Gemini vs ChatGPT vs Perplexity: how to appear in all AI models

Your potential customer might ask ChatGPT in the morning, Gemini on Google in the afternoon, and Perplexity when looking for verified sources. If you only appear in one of the three, you lose two out of three opportunities. This guide explains how to build a GEO strategy that works across all three main models simultaneously, without duplicating effort.

Profile of each model and its audience

ChatGPT (OpenAI)

With over 200 million active users, ChatGPT is the reference LLM for long conversations and complex queries. Its GPT-4o and higher models have a fixed knowledge cutoff supplemented with real-time browsing when the user activates it. ChatGPT users tend to ask consultative and high-value questions: "I need a lawyer specialized in...", "what company do you recommend for...", "explain to me how... works"

ChatGPT's audience is the most massive and diverse of the three. It includes professional users, consumers, students, and businesses. For high-value B2C sectors and for B2B with intermediate decision-makers, ChatGPT tends to be the model with the greatest impact.

Google Gemini

Gemini is Google's model, integrated into Search (as AI Overviews), Google Workspace and as a standalone assistant. Its main advantage is real-time web access and integration with the Google ecosystem. Gemini users arrive from the search context, which means their questions have high resolution intent: they are actively looking for a solution.

Gemini has privileged access to Google's index and Business Profile. For businesses with local or regional presence, Gemini is especially important because it combines Google's search intent with AI's generative capability. A business well-optimized for Google local has a starting advantage for Gemini.

Perplexity AI

Perplexity is an answer engine that always queries the web in real time and cites its sources. Its user profile is more technical and professional: journalists, researchers, consultants, executives. Responses include explicit references to sources, which makes it especially valuable for authority positioning: if Perplexity cites you, it validates your credibility explicitly before a demanding audience.

Key differences for positioning

Real-time data access

• ChatGPT: mixed (base knowledge + optional search activated by the user)
• Gemini: always with access to the updated Google index
• Perplexity: always with real-time web search as standard process

Practical implication: for Gemini and Perplexity, solid SEO and updated content are more directly relevant. For ChatGPT, accumulated historical signals (presence in multiple sources over time) carry more weight than content published yesterday.

Which sources they prioritize

• ChatGPT: sources with high representation in its training corpus: relevant media, authority websites, Wikipedia, reference forums like Reddit and Quora
• Gemini: sources that already rank well on Google + Google Business Profile + content with elevated E-E-A-T + Google Reviews
• Perplexity: sources with domain authority, fresh content, direct and well-structured answers, with clean semantic HTML

Length and depth of responses

ChatGPT and Claude tend to generate longer, more conversational responses. Gemini in AIO mode generates shorter, more concise responses, oriented toward quick resolution. Perplexity generates medium-length responses with explicit citations. To optimize for all three, your content needs to have both short and direct fragments (for Gemini and Perplexity) as well as long and complete sections (for ChatGPT).

Common factors: where to focus 80% of the effort

The good news is that there is a set of factors that positively influence all three models. Concentrating effort here produces the greatest return:

• Presence in authority media: an article in a relevant medium helps in ChatGPT, Gemini and Perplexity simultaneously. It's the action with the greatest multi-model effect.
• Complete and correct Schema.org: all three process structured data and use it to build their understanding of your entity.
• Comprehensive and well-structured content: long guides, FAQs, case studies. Quality content is the common denominator of all models.
• Optimized Google Business Profile: directly influences Gemini and has indirect effect on the other two via Google ecosystem credibility.
• Quality and quantity of reviews: universal trust signal for all three models.

Unified three-model strategy

The GEO strategy from GEOMOND is designed from the start to maximize visibility across the four main models (we add Claude to the mix). The principle: building a solid entity presence with authority signals distributed across all relevant sources is the strategy that amplifies most across all models at once.

The only model-specific adjustments are tactical: for Gemini, pay special attention to Google Business and AIO; for Perplexity, prioritize technical SEO and content freshness; for ChatGPT, long-term media and Wikipedia amplification.

The multi-model Presence Index

GEOMOND's Presence Index measures your visibility across the four main models and generates a weighted score that reflects your global visibility in AI. A business can have a high Presence Index in ChatGPT and low in Gemini —or vice versa— depending on whether it has worked more on SEO or more on historical GEO.

The multi-model metric is the only one that gives a complete and actionable picture of your AI visibility. Knowing your distribution by model is the first step to knowing exactly where to focus the next quarter of work for the greatest possible impact.

Claude and emerging models: don't ignore them

Beyond Gemini, ChatGPT and Perplexity, there are other models gaining usage share that are part of a complete GEO strategy. Anthropic's Claude has a growing professional user base, especially in sectors like technology, consulting and financial services. Its focus on detailed and well-structured responses makes it especially interesting for companies with high-quality technical content.

Models integrated into productivity assistants (Microsoft Copilot in Windows and Office, Google Assistant with Gemini, etc.) are another emerging front. With hundreds of millions of Office 365 and Google Workspace users, these models integrated into daily workflow have enormous recommendation potential for B2B services. The user who asks Copilot "what digital transformation consultancy do you recommend?" while working in Excel is exactly the decision-maker profile that interests many service companies most.

How to adapt content to the peculiarities of each model

Each model has slightly different citation patterns that can be leveraged strategically. ChatGPT tends to especially value content with clear structure and well-defined paragraphs, sources with a history of accuracy, and content that addresses the question directly and without detours. Articles with FAQPage format have high citation rates in ChatGPT.

Gemini, being integrated with Google, has privileged access to Google's knowledge graph and the real-time web index for the version with web browsing enabled. For Gemini, traditional SEO factors (Google position, domain authority, entity consistency in Knowledge Graph) carry more weight than for other models. Investing in SEO also directly improves visibility in Gemini.

Frequently asked questions about multi-model GEO

Should I prioritize one model over another? Prioritization depends on your customer profile. For professional B2B: prioritize LinkedIn + Gemini. For consumers with high purchase intent: prioritize ChatGPT. For technical users looking for verified sources: prioritize Perplexity. Ideally, a solid GEO strategy covers all simultaneously with model-specific tactical adjustments.

Does Gemini have an advantage from being integrated with Google? Yes, significant. Gemini has direct access to Google's index and Google Business Profile, which means companies with good local SEO and complete GBP have a natural advantage to appear in Gemini. It's an additional reason to work on local SEO and GBP as part of the GEO strategy.

The correct strategy is to build a solid authority base that works across all models (the five pillars of GEO are universal), with tactical adjustments per model where differentiation justifies it. At GEOMOND, we monitor the evolution of emerging models to incorporate them into the strategy at the moment their adoption justifies it for each sector and customer profile.

Is it worth optimizing for Anthropic's Claude or other minority models as well? In 2025, the concentration of the consumer LLM market in Spain is clear: ChatGPT, Gemini and Perplexity represent more than 85% of usage. Claude has a more technical and professional user profile. Specifically optimizing for Claude adds little marginal effort if you're already executing the 5 pillars well, because Claude also values domain authority, structured content and mentions in quality sources. A good general GEO strategy covers all relevant models.

Multi-model strategy does not add insurmountable complexity to GEO execution: the fundamental pillars (clear entity, quality content, amplification in authority sources) are common to all models. Tactical adaptations are fine adjustments on a solid foundation. Correctly implementing the 5 pillars of GEO in one model produces improvements in all others as a side effect. This is how a well-designed GEO methodology works: work on one front reinforces all others simultaneously.

Diversification of presence across AI models is not just a defensive strategy: it's the way to maximize the total reach of your business in the generative AI channel. Each model has its own audience and its own usage patterns. Being present in all relevant models multiplies the number of potential users who can receive a recommendation of your company when they ask AI about your sector.