For years, [search](https://www.perrill.com/google-shouldnt-reveal-search-algorithm/) engine optimization followed one simple rule: optimize for Google to win. That rule shaped how brands built websites, wrote content, and measured visibility. The playbook was clear—rank high, earn clicks, repeat. Google was the sun [SEO](https://www.perrill.com/seo-isnt-dead-9-tactics/) specialists orbited around.

But that world is gone.

Today's customers move fluidly between Google and Bing to ChatGPT, Perplexity, Gemini, Claude, and Copilot. They use these tools not just to search but to learn, compare, validate, and decide. Each platform runs on its own architecture, training data, and logic—meaning five different tools can give five different answers to the same question.

A single-platform [generative engine optimization strategy](https://www.perrill.com/generative-engine-optimization-services/) is no longer safe. To stay visible in this multi-model world, your brand needs to show up accurately wherever your audience is looking for answers.

## Comparing AI Platforms: Share of Model

Most brands still treat AI platforms as neutral gateways—just new versions of “search.” But they're not neutral at all. Each model interprets reality differently because each one is built, trained, and maintained in its own way.

What earns visibility in one engine might barely register in another. One might describe your brand as an industry leader. Another might barely know you exist.

To consistently show up as the answer—not just *an* answer—your brand needs to understand the hidden logic each AI model runs on. They shape how models pull, process, and present information and whether your brand rises to the surface or gets lost in the noise.

Here's a breakdown of the key differences:

## AI models comparison

The divergence goes deeper than ranking factors. Comparing visibility across **answer engines** and **traditional search** is now part of measuring search presence, because these **large language models** don’t just serve different links—they construct different realities for **users** based on their data sources, reasoning approaches, and retrieval logic:

* Perplexity and SearchGPT pull dynamically from the live web, prioritizing recency and timeliness.

* ChatGPT and Claude rely primarily on large-scale training data and curated sources, with limited or optional retrieval layers that emphasize synthesis over real-time citation.

* Gemini and Copilot blend training data with retrieval, but filter heavily through their own proprietary ecosystems – Gemini favors sites with strong authority across Google’s product suite, Copilot leans into the Microsoft stack.

That distinction matters because the optimization work looks completely different based on the preferred content types—and it directly shapes which models are most relevant to your audience. It’s also why a single-platform strategy leaves you blind to entire segments of your audience.

## Targeting the right models for your audience's AI answers

Understanding which large language models and answer engines your audience is choosing is crucial to building a smart [GEO strategy](https://www.perrill.com/essential-steps-to-make-your-website-llm-ready/). And the platforms your audience uses might be less of a personal preference and more about structural context.

Consumers increasingly rely on AI to recommend products and make purchasing decisions, often bypassing traditional search engines.

Some may look down at their keyboard and see a native Microsoft Copilot button on their work-issued computer. Some highly remote workplaces might be entrenched in the Google ecosystem of Drive, Gmail, and the familiar Gemini. A startup high on ingenuity and productivity but low on established technology policies and equipment might be quick to adopt ChatGPT.

Over 400 million people use OpenAI products weekly, which is exactly why audience platform behavior matters when deciding where to focus.

Analyzing your brand’s referral data sources and prompt entry over the course of 30, 60, or 90 days will help determine what you should be focusing on. This is how Share of Model™ becomes useful: by showing where your brand appears through brand mentions, AI mentions, and AI responses across the platforms your audience already uses. Those visibility patterns create clearer insights and help tie attention in these environments to measurable business outcomes. Does your brand involve high-level services with many resources and content pieces? You might see the most traffic from Claude or ChatGPT. Is your brand’s ideal audience a business with many locations and/or an established, long-time business with highly thorough processes and technology? You might see Gemini and CoPilot.

In reality, while some of your target customers might use one LLM more than another, the diversified strategy ensures you’re not leaving anyone out of your targeting and can capture all aspects of your audience. Focusing on the 3-4 models that will bring the most value to your business will help stem the ebbs and flows of the rapid changes amongst all LLMs over time.

## **Measuring performance across models**

As benchmarks and tracking methods shift constantly, one thing is becoming clear: brands need a consistent metric that cuts across models and platforms to measure their true impact across AI engines.

In traditional SEO, metrics like average position and share of voice gave you a clear read on visibility. GEO has its own equivalents – but with an important caveat: a single averaged score across multiple LLMs tells you less than strong, specific performance within each one.

The most useful [measurement framework](https://www.perrill.com/how-to-measure-generative-engine-optimization-campaigns/) is share of model, a KPI that measures how often a brand is mentioned, recommended, or referenced by AI models compared to [competitors](https://www.perrill.com/what-are-my-competitors-doing-better/). Measurement tracks those brand citations as a proportion of total category mentions across different AI platforms. Rather than blending performance into one blunt number, share of model gives you granular insight into where you’re winning and where you have gaps; to calculate market share for a specific model, you define the prompt set or other parameters, then divide that model’s performance by the total market or category size.

This matters because the fix for underperforming in Gemini looks very different from the fix for underperforming in Perplexity, especially when AI responses reshape visibility and [direct traffic](https://www.perrill.com/what-is-direct-traffic/) keeps falling. Broad, averaged metrics smooth over those differences. Share of model surfaces them, giving teams clearer priorities that connect visibility to measurable business outcomes.

### Visibility is the new ranking in answer engine optimization

At Perrill, we get it. Leads, sales, and revenue are the most important goals. But because LLM models typically keep traffic within their ecosystems, the path to driving those goals has changed.

Rather than higher rankings leading to more traffic, which translates to more sales, leads, and revenue, high visibility and share of model will drive discovery and opportunity. 

Improving your share of model metrics today means a higher share of mentions in relevant prompts across the different LLMs for the future—and that's where the opportunity is for your brand.

## Diversify your LLM strategy with Perrill to stay ahead

Every model your brand isn’t visible in is a conversation you’re not part of — and a potential customer who finds a competitor instead. Gartner predicts traditional search volume will drop 25% by 2026 as AI answer engines grow, which is why tracking brand's visibility matters more than relying only on search results. A diversified LLM strategy isn’t a future investment. It’s the baseline for staying competitive today.

The brands that invest in cross-platform AI visibility now will have a significant advantage as ai models continue to shape how buyers discover, evaluate, and choose through ai generated responses, ai generated answers, ai answers, ai results, and even voice assistants. That shift requires answer engine optimization, with a clear focus on optimizing, optimizing content, and using keywords, schema markup, and the right page structure so content appears clearly across your site for ai systems and ai agents.

The brands that act early won’t just rank higher. They’re going to define what authority looks like in their category by finding content gaps, building smarter strategies, and using AI-driven insights to create stronger visibility across the tools and ads that influence discovery.

If you’re ready to build a diversified LLM strategy, [reach out](https://www.perrill.com/contact/). Our team of [GEO and SEO specialists](https://www.perrill.com/generative-engine-optimization-services/) will work with you to understand which LLMs your audience is using before analyzing your current visibility and constructing a strategy to grow your presence where it matters most.