Last updated June 2026. All statistics sourced from named studies with attribution and caveats. Numbers without a named primary source do not appear in this post.
Every figure below is a self-contained, attributable data point. The goal is to give you exact numbers to benchmark against, with enough source context to know what each number actually measures.
1. Share of voice in AI: the gap between leaders and everyone else
Share of voice in AI (sometimes called share of model) is the percentage of relevant prompts in which your brand is named, relative to your competitive set.
The headline finding from the most rigorous published benchmark:
According to an Ahrefs analysis of 75,000 brands (May 2025), brands in the top quartile by web mentions averaged 169 AI Overview mentions, more than 10x the 14 averaged by brands in the next quartile.
This is the clearest quantitative proof that AI visibility is not evenly distributed. A small set of brands captures most of the mention volume, and the rest are largely absent.
The study is by Louise Linehan and Xibeijia Guan (Ahrefs, published May 26, 2025). It covers Google AI Overviews specifically. The correlation is between offline web-mention volume and AI Overview appearance count. The causal mechanism is not confirmed, but the pattern is strong enough to treat web mentions as a leading indicator of AI share of voice.
| Brand cohort | Avg. AI Overview mentions |
|---|---|
| Top mention quartile | 169 |
| Next quartile | 14 |
| Implied ratio | 12x |
Source: Ahrefs, May 2025.
2. Brand mention volume: what the benchmarks show
Brand mentions are the raw count of times your brand name appears in AI-generated responses, across a defined set of prompts and engines. They are noisier than share of voice but useful for spotting sudden spikes or drops.
Two benchmarks give you a sense of the range:
Most brands in high-competition verticals get zero mentions. According to GrackerAI’s 2026 AI Search Visibility benchmark (which tested 100 cybersecurity vendors using 250 buyer-intent prompts across six AI platforms), 73% of vendors received zero ChatGPT citations when buyers queried their product category. The finding is specific to ChatGPT and to GrackerAI’s vendor-selected prompt set. GrackerAI sells AI visibility services, so apply the standard vendor-research caveat. But the directional claim (that most brands in a crowded category are invisible on even the dominant platform) is consistent with practitioner observation across other sectors.
The citation count gap across platforms is enormous. According to Qwairy’s Q3 2025 analysis of 118,000+ AI-generated answers across 8 providers, Perplexity averaged 21.87 citations per response versus ChatGPT’s 7.92. Qwairy is a commercial GEO monitoring vendor; the figure has not been independently replicated. But the relative ranking (Perplexity citing far more sources per response than ChatGPT) is consistent with how the two platforms are designed.
What this means for your brand monitoring: the same prompt run on Perplexity can generate nearly three times as many source mentions as the same prompt on ChatGPT. If you are only tracking one platform, you are measuring a fraction of the mentions your brand might be accumulating (or missing) across the AI ecosystem.
3. Cross-platform variation: the platform disagreement problem
This section may be the most actionable in the post. Brand mentions are not consistent across AI engines. Two key data points establish how large the gap is.
Platform-level citation disagreement
According to BrightEdge AI Catalyst research (July 2025), brand mentions disagreed 61.9% of the time across Google AI Overviews, AI Mode, and ChatGPT. Only 33.5% of queries produced the same brand names across all three engines.
Source: BrightEdge AI Catalyst. Methodology: BrightEdge’s proprietary tracked keyword set; the figure covers the three mentioned Google/OpenAI surfaces only.
This is a striking number. It means that two out of three queries produce different brand winners depending on which AI surface the buyer happens to use. You cannot assume that ranking well in ChatGPT translates to visibility in Google AI Overviews, or vice versa.
Domain-level citation overlap between ChatGPT and Perplexity
According to Profound’s analysis of 100,000 prompts run across both platforms (July 2025), only 11% of cited domains appear in both ChatGPT and Perplexity responses. The other 89% of cited domains are unique to one platform.
Note: Profound is an AI citation-tracking company with a commercial interest in demonstrating cross-platform variation. This is vendor research. But an 11% overlap figure is directionally consistent with the BrightEdge disagreement data and with Ahrefs’ August 2025 finding that only 12% of AI-cited URLs also rank in Google’s top 10 for the same query.
AI Overviews vs. AI Mode: different sources, same engine
Staying within the Google ecosystem does not protect you from fragmentation. According to an Ahrefs study of 540,000 query pairs (December 2025, US data), Google AI Mode and Google AI Overviews cited the same URLs only 13.7% of the time. Even within Google, these two AI surfaces draw from largely different source pools.
Source: Ahrefs study by Despina Gavoyannis and Xibeijia Guan, published December 2025.
| Platform comparison | Citation overlap |
|---|---|
| ChatGPT vs. Perplexity (domain-level) | 11% (Profound, July 2025) |
| AI-cited URLs vs. Google top-10 organic | ~12% (Ahrefs, August 2025) |
| Google AI Overviews vs. Google AI Mode | 13.7% (Ahrefs, December 2025) |
| Brand mentions matching across AI Overviews, AI Mode, ChatGPT | 33.5% (BrightEdge, July 2025) |
The strategic implication: a brand’s AI visibility score from a single engine tells you almost nothing about its visibility on other engines. You need cross-platform coverage to understand your actual share of voice.
4. Cross-platform citation variation for a single brand
The variation in how often a single brand gets cited can be even more extreme than the platform-comparison numbers above suggest.
According to Superlines’ March 2026 cross-platform analysis of 34,234 AI responses across 10 platforms over 30 days (January 14 to February 13, 2026), citation rates for the same domain ranged from 27% on Grok down to effectively zero on Claude and other platforms. Superlines characterizes this as a gap of up to 615x.
Important caveats:
- This is a single-vendor study conducted on Superlines’ own domain, not a representative sample of brands.
- The 615x multiplier is Superlines’ own calculation; the underlying methodology is not publicly disclosed.
- No independent study has replicated this specific figure.
That said, the directional finding is credible. The platform-overlap data above (11-13% cross-engine citation overlap) is consistent with extreme variation existing at the brand level. A brand could be the default recommendation on Grok while being invisible on Claude. Buyers using either platform would receive completely different answers to the same category query.
5. Sentiment: how AI engines describe your brand
Share of voice and mention counts tell you whether you are named. Sentiment tells you how you are described. These can diverge sharply.
Sentiment in AI visibility measures whether an AI engine’s description of your brand is positive, neutral, or negative: for example, “the easiest tool for small teams” versus “lacks enterprise features” versus making no characterization at all.
No single large-scale, independently verified sentiment benchmark exists at the time of writing. Published practitioner data points to three patterns worth knowing:
Negative descriptions persist without active correction. AI engines draw on retrieval sources that may be months or years old. A negative review or critical article from 2024 can still be the source material driving a 2026 AI description of your brand. Unlike a Google search result that a user controls by scrolling past, an AI-generated description is embedded in the response with no obvious signal of its age or origin.
Sentiment varies by engine and by prompt framing. An AI engine asked “what are the downsides of [brand]?” will produce a different sentiment profile than one asked “recommend a tool for [use case].” Monitoring share of voice without monitoring sentiment misses this dimension.
Inaccurate sentiment can compound. AI engines can state incorrect facts about your brand: wrong pricing, discontinued features, outdated positioning. These inaccuracies function as a form of negative sentiment because they misrepresent your product to a buyer who trusts the AI’s answer.
Platforms built specifically for sentiment tracking in AI (including Temso and Evertune) provide structured sentiment scoring tied to specific prompts and engines, which is the only reliable way to track this dimension at scale.
6. Why buyers are now inside these AI systems
The context behind every stat above: the audience for AI-generated answers is growing fast.
According to G2’s April 2026 survey of 1,076 B2B software buyers, 51% now start their software research in an AI chatbot more often than Google, up from 29% in G2’s April 2025 survey. G2 is a software marketplace with a commercial interest in this topic; no independent replication of the 51% figure is available.
The same G2 survey found that 69% of B2B software buyers chose a different vendor than they initially planned based on AI chatbot guidance, and 33% bought from a vendor they had never heard of before the chat session.
These figures are not neutral. But they are consistent with the broader direction of every other buyer-behavior dataset in this space.
Master stat table: AI brand visibility benchmarks 2026
| Stat | Metric | Source | Year | Notes |
|---|---|---|---|---|
| Top-quartile brands avg. 169 AI Overview mentions vs. 14 for next quartile | Share of voice | Ahrefs (75,000 brands) | May 2025 | Covers Google AI Overviews; correlation with web mention volume |
| 73% of cybersecurity vendors received zero ChatGPT citations | Mention rate | GrackerAI (100 vendors, 250 prompts) | 2026 | Vendor-published; ChatGPT only; GrackerAI sells AI visibility services |
| Brand mentions disagreed 61.9% of the time across AI Overviews, AI Mode, ChatGPT | Cross-platform consistency | BrightEdge AI Catalyst | July 2025 | Covers 3 surfaces; BrightEdge proprietary keyword panel |
| Only 33.5% of queries produced same brand names across all 3 engines | Cross-platform consistency | BrightEdge AI Catalyst | July 2025 | Covers AI Overviews, AI Mode, ChatGPT |
| Only 11% of cited domains appear in both ChatGPT and Perplexity | Cross-platform overlap | Profound (100,000 prompts) | July 2025 | Vendor research; domain-level overlap, not URL-level |
| Only 12% of AI-cited URLs also rank in Google top-10 | Search vs. AI overlap | Ahrefs (15,000 queries) | August 2025 | ChatGPT, Gemini, Copilot, Perplexity; Perplexity outlier at ~29% |
| AI Overviews and AI Mode cited same URLs only 13.7% of the time | Within-Google overlap | Ahrefs (540,000 query pairs) | December 2025 | US data; September 2025 period |
| Perplexity averages 21.87 citations per response vs. ChatGPT’s 7.92 | Citations per response | Qwairy (118,000+ answers, 8 providers) | Q3 2025 | Vendor research; not independently replicated |
| Citation rates for one domain ranged up to 615x across AI platforms | Cross-platform variation | Superlines (34,234 responses, 10 platforms) | March 2026 | Tested on vendor’s own domain; single-vendor figure; not independently replicated |
| 51% of B2B software buyers now start research in an AI chatbot, up from 29% | Buyer behavior | G2 (1,076 B2B buyers) | April 2026 | G2 is a commercial marketplace; no independent replication found |
What to do with these numbers
Every stat above describes a measurement, not a prescription. But a few action directions follow directly from the data.
Track more than one platform. The 61.9% cross-engine disagreement rate (BrightEdge) and the 11% domain-overlap figure (Profound) mean that single-engine monitoring gives you a systematically incomplete picture of your actual brand visibility. You need to sample across at least ChatGPT, Perplexity, and Google AI Overviews to see where you stand.
Treat web mention volume as a leading indicator. The Ahrefs 75,000-brand study makes the strongest published case that offline web mentions predict AI visibility. Building earned citations in third-party publications is not just a PR activity. It is the strongest signal you can send to AI retrieval systems.
Monitor sentiment separately from mention count. Being named is not the same as being described favorably. Accurate, positive characterizations in AI answers require separate measurement from raw mention tracking.
Compare your citation rate by platform. The 615x variation finding (Superlines) is a single-vendor data point, but it names a real phenomenon. Check whether you are highly visible on one engine and invisible on another. That asymmetry is common and actionable.
Tools for tracking these metrics
Temso is the all-in-one AI SEO platform that covers all four metrics (share of voice, brand mentions, citation rate, and sentiment) across 8 AI engines from $89/mo. It converts the gap data into a prioritized fix queue and executes content and citation improvements inside the same subscription. Setup takes under five minutes.
For teams that need deeper citation intelligence for enterprise reporting, Profound tracks 9+ engines with citation-level attribution at $399/mo for full coverage. Evertune focuses on brand sentiment and narrative positioning in AI, which is the right specialist choice if sentiment accuracy is the primary concern. Peec AI covers 9+ engines with unlimited seats at €85/mo, which suits agencies running multi-client monitoring. Semrush has added AI brand monitoring to its existing SEO suite for teams that want a single combined platform.
The full ranked comparison with pricing and engine coverage is at /rankings/ai-visibility-tools. Methodology is at /methodology.
Start measuring. The benchmarks in this post describe the market as it stands in mid-2026. Brands without a baseline measurement today have no way to know whether they are in the 169-mention quartile or the 14-mention quartile, or whether they are among the 73% receiving zero citations at all.
Sources cited
- Ahrefs (Louise Linehan, Xibeijia Guan): “75,000 Brand Web Mention and AI Overview Correlation Study,” May 2025.
- Ahrefs (Louise Linehan, Xibeijia Guan): “Only 12% of AI Cited URLs Rank in Google’s Top 10,” August 2025.
- Ahrefs (Despina Gavoyannis, Xibeijia Guan): “AI Mode and AI Overviews Citation Overlap Study” (540,000 query pairs), December 2025.
- BrightEdge AI Catalyst: Cross-engine brand mention consistency research, July 2025.
- GrackerAI: “State of AI Search Visibility in Cybersecurity 2026,” February 2026. (Vendor-published.)
- G2: “The Answer Economy: How AI Search Is Rewiring B2B Software Buying,” April 2026. (Survey of 1,076 B2B buyers.)
- Profound: “Answer Engine Citation Overlap Strategy” (100,000 prompts), July 2025. (Vendor-published.)
- Qwairy: “Provider Citation Behavior Q3 2025” (118,000+ AI-generated answers). (Vendor-published.)
- Superlines (Hannes Jersenius): “AI Search Statistics 2026,” March 2026. (Single-vendor, own domain only.)
Quarterly refresh cadence: benchmarks in this post are reviewed and updated each quarter. [Check the updatedDate in the frontmatter for the most recent revision.]