Tracking & Monitoring

Per-Model AI Visibility Comparison

Measuring how a brand performs in ChatGPT vs. Gemini vs. Claude vs. Perplexity side by side, so you can spot the exact models where you're invisible.

TL;DR

AI model comparison means checking how your brand performs across each AI model , ChatGPT, Gemini, Claude, Perplexity and more. You can be a default pick in one and completely invisible in another.

prompt set, all models

per-model

gaps revealed

steps to act

Explain it like I've never heard of this

Imagine asking the same question to four different experts and getting four different answers. One recommends you warmly, one mentions you in passing, and one has never heard of you. That's exactly what happens across AI models.

AI model comparison is running the same questions across every model and lining up the answers, so you can see precisely where you're winning and where you're missing.

Same prompt set · 4 models

Prompt: "best CRM for small teams"

ChatGPT

Mention rate78%

Position#2 of 5

Citations12 sources

● Strong presence

Perplexity

Mention rate64%

Position#3 of 6

Citations9 sources

● Partial coverage

Claude

Mention rate41%

Position#5 of 7

Citations4 sources

● Partial coverage

Gemini

Mention rate12%

Positionnot listed

Citations0 sources

● Blind spot

Same prompt, four models, four very different results — Gemini is a blind spot.

"AI model comparison" answers: am I equally visible across all the AI models my buyers use, or only some?

Words you'll see, in plain English

These terms come up whenever people compare across AI models. Here's what each means.

AI model comparison

Running the same questions across different AI models and lining up the results side by side.

Training cutoff

The date a model's knowledge stops, anything newer it only knows via live retrieval.

Retrieval stack

The systems a model uses to pull in fresh web sources when it answers.

Per-model gap

When you're strong in one model but weak or invisible in another.

Mention rate

How often a model names your brand when answering relevant questions.

Source preference

The third-party sites a specific model tends to trust and pull from.

Why model-by-model tracking matters

Each model has its own training cutoff, retrieval stack, and ranking pipeline. A brand can be a default recommendation in ChatGPT and effectively invisible in Gemini. Without per-model tracking, those gaps stay hidden, and so does the easiest path to fixing them.

How to act on the data

A comparison is only useful if it points to a next step. Here's the loop.

Run the same set everywhere

Use one identical prompt set across every model so the comparison is apples-to-apples, not noise.

Find your weakest model

Identify where your brand underperforms in its category, that's where the biggest opportunity hides.

Investigate the source mix

Each model favors different sources. Gemini leans on YouTube, Reddit, and Google's knowledge graph, for example.

Close the gap deliberately

Build presence on the sources your weak model trusts, then re-run to confirm the needle moved.

Your AI model comparison checklist

The exact same prompt set runs on every model
Results are broken down model by model
Mention rate, position, and citation are tracked per model
Your lowest-performing model is identified
You know which sources each weak model favors
You re-run after changes to confirm the gap closed

Frequently asked questions

Why does my brand appear in one model but not another?

Each model has its own training cutoff, retrieval stack and ranking pipeline. A brand can be a default recommendation in ChatGPT and effectively invisible in Gemini, because the two are drawing on different sources and weighing them differently.

Do I have to run the same prompts on every model?

Yes — that is the whole point. One identical prompt set across every model is what makes the comparison apples-to-apples; different prompts per model produce noise you cannot act on.

Which model should I fix first?

The one where you underperform most relative to your category, not the one with the lowest raw score. That gap is where the largest opportunity sits, because the demand already exists and you are simply absent from it.

Why does Gemini behave so differently?

Its source mix leans on YouTube, Reddit and Google's knowledge graph. A brand strong in written industry publications but absent from those surfaces will see the difference immediately.

How do I close a gap on a specific model?

Build presence on the sources that model trusts for your category, then re-run the same prompt set and confirm the needle actually moved. Without the re-run you are guessing.

See how your brand performs across AI assistants

Strajist tracks your visibility, share of voice, and citations across ChatGPT, Gemini, Claude, Perplexity, and more.

Start free trial

Related terms

Multi-Model Visibility

Measuring and improving brand visibility across multiple AI models at once, instead of optimizing for just one.

AI Search

Search experiences where the result is a synthesized answer generated by a large language model, often with inline citations, instead of a list of links.

AI Visibility Score

A composite metric that summarizes how strongly a brand appears across AI answers, combining mention rate, position, and citation share into a single 0–100 number.

AI Perception Intelligence

The discipline of measuring how AI models describe, categorize, and contextualize a brand, and which sources shape those descriptions. One layer beneath visibility tracking.

Brand Sentiment in AI

Whether AI-generated answers describe a brand in positive, neutral, or negative terms.

Competitive Intelligence (AI)

Tracking which competitors AI models recommend in your category and how their visibility evolves over time.

Back to full glossary