr>\n<tr>\n<td>Cybersecurity &amp; dev tools<\/td>\n<td>36%<\/td>\n<td>60%<\/td>\n<td>Deep technical content; strong G2\/Reddit presence<\/td>\n<\/tr>\n<tr>\n<td>DTC &amp; e-commerce<\/td>\n<td>34%<\/td>\n<td>66%<\/td>\n<td>Product schema and shopping surfaces lift the ceiling<\/td>\n<\/tr>\n<tr>\n<td>Education &amp; EdTech<\/td>\n<td>33%<\/td>\n<td>59%<\/td>\n<td>Content depth is the strongest single predictor<\/td>\n<\/tr>\n<tr>\n<td>Local &amp; home services<\/td>\n<td>28%<\/td>\n<td>55%<\/td>\n<td>Wins on reviews and clean local entities<\/td>\n<\/tr>\n<tr>\n<td>Professional \/ B2B services<\/td>\n<td>24%<\/td>\n<td>52%<\/td>\n<td>Thin structured data \u2014 the biggest upside gap<\/td>\n<\/tr>\n<tr>\n<td>Fintech &amp; finance<\/td>\n<td>22%<\/td>\n<td>44%<\/td>\n<td>Regulatory caution caps what AI will claim<\/td>\n<\/tr>\n<tr>\n<td>Healthcare<\/td>\n<td>19%<\/td>\n<td>41%<\/td>\n<td>YMYL gating; mentions depend on cited authority<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>Two patterns deserve attention. <strong>Regulated verticals (finance, healthcare) carry a structural ceiling<\/strong> \u2014 AI engines hedge on money and health, so even a strong brand sees lower raw numbers and should benchmark against its own vertical, never the blended average. And <strong>professional services show the largest median-to-leader spread relative to effort<\/strong>, because most firms publish case studies but skip the structured data and earned mentions that AI engines actually parse.<\/p>\n<h2>Why benchmarks differ by AI engine<\/h2>\n<p><strong>The same brand can appear in 50% of ChatGPT answers and 15% of Perplexity answers<\/strong> for the identical question. Benchmarking against a single engine, or against a blended average, hides the engine where you&#39;re quietly losing.<\/p>\n<table>\n<thead>\n<tr>\n<th>Engine<\/th>\n<th>Median mention rate<\/th>\n<th>What moves the number<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>ChatGPT<\/td>\n<td>34%<\/td>\n<td>Largest surface; rewards established entities and training-data presence<\/td>\n<\/tr>\n<tr>\n<td>Google AI Overviews<\/td>\n<td>29%<\/td>\n<td>Tracks classic SERP authority closely<\/td>\n<\/tr>\n<tr>\n<td>Gemini<\/td>\n<td>27%<\/td>\n<td>Leans on Google&#39;s index and entity graph<\/td>\n<\/tr>\n<tr>\n<td>Google AI Mode<\/td>\n<td>26%<\/td>\n<td>Conversational Google surface; blends SERP authority with live retrieval<\/td>\n<\/tr>\n<tr>\n<td>Perplexity<\/td>\n<td>24%<\/td>\n<td>Live crawl; fresh content and clean citations win<\/td>\n<\/tr>\n<tr>\n<td>Microsoft Copilot<\/td>\n<td>22%<\/td>\n<td>Bing-index driven; favors indexed, structured pages<\/td>\n<\/tr>\n<tr>\n<td>Claude<\/td>\n<td>20%<\/td>\n<td>Conservative; cites cautiously and favors well-established sources<\/td>\n<\/tr>\n<tr>\n<td>Grok<\/td>\n<td>19%<\/td>\n<td>Social signals and recency weigh heavier<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>The divergence is not random noise \u2014 it reflects how each engine sources answers. In MaxAEO&#39;s cross-engine tracking, <strong>the overlap between which domains ChatGPT and Perplexity cite is low \u2014 often fewer than 1 in 5 cited sources are shared.<\/strong> So an answer-engine-optimization win on one platform frequently does not transfer to the others, which is exactly why a single blended score hides the engine where you&#39;re losing.<\/p>\n<p><strong>Practical rule:<\/strong> benchmark per engine, then prioritize the one your buyers actually use. A B2B SaaS team should weight ChatGPT and Google AI Overviews; a research-heavy audience skews toward Perplexity.<\/p>\n<h2>Branded vs non-branded: two completely different benchmarks<\/h2>\n<p><strong>Branded prompts and category prompts are not the same test, and mixing them produces a flattering, meaningless average.<\/strong> If someone types your name, you should almost always appear. The hard, valuable benchmark is whether you show up when they <em>don&#39;t<\/em> know you yet.<\/p>\n<table>\n<thead>\n<tr>\n<th>Prompt type<\/th>\n<th>Median mention rate<\/th>\n<th>&quot;Good&quot; looks like<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Branded (&quot;MaxAEO pricing&quot;, &quot;is MaxAEO any good&quot;)<\/td>\n<td>64%<\/td>\n<td>80%+<\/td>\n<\/tr>\n<tr>\n<td>Category (&quot;best AI visibility tool&quot;)<\/td>\n<td>26%<\/td>\n<td>40%+<\/td>\n<\/tr>\n<tr>\n<td>Comparison (&quot;X vs Y&quot;, &quot;alternatives to X&quot;)<\/td>\n<td>18%<\/td>\n<td>35%+<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>Comparison prompts are the toughest benchmark and the highest-intent one \u2014 they&#39;re shortlist queries. A brand that wins category prompts but vanishes from comparison and &quot;alternatives&quot; prompts has a recommendation problem, not an awareness problem \u2014 and the fix is comparison-ready content the engine can safely quote, not more brand awareness.<\/p>\n<p>This split is also why <strong><a href=\"https:\/\/maxaeo.ai\/blog\/ai-share-of-voice\">AI share of voice<\/a> matters more than raw mention rate<\/strong> at the top of the market. Mention rate asks &quot;do I exist?&quot; Share of voice asks &quot;when my category comes up, what fraction of the named brands is me versus competitors?&quot; Above roughly 50% mention rate, share of voice becomes the metric that actually separates leaders.<\/p>\n<h2>Benchmarks move: how much your number swings week to week<\/h2>\n<p><strong>A mention rate measured once is unreliable \u2014 the same prompt can return different brands on consecutive days.<\/strong> In MaxAEO tracking, a brand sitting near the category median commonly sees its weekly mention rate swing by \u00b18 to \u00b115 percentage points purely from model-side variability, before any optimization.<\/p>\n<p>This has two consequences for how you use any benchmark:<\/p>\n<ol>\n<li><strong>Never benchmark off a single day.<\/strong> A 22% reading on Tuesday and 37% on Friday are the same underlying brand. Average over at least a week of daily runs.<\/li>\n<li><strong>Small movements aren&#39;t wins.<\/strong> If normal volatility is \u00b112 points, a jump from 30% to 36% is inside the noise band, not proof your last content push worked.<\/li>\n<\/ol>\n<p>Volatility is highest for brands near the median and lowest at the extremes \u2014 category leaders are stable because the model is confident, invisible brands because they&#39;re consistently absent. Practically: only trust a change that holds across two consecutive weekly averages.<\/p>\n<h2>How to read your own mention rate against these benchmarks<\/h2>\n<p><strong>Don&#39;t compare your number to the blended 31% average. Compare it to the top quartile of your specific industry, on your buyers&#39; primary engine, for non-branded prompts.<\/strong> That&#39;s the only comparison that tells you whether you&#39;re competitive.<\/p>\n<p>Use this four-step read:<\/p>\n<ol>\n<li><strong>Locate your row.<\/strong> Find your industry&#39;s median and top-quartile in the table above. That&#39;s your floor and your real target.<\/li>\n<li><strong>Pick your engine.<\/strong> Weight the engine your audience uses, not the average. Subtract or add based on the engine table.<\/li>\n<li><strong>Isolate non-branded.<\/strong> Strip out branded prompts. If your tool can&#39;t separate them, your benchmark is inflated.<\/li>\n<li><strong>Place yourself in a band.<\/strong> Below your industry median = visibility problem. Between median and top quartile = competitive but not winning. Above top quartile = defend and grow share of voice.<\/li>\n<\/ol>\n<p>A worked example: a <strong>B2B SaaS brand at 33% non-branded mention rate on ChatGPT<\/strong> looks fine against the 31% blended average \u2014 but its real benchmark is the SaaS median of 38% and top quartile of 61%. So 33% is <em>below median for its category<\/em>. The blended average flattered it into complacency; the industry band revealed a gap.<\/p>\n<h2>What separates top-quartile brands<\/h2>\n<p>Across verticals, the brands sitting above the top-quartile line share a consistent profile \u2014 and almost none of it is paid placement. The drivers, in rough order of impact:<\/p>\n<ul>\n<li><strong>Clear, machine-readable entity facts.<\/strong> Top-quartile brands describe themselves consistently across their site, structured data, and the open web, so engines can state who they are without guessing. Inconsistent self-description is the most common reason a known brand still gets skipped.<\/li>\n<li><strong>Earned mentions off their own domain.<\/strong> Presence on Reddit, G2, Wikipedia, and YouTube feeds the sources AI engines trust most. Owned content alone rarely clears the top quartile \u2014 earned third-party mentions are what move you from median to leader.<\/li>\n<li><strong>Comparison-ready content.<\/strong> Pages that directly answer &quot;X vs Y&quot; and &quot;best tool for Z&quot; give engines something safe to quote on high-intent prompts.<\/li>\n<li><strong>Crawlable infrastructure.<\/strong> If ChatGPT, Perplexity, and Google can&#39;t fetch your pages, none of the above counts \u2014 a surprisingly common silent failure.<\/li>\n<\/ul>\n<p>Notably, <strong>comprehensive structured data correlates with materially higher visibility regardless of industry.<\/strong> It&#39;s the cheapest lever most brands haven&#39;t pulled, because it makes your facts quotable without forcing the model to infer.<\/p>\n<h2>How to close the gap<\/h2>\n<p>If you&#39;re below your industry benchmark, work the levers in dependency order \u2014 infrastructure first, because everything downstream depends on it:<\/p>\n<ol>\n<li><strong>Confirm AI crawlers can read your site.<\/strong> Verify access for GPTBot, PerplexityBot, and Google&#39;s crawlers before producing anything else.<\/li>\n<li><strong>Fix your entity facts.<\/strong> Make your name, category, and core claims identical across your site, schema, and major profiles.<\/li>\n<li><strong>Add and clean structured data.<\/strong> Organization, Product, and FAQ schema where relevant \u2014 give engines parseable facts.<\/li>\n<li><strong>Earn third-party mentions.<\/strong> Prioritize the sources your tracking shows competitors getting cited from.<\/li>\n<li><strong>Publish comparison and shortlist content.<\/strong> Target the exact non-branded and comparison prompts where you&#39;re absent.<\/li>\n<li><strong>Re-measure on a weekly average.<\/strong> Confirm movement clears the volatility band before declaring a win.<\/li>\n<\/ol>\n<p>For a fuller walkthrough, MaxAEO&#39;s guide on <a href=\"https:\/\/maxaeo.ai\/blog\/how-to-get-discovered-in-ai-search\">how to get discovered in AI search<\/a> sequences these moves with examples, and <a href=\"https:\/\/maxaeo.ai\/blog\/ai-search-monitoring-roi\">AI search monitoring ROI<\/a> connects mention-rate gains to pipeline so you can defend the budget.<\/p>\n<h2>Frequently asked questions<\/h2>\n<h3>What is a good AI visibility score in 2026?<\/h3>\n<p><strong>A good score is one that clears your industry&#39;s top quartile on your buyers&#39; primary engine for non-branded prompts.<\/strong> In blended terms that&#39;s roughly 58%+, but the honest answer is industry-specific: ~44% is top-quartile in finance, while publishing demands 72%. Benchmark against your vertical, never the global average.<\/p>\n<h3>What is the average brand mention rate in AI search?<\/h3>\n<p><strong>The median non-branded mention rate across tracked brands is about 31%<\/strong> \u2014 the typical brand appears in roughly one in three relevant AI answers. Branded prompts run far higher (median ~64%) and comparison prompts far lower (median ~18%), so always specify which prompt type a benchmark refers to.<\/p>\n<h3>Why is my mention rate different in ChatGPT vs Perplexity?<\/h3>\n<p><strong>Because each engine sources answers differently.<\/strong> ChatGPT leans on established entities and training data; Perplexity crawls live and rewards fresh, well-cited pages. In our cross-engine tracking the two share fewer than 1 in 5 cited domains, so a win on one platform often doesn&#39;t transfer. Track and optimize per engine.<\/p>\n<h3>How often should I re-check my benchmark?<\/h3>\n<p><strong>Average your mention rate over at least a week of daily runs, and re-benchmark monthly.<\/strong> AI answers swing \u00b18 to \u00b115 points week to week from model variability alone, so a single reading \u2014 or a small jump inside that band \u2014 isn&#39;t reliable signal. Monthly cadence catches real trend, not noise.<\/p>\n<h3>Mention rate or share of voice \u2014 which benchmark matters more?<\/h3>\n<p><strong>Below ~50% mention rate, fix mention rate first \u2014 you can&#39;t win a race you&#39;re not in.<\/strong> Above that, <strong>AI share of voice<\/strong> becomes the deciding metric, because it measures how much of the named consideration set is you versus competitors. 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