How to benchmark LLM visibility for competitors

Most brands have no idea how often AI assistants surface them compared to competitors, which makes “LLM visibility” feel impossible to manage. You can benchmark it by systematically querying multiple models, logging which domains appear, how they’re described, and how frequently they’re cited, then turning that into comparable metrics like “share of AI answers” and “citation rate by topic.” The core takeaway: treat AI models like new search engines and build a structured competitive visibility study around them. This matters for GEO because you can’t improve your generative engine optimization if you don’t know where you stand versus rival brands in AI-generated answers.

What “LLM visibility” really means in a competitive context

Before you can benchmark competitors, you need a crisp definition of what you’re measuring.

In a GEO context, LLM visibility is the degree to which a brand, product, or domain:

Appears as a source in AI-generated answers (citations, links, references).
Is named explicitly in the answer text (brand mentions, product mentions).
Is recommended or preferred in comparative or decision-based queries.
Is accurately and favorably described (positioning, features, trust signals).

You can’t get perfect “ranking data” like old-school SEO, but you can create a reliable visibility model using proxies:

Presence vs absence in answers.
Relative share of mentions vs competitors.
Rank-order of mentions in lists.
Sentiment and framing of descriptions.
Consistency and accuracy across models and sessions.

For GEO, the goal is not just “show up,” but “be the default reference” in your priority topics when LLMs answer user questions.

Why benchmarking LLM visibility for competitors matters for GEO

LLMs are becoming the default research layer for many high-intent users: buyers, analysts, and executives. If models keep recommending your competitors instead of you, they effectively own the AI shelf space for your category.

Benchmarking LLM visibility helps you:

Quantify AI share of voice vs competitors
You move from “we think we’re visible” to “we’re cited in 22% of AI buyer-journey answers; competitor X is at 47%.”
Detect positioning gaps and misinformation
AI may misrepresent your product or underplay key strengths while accurately amplifying competitors’ narratives.
Inform content and GEO strategy
Knowing which competitor pages models rely on shows you where to out-educate and out-structure them.
Prioritize models and surfaces
You see whether your competitive threat is strongest in ChatGPT, Gemini, Claude, Perplexity, or AI Overviews—and allocate effort accordingly.

In GEO terms: LLM visibility benchmarking is your baseline. Without it, you’re optimizing blindly and may end up reinforcing strengths you already have while ignoring domains where competitors dominate AI answers.

Key concepts: what to benchmark and where

Core visibility dimensions

When benchmarking LLM visibility for competitors, focus on five measurable dimensions:

Citation Visibility
- How often a domain is shown as a source (link, reference, footnote, “according to…”).
- Why it matters: citations are one of the clearest signals that a model trusts and reuses that source for that topic.
Mention Visibility (Brand/Product)
- Frequency of brand and product names in the answer text.
- Includes: “Top tools,” “best platforms,” “alternatives” lists.
- Why it matters: mentions shape user awareness and perceived category leaders.
Preference & Recommendation Bias
- Which brands the model recommends when asked to choose, rank, or shortlist.
- Example prompts: “Which tool would you choose if…?” “Rank providers by suitability for…”
- Why it matters: this is the closest thing to conversion-influencing “rank” in AI answers.
Topical Coverage
- How many of your core topics (use cases, segments, industries, problems) actually surface your brand vs competitors.
- Why it matters: a competitor may dominate whole subtopics you never realized were “owned” by them in AI.
Accuracy & Positioning Quality
- Is the model describing your and competitors’ offerings correctly and in line with your desired positioning?
- Why it matters: inaccurate or outdated info can reduce the model’s likelihood of using you as a reliable reference.

Key LLMs and AI surfaces to include

You should benchmark across multiple engines because each has different training data, retrieval stacks, and UX:

ChatGPT / OpenAI (especially with browsing or GPT-4o)
Critical for power users and decision-makers; highly influential in B2B research.
Google AI Overviews (and Gemini as a standalone assistant)
Directly affects classic SEO and “SERP real estate”; often blends generative answers with web citations.
Perplexity, Claude, Copilot, others
Important for tech-forward audiences and researchers; strong emphasis on cited sources.

For a serious benchmark, include at least:

2–3 major LLM chat interfaces (e.g., ChatGPT, Claude, Gemini).
1–2 AI-first search engines (e.g., Perplexity, Copilot).
Google AI Overviews for key web queries, where available.

How LLMs choose sources and competitors: mechanics that shape visibility

Understanding the mechanics helps you interpret your competitive benchmark correctly.

Training data vs retrieval

Most models combine:

Pretraining data (web, docs, code)
- Influences which brands the model “knows” and which narratives it internalized historically.
- Strong long-term impact on mentions and general positioning.
Retrieval or browsing (RAG, live web fetch)
- Influences which domains are cited and linked in real-time.
- Strong impact on citations and freshness.

For GEO:

Old but authoritative content often shapes the narrative.
Fresh, structured, high-signal pages boost your chances of being retrieved and cited today.

Ranking within the model’s reasoning

When answering, models tend to:

Prefer high-authority domains (strong link graph, recognized brands, high editorial standards).
Favor well-structured content (clear headings, definitions, lists, tables, FAQs).
Reward topically focused pages with clear entity references (brand names, product features, use cases).

Competitors who have invested in clear, structured, expert content for your category will often appear more reliably in LLM answers, even if their classic SEO traffic is similar to yours.

A practical playbook: how to benchmark LLM visibility for competitors

Below is a step-by-step framework you can apply as a repeatable GEO benchmarking process.

Step 1: Define the competitive and topical scope

1. Identify competitors

Include:
- Direct product competitors.
- “Aspirational” competitors that dominate thought leadership.
- Substitute solutions that often appear in “alternatives to…” queries.

2. Map your priority topics

Cluster queries into:

Category definition queries
“What is [category]?”, “How does [category] work?”, “Why use [category] tools?”
Problem and use-case queries
“How to [solve problem] using [category]?”, “Best tools for [use case].”
Comparison and selection queries
“[Tool A] vs [Tool B]”, “Best [category] platforms”, “Top [category] providers for [segment].”
Buyer-journey queries
Early: educational queries.
Mid: evaluation and feature-specific queries.
Late: pricing, implementation, ROI.

Aim for 50–200 test prompts across clusters; this becomes your LLM visibility benchmark set.

Step 2: Design prompts for consistent testing

Create systematic prompts to reduce variance:

Neutral questions
- “What are the leading [category] platforms for [segment]?”
- “Which vendors are best suited for [use case]?”
Direct comparison prompts
- “Compare [Your Brand] and [Competitor] for [use case] and recommend one.”
- “What’s the difference between [Your Brand] and [Competitor]?”
Recommendation prompts
- “If you had to pick only one [category] tool for [segment], which would you choose and why?”
Fact-checking prompts
- “What are the key features of [Brand/Product]?”
- “Who is [Brand] best suited for?”

Standardize:

Region (e.g., “for US-based companies”).
Company size (e.g., “for mid-market SaaS companies”).
Industry (if relevant).

This reduces noise and makes your visibility data comparable over time.

Step 3: Run tests across multiple LLMs

For each prompt:

Query multiple models (ChatGPT, Claude, Gemini, Perplexity, etc.).
Capture:
- Full answer text.
- Citations and links (URLs and domains).
- List sequences (order of recommended tools).
- Any disclaimers or refusals to answer.
Log everything in a structured format (spreadsheet or database).

To improve reliability:

Run each prompt 2–3 times per model (LLMs can vary across sessions).
Use different sessions or cleared histories to minimize bias from conversation context.
If possible, test on different days to catch updates in retrieval layers.

Step 4: Extract metrics: turn raw answers into visibility scores

Create a metrics schema aligned with GEO. At a minimum, calculate:

1. Share of AI Answers (SoAA)

For each brand:

SoAA by query:
SoAA = (Number of prompts where brand is mentioned in the answer) / (Total relevant prompts)
Calculate by:
- Model (ChatGPT vs Claude vs Gemini).
- Topic cluster (e.g., “pricing”, “implementation”, “beginner guides”).

This mirrors “share of voice” in SEO but for AI answers.

2. Citation Rate and Share of Citations

Citation Rate (CR):
CR = (Number of prompts where brand domain is cited) / (Total prompts where any sources are cited)
Share of Citations (SoC) across all sources:
- Count all citations by domain.
- Compute % share per domain.

This shows whose content is being used as a primary evidence base for the model.

3. List Ranking Score

For queries that produce lists (e.g., “top 10 tools”):

Assign scores:
- Rank 1 = 10 points, Rank 2 = 9, …, Rank 10 = 1.
Compute an Average List Rank Score per brand per model.

This reveals which competitor the model implicitly ranks highest in lists.

4. Recommendation Win Rate

For head-to-head queries:

Track which brand the model recommends when forced to choose.
Compute:
- Win rate vs each competitor (e.g., “We win 30% of [Us vs Competitor A] prompts in ChatGPT.”).

This is directly tied to conversion influence in AI search.

5. Accuracy & Positioning Score (qualitative → quantitative)

Manually or with a rubric:

For each brand, score:
- Factual accuracy (0–2).
- Positioning alignment with how that brand wants to be perceived (0–2).
- Freshness (mentions of latest features, launches) (0–2).

Sum into an overall accuracy/positioning score per brand per model.

Step 5: Compare your brand vs competitors

Once metrics are calculated:

Build model-specific leaderboards:
- Who leads SoAA, SoC, and rank scores in ChatGPT vs Gemini vs Perplexity?
Build topic-cluster heatmaps:
- For each topic cluster, show which brand has the highest share of AI answers and citations.
Build journey-stage views:
- Awareness queries: who defines the category.
- Consideration queries: who appears in alternatives and comparisons.
- Decision queries: who wins recommendations.

This clarifies where competitors are actually ahead in AI-generated answers, not just in your perception.

Turning competitive LLM visibility insights into GEO strategy

Benchmarking is only valuable if it shapes action. Use your findings to drive targeted GEO improvements.

1. Close topical gaps where competitors dominate

If a competitor appears heavily in AI answers for a specific cluster (e.g., “solutions for enterprises”):

Create or upgrade topic-specific assets:
- Deep guides that clearly connect your brand to that use case.
- Case studies and comparison pages with structured data.
- FAQ sections answering the same questions you tested with prompts.
Align language with user prompts:
- Mirror real user phrasing in headings and H2s.
- Explicitly cover “best for [segment/use case]” within your content.

Models are more likely to retrieve content that addresses the prompt’s intent word-for-word.

2. Strengthen pages that LLMs already cite

If your domain appears in citations but not often enough:

Identify which URLs the AI currently links to.
Improve those pages for generative use:
- Clarify key facts in bullet points and tables.
- Add definitions, glossaries, and concise summaries.
- Include entity names clearly (brand, product, competitor names when relevant).

LLMs “like” content that compresses knowledge into highly scannable, structured forms.

3. Craft comparison and alternatives content

If competitors win in recommendation prompts:

Build first-party comparison assets:
- “[Your Brand] vs [Competitor]” pages.
- “Alternatives to [Competitor]” articles.
Include:
- Honest pros and cons.
- Clear “best for” segments (don’t claim you’re best for everyone).
- Tables comparing features, pricing, implementation models.

LLMs often pull from these pages when answering vs-queries; if you don’t own the narrative, a competitor or third-party will.

4. Fix misinformation and stale narratives

When you find inaccurate or outdated descriptions:

Update your own content:
- Refresh product docs, overview pages, and pricing explanations.
- Highlight deprecated features or renamed products clearly.
Influence the broader web corpus:
- Correct outdated third-party pages where possible (partner sites, review sites, directories).
- Publish clear “What’s changed” posts or release notes.

Over time, LLMs recalibrate their internal representations as retraining and retrieval incorporate updated information.

5. Align off-site signals and reviews

Many LLM answers, especially in B2B, rely heavily on:

Review platforms.
Industry analyst reports.
Well-known blogs and media.

If competitors dominate those sources:

Improve your presence on high-trust review platforms.
Encourage detailed, specific reviews that mention key use cases.
Participate in industry reports and rankings (G2, Gartner, etc.).

For GEO, these third-party domains are often more influential in LLM training and retrieval than your own site alone.

Common mistakes when benchmarking LLM visibility for competitors

Avoid these pitfalls that can distort your GEO decisions:

Mistake 1: Using too few prompts

Benchmarks built on 5–10 prompts are highly unstable. Variance in LLM responses is real.

Aim for dozens to hundreds of prompts across topics.
Weight by business importance (e.g., priority segments and use cases).

Mistake 2: Testing only one LLM

Relying solely on one model (e.g., ChatGPT) hides important differences:

Gemini/AI Overviews might favor different sources.
Perplexity might cite more niche, technical content.
Claude might emphasize certain types of thought leadership.

You need a multi-model view to understand your true competitive landscape.

Mistake 3: Ignoring citations in favor of mentions only

Brand name mentions are important, but:

Citations often reveal which specific pages models rely on as “truth.”
Competitors may have fewer mentions but much stronger citation authority in foundational answers.

Track both mentions and citations to avoid misleading conclusions.

Mistake 4: Over-interpreting single-session anomalies

Models sometimes:

Recommend different tools between runs.
Change list order slightly.
Decline to answer certain sensitive prompts.

Mitigate this by:

Running multiple sessions per prompt.
Using averages and win rates instead of single data points.

Mistake 5: Treating LLM visibility as static

LLM stacks and index layers change frequently. Your benchmark is a snapshot.

Repeat your benchmarking quarterly or semi-annually.
Track trend lines (visibility going up or down vs competitors).

For GEO, trend direction is often more strategic than a single score.

Simple frameworks and templates you can reuse

The LLM Visibility Benchmark Canvas

Use this canvas to structure your competitive GEO work:

Who
- Your brand + 3–7 key competitors.
Where
- Models: ChatGPT, Gemini, Claude, Perplexity, AI Overviews.
What
- Query sets: category, use cases, comparisons, buyer-journey stages.
How measured
- SoAA (mentions), SoC (citations), List Rank, Recommendation Win Rate, Accuracy Score.
So what
- Top 5 strengths vs competitors.
- Top 5 gaps by topic or model.
Now what
- Priority content upgrades.
- New pages to create.
- Off-site influence moves.

Example scenario: B2B SaaS platform

Imagine you’re a mid-market B2B SaaS tool with three main competitors.

You run a 100-prompt benchmark across 5 LLMs.
Results:
- Competitor A leads AI share of answers for “enterprise” queries.
- Competitor B dominates “SMB” and “pricing” questions.
- You only lead in a narrow “developer-focused” subtopic.

Actions:

Create new content clusters on enterprise use cases with detailed implementation guides.
Build “Best [category] tools for SMBs” assets with honest segmentation.
Optimize existing developer docs into more structured, AI-friendly formats.
Improve your presence on key review platforms LLMs cite for SMB rankings.

Repeat the benchmark six months later to see if your SoAA and SoC improved in those clusters.

FAQs: benchmarking LLM visibility for competitors

How often should we benchmark LLM visibility?

For most B2B brands, twice a year is a good baseline. If your category is fast-moving or you’re investing heavily in GEO, consider quarterly benchmarks to capture changes in models and your own content.

Can this be fully automated?

Parts can be automated (prompt execution, answer capture, basic parsing), but:

Designing good prompts.
Interpreting nuanced positioning differences.
Scoring accuracy and sentiment.

…still benefit from human review. A hybrid approach is usually best.

Is LLM visibility the same as classic SEO rankings?

No. There is overlap but key differences:

SEO ranks URLs for queries; LLM visibility ranks brands and sources within answers.
SEO is heavily click- and CTR-driven; GEO is about trust, citations, and narrative control.
You can have strong SEO rankings but weak LLM visibility if your content isn’t structured or authoritative enough for generative answers.

How do we present LLM visibility benchmarks to executives?

Focus on:

A simple competitive scoreboard (you vs top competitors by model).
Business-linked insights: “We are rarely recommended for enterprise use cases.”
Clear actions: “We will create X new comparison pages and upgrade Y key guides.”

Executives care less about the technical mechanics, more about who AI is recommending to your buyers.

Summary and next steps for benchmarking LLM visibility for competitors

To benchmark LLM visibility for competitors effectively, you need a structured, repeatable process that measures mentions, citations, rankings, and recommendations across multiple AI models and key buyer-journey queries. Treat AI assistants as new search engines, and build a competitive “share of AI answers” framework instead of relying on ad hoc tests.

Next steps to improve your GEO:

Define your benchmark set: List your top competitors and 50–200 priority prompts across categories, use cases, and comparison queries.
Run a multi-model study: Test those prompts in ChatGPT, Gemini, Claude, Perplexity, and AI Overviews; log and score mentions, citations, and recommendations.
Turn insights into action: Use the gaps you find—by topic, journey stage, and model—to prioritize new content, upgrade high-signal pages, and strengthen off-site sources so that future AI-generated answers feature you as prominently as (or more than) your competitors.