What Share of Model should my brand target?

Your target SoM depends on your category, competitive set, and current baseline. As a general guideline, aim to reach the "competitive" tier (10-25% SoM on your primary engine) within 6-12 months. Benchmark against your specific competitors with your own query bank to set a personalised target.

Why does my SoM differ so much between engines?

Each AI engine uses different training data, retrieval methods, and weighting algorithms. A brand with strong review presence might score well on engines that heavily index review sites but poorly on engines that prioritise academic or technical sources. Cross-engine variance of 2-4x is normal.

Can SoM drop suddenly?

Yes. SoM can shift significantly when an AI engine releases a major model update or changes its retrieval approach. Our 2026 study suggested quarterly SoM fluctuations of 10-15 percentage points are not uncommon. Continuous monitoring is essential to detect and respond to these shifts.

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Research

Share of Model Benchmarks

What good Share of Model looks like — median, top-quartile, and leader-tier SoM across categories.

Last updated: 2026-10-02

Quick answer

Share of Model is the defining metric of AI visibility, but without benchmarks it is just a number. Our 2026 study suggested that "good" SoM varies enormously depending on category competitiveness, engine, and query type — a 15% SoM that represents dominance in one category might signal underperformance in another.

Key findings

Data at a glance

~8%

Median SoM across categories

Our 2026 study suggested that the median Share of Model across all tracked categories and engines is approximately 8%, reflecting the long tail of brands competing for AI visibility.

~24%

Top-quartile SoM threshold

Based on data collected in our 2026 study, brands in the top 25% of their category typically achieve at least 24% Share of Model on their strongest engine.

~40%+

Category leader SoM

Our 2026 study suggested that clear category leaders — brands that dominate their niche — often achieve 40% or higher SoM on favourable engines.

~3.2x

Cross-engine SoM variance

Based on data collected in our 2026 study, the average brand sees roughly 3.2x variance between their highest and lowest SoM across engines.

~6 mo.

Time to meaningful SoM gains

Our 2026 study suggested that brands implementing a structured AEO programme typically see meaningful SoM improvement within approximately 6 months.

~2.1x

SoM advantage from reviews

Based on data collected in our 2026 study, brands with strong third-party review presence achieve approximately 2.1x higher SoM than those without.

~15%

SoM volatility per quarter

Our 2026 study suggested that SoM can fluctuate by approximately 15 percentage points per quarter due to model updates and competitive content changes.

Avg. competitors per response

Based on data collected in our 2026 study, AI engines typically mention approximately 5 brands in a competitive recommendation response, making the visibility window narrow.

Understanding SoM tiers

Our 2026 study suggested that Share of Model scores cluster into distinct tiers that reflect a brand's competitive position in AI search. The "invisible" tier (0-3% SoM) includes brands that rarely appear in AI responses — often because they lack the content signals AI engines need to identify and recommend them.

The "emerging" tier (3-10% SoM) represents brands that appear occasionally but inconsistently. Based on data collected in our 2026 study, most brands sampled fell into this tier when they first begin measuring. The "competitive" tier (10-25% SoM) indicates a brand that AI engines regularly recognise and recommend. The "leader" tier (25%+ SoM) represents clear category dominance.

These tiers are not fixed — they vary by category competitiveness. Our 2026 study suggested that in a category with 30+ active brands, achieving 15% SoM puts you in the leader tier. In a category with only 5-6 brands, 15% SoM might place you in the emerging tier.

SoM by engine: where to focus

Not all engines are created equal for SoM, and the right focus depends on your audience. Our 2026 study suggested that ChatGPT currently commands the largest user base for product research queries, making it the highest-priority engine for most brands. However, Perplexity shows the highest citation density — meaning it names more brands per response — which creates opportunities for challenger brands.

Based on data collected in our 2026 study, Gemini tends to favour brands with strong Google ecosystem presence (Google Business profiles, YouTube content, and Google Reviews). Claude shows a preference for brands with detailed technical documentation and thought leadership content. Copilot draws heavily from Microsoft ecosystem signals.

The strategic implication is that brands should not optimise for SoM in aggregate but should set engine-specific targets based on where their audience searches. Our 2026 study suggested that focusing on the 2-3 engines most relevant to your audience yields better results than spreading effort across all engines equally.

Improving SoM: what works

Our 2026 study suggested that the most effective SoM improvement strategies share common characteristics: they focus on creating content that AI engines can easily extract and cite, building third-party authority signals, and maintaining consistent brand presence across the information sources AI engines use for training and retrieval.

Based on data collected in our 2026 study, the three highest-impact actions for SoM improvement are: (1) publishing detailed comparison and alternative content that directly addresses the queries where you want to appear, (2) building and maintaining profiles on major review platforms and industry directories, and (3) ensuring your website is technically accessible to AI crawlers with proper structured data markup.

The timeline for SoM improvement is important to set expectations. Our 2026 study suggested that brands implementing a comprehensive AEO programme typically see initial SoM movement within 2-3 months, with meaningful improvement by 6 months. However, SoM gains are not linear — they often come in steps as AI models are retrained on new content.

SoM benchmarks and competitive strategy

Understanding your SoM relative to competitors is more valuable than tracking your absolute number. Our 2026 study suggested that the competitive SoM landscape is a zero-sum game within each response — when one brand gains visibility, others lose it. This means that monitoring competitor SoM is essential for understanding both threats and opportunities.

Based on data collected in our 2026 study, the most successful AEO programmes track SoM for at least 3-5 named competitors alongside their own brand. This competitive tracking reveals which brands are gaining or losing ground and helps identify the content strategies driving those shifts.

Our 2026 study suggested that brands entering a new category or launching a competitive displacement campaign should target the "competitive" tier (10-25% SoM) as an initial milestone. Once in this tier, the brand is consistently appearing in AI responses and can begin optimising for position within responses rather than just presence.

Methodology

How this research was conducted

SoM benchmarks are calculated from data collected in our 2026 study across all sampled brands and categories. We ran query banks across major AI engines, and recorded and attributed brand mentions in each response. SoM is calculated as (responses mentioning brand / total responses) per category per engine.

Benchmark tiers, medians, and percentiles are derived from the distribution of SoM scores across all sampled brands and categories. Figures are a snapshot and represent estimates based on the brands and categories in our 2026 study.

FAQ