What the AI Answer Density page shows
The AI Answer Density page compares how ChatGPT, Perplexity, Gemini, Claude, and other AI models differ in how densely they reference brands and URLs in their responses. It answers a practical question: is your visibility gap a content problem, or a model behavior problem?
Why model-level response structure affects how you interpret visibility gaps
Different AI models have different response patterns. Some models consistently name five or six companies per answer. Others name one or two. Some cite five external URLs per response. Others rarely cite any. If you're tracking your brand mentions across multiple models and seeing discrepancies, you need to know whether those gaps reflect your actual visibility - or simply the structural tendencies of each model. This page gives you that baseline.
How ALLMO calculates brand and URL density distributions per model
For every response in your report, ALLMO counts how many companies were mentioned and how many URLs were cited. These counts are aggregated by AI model to produce statistical distributions:
Companies per response - shown as a box plot and a table with min, max, average, and median values per model.
URLs per response - same structure, for citation density.
The box plot gives you an at-a-glance comparison. The table below it gives you the precise numbers. Models are sorted by response volume so the most data-rich comparisons are at the top.
How to read the box plots and distribution tables
Open Answer Density from the Explore section in the sidebar.
Scan the Companies per response box plot. Models with a higher median and wider range tend to mention more brands per answer - your visibility within those models has more competition but also more surface area.
Check the URLs per response box plot. A model with a near-zero median likely has web search disabled or rarely cites sources - domain citations in those models are less relevant to track.
Use the tables below each plot for precise figures when comparing two specific models.
Hover over any row in the box plot for the detailed tooltip: min / Q1 / median / Q3 / max / average.
How to adjust your visibility strategy based on model behavior
If a model has very low citation density: Focus on brand mentions rather than domain citations when optimizing for that model. Optimizing for URL citations there will have diminishing returns, because it's a signal that the model will often choose it's training data memory over live retrieval for the prompts you monitor.
If your mention rate is lower on a model with high companies-per-response: More competition per answer means you need stronger brand signals to appear. Review which sources are being cited in that model's responses.
If your mention rate is higher on a model with low companies-per-response: Good news - you're one of the few brands making it into tight, selective responses. Maintain the content signals that got you there.