How Different AI Models See the Same Brand Differently

One brand, eight opinions

Ask ChatGPT to recommend a CRM tool and you might get Salesforce, HubSpot, and Pipedrive. Ask Claude the same question and the list might be different. Ask Gemini and you could get an entirely different set of recommendations with different reasoning.

This isn't a bug — it's a feature of how these models work. Each AI model has been trained on different data, with different objectives, by different teams. They weigh sources differently, interpret context differently, and have different ideas about what constitutes authority.

Why the differences matter

If your brand appears strongly in ChatGPT but is absent from Claude, you have a visibility gap. ChatGPT might have picked up your brand from press coverage and reviews, while Claude's training data might not include those sources — or might weight them differently.

These gaps are actionable. Understanding which models mention you and which don't tells you something specific about your content footprint and citation profile. A brand that dominates Reddit discussions might score well in models that weight forum content, but poorly in models that prioritise academic or news sources.

Cross-model patterns we see

After analysing hundreds of brands across multiple AI models, some patterns emerge:

• Established brands tend to appear consistently across all models, but with varying sentiment. Being known isn't the same as being recommended.
• Challenger brands often show high variance — appearing strongly in 2-3 models and barely registering in others. This suggests concentrated rather than broad visibility.
• B2B brands typically score higher in models that access business-oriented sources (Perplexity, Copilot) and lower in consumer-oriented models.
• Recently funded startups get a temporary boost from press coverage but can fade quickly as newer content displaces them in model training data.

What to do about it

The first step is measurement. Run your brand across all major AI models and see where you stand. Look for gaps — models where you're underrepresented — and investigate why. The fix might be as simple as strengthening your Wikipedia presence, getting cited by authoritative industry sources, or improving the consistency of how your brand is described across the web.

The key insight: optimising for one AI model isn't enough. The users asking about your category are spread across all of them.