Not all AI models treat your drug the same. Across 23 pharmaceutical brands, the difference between the best-performing and worst-performing model reached 48 points on a single product. If your AI strategy treats every platform equally, you are flying blind.

The era of optimizing for one search engine is over. Today, patients and healthcare professionals ask ChatGPT, Gemini, and Perplexity for drug information, and each model answers differently. Different scores. Different reliability. Different sentiment. Sometimes, a different answer entirely.

We analyzed 23 pharmaceutical brands across all three major AI platforms to understand how each model handles drug information. The findings reveal that your AI visibility strategy cannot be platform-agnostic. Each model has a distinct personality when it comes to pharmaceutical content, and understanding those personalities is the first step toward controlling your brand narrative in the age of generative search.

Data source: PharmaGEO platform analysis of 23 pharmaceutical brands across OpenAI, Gemini, and Perplexity (2025).

The Three-Model Landscape: A Side-by-Side Snapshot

Before diving into the nuances, here is the high-level picture. These ranges represent typical performance across the 23 brands we analyzed.

AI Model Typical Score Range Typical Reliability Dominant Sentiment
OpenAI (ChatGPT) 43 -- 66 73 -- 89% Overwhelmingly neutral
Gemini 40 -- 71 71 -- 89% Neutral to slightly positive
Perplexity 23 -- 63 52 -- 81% Neutral, occasionally positive

Three models. Three very different profiles. The score ranges overlap, but the patterns beneath those numbers tell a far more actionable story.

What stands out immediately: Gemini reaches the highest highs, OpenAI delivers the most consistent reliability, and Perplexity trails on nearly every metric. But the devil is in the details, and pharma teams making platform investment decisions need those details.

Key Takeaway: No single AI model dominates across all dimensions. Your optimization strategy must be model-aware, not model-agnostic.

OpenAI (ChatGPT): The Reliable Workhorse

OpenAI is the model most pharma teams think of first, and for good reason. ChatGPT commands the largest user base and remains the default AI assistant for millions of patients and HCPs. But our data reveals that OpenAI's strength is not raw performance. It is consistency.

The Numbers

- Score range: 43 -- 66 across the 23-brand dataset

- Reliability: Frequently lands between 76% and 87%, with an upper bound of 89%

- Top brand score: Beyfortus at 66

- Sentiment: Overwhelmingly neutral

What the Data Tells Us

OpenAI is the most reliable model but not the highest-scoring one. It rarely delivers a breakout performance, but it also rarely drops the ball entirely. For pharmaceutical brands, this translates to a platform where your drug information is likely to appear and likely to be factually grounded, but unlikely to frame your product in a competitive advantage.

Source accuracy is a standout feature. Compared to Gemini and Perplexity, OpenAI tends to pull from more authoritative sources. When it cites clinical data, the references are more frequently traceable to legitimate medical literature or regulatory documents. This matters enormously for pharma teams concerned about [misinformation and pharmacovigilance risks](/blog/pharmacovigilance-blind-spot-ai).

The overwhelmingly neutral sentiment is a double-edged sword. On the one hand, neutral responses reduce the risk of misleading promotional claims appearing in AI-generated answers. On the other hand, neutral sentiment means OpenAI is unlikely to surface your competitive differentiators unprompted. If a patient asks ChatGPT to compare two treatments, the response will typically present both options without favoring either.

Key Takeaway: OpenAI is the platform where your baseline accuracy matters most. Get your foundational content right, and ChatGPT will reliably surface it. But do not expect it to sell your product for you.

Who Should Prioritize OpenAI

- Brands where accuracy and safety messaging are the top priority

- Products in highly regulated or scrutinized categories

- Teams focused on ensuring [reliable AI-generated answers](/blog/reliability-tax-pharma-ai) rather than optimizing for visibility scores

Gemini: The High-Ceiling Performer

Google's Gemini model tells a different story. Where OpenAI is the steady hand, Gemini is the platform capable of delivering the highest individual scores in the entire dataset, but with slightly more variability.

The Numbers

- Score range: 40 -- 71 across the 23-brand dataset

- Reliability: 71 -- 89%, comparable to OpenAI at the top end

- Top brand score: Braftovi at 71, the highest single-model score across all 23 brands

- Notable score: Beyfortus at 70

- Sentiment: Neutral to slightly positive

What the Data Tells Us

Gemini's standout characteristic is its ceiling. A score of 71 for Braftovi represents the single best performance by any model for any brand in our dataset. Beyfortus also reached 70 on Gemini, compared to 66 on OpenAI and 63 on Perplexity.

What drives these higher scores? Our analysis points to competitive comparison content. Gemini is more likely than OpenAI to generate responses that position drugs within a competitive landscape, referencing alternatives and articulating relative advantages. For brands with strong clinical differentiation, this behavior is a significant opportunity.

The slightly positive sentiment skew reinforces this finding. Gemini does not editorialize aggressively, but it is more willing than OpenAI to frame a drug's benefits in context. When a product has clear advantages in efficacy, safety profile, or convenience, Gemini is more likely to surface those distinctions.

However, the broader score range (40 -- 71 versus OpenAI's 43 -- 66) indicates more variability. Gemini's floor is slightly lower than OpenAI's, meaning some brands receive weaker representation on this platform.

Key Takeaway: Gemini rewards brands that have strong, well-structured competitive positioning content available online. If your drug's advantages are clearly documented, Gemini is the model most likely to amplify them.

Who Should Prioritize Gemini

- Brands with clear competitive differentiators they want AI to surface

- Products in crowded therapeutic categories where comparison queries are common

- Teams that have invested in structured content articulating [clinical benefits and positioning](/blog/benchmarking-23-pharma-brands-ai-visibility)

Perplexity: The Parsing Problem

Perplexity has built a loyal following among researchers and information-seekers who value its citation-heavy, source-transparent approach. But for pharmaceutical brands, our data reveals a consistent underperformance that demands attention.

The Numbers

- Score range: 23 -- 63 across the 23-brand dataset

- Reliability: 52 -- 81%, significantly lower than both OpenAI and Gemini

- Top brand score: Beyfortus at 63

- Sentiment: Neutral, occasionally positive

What the Data Tells Us

Perplexity underperforms on both score and reliability. Its score ceiling of 63 sits below the midpoint of what Gemini can achieve, and its reliability floor of 52% means that for some brands, nearly half of Perplexity's responses may contain inaccurate or incomplete information.

But the most alarming pattern is what we call the parsing problem.

Across multiple brands, including Entyvio, Ledaga, Imfinzi, and Parodontax, Perplexity returned "Unable to parse response" for Discoverability metrics. This is not a low score. It is a complete failure to process the query in a meaningful way. When a model cannot even parse a query about your drug, optimization becomes irrelevant.

The parsing failures compound an already challenging pattern: Perplexity frequently returns "Not Found" for pharmaceutical products, meaning it does not mention the product at all in its response. While OpenAI and Gemini may score low on certain brands, they at least acknowledge the product's existence. Perplexity, in contrast, can omit a drug entirely.

The Recognition Gap

Our recognition analysis underscores the severity:

- Brand name recognition: 0% across all three models (a [systemic issue in pharma AI](/blog/brand-recognition-crisis-pharma-ai))

- INN (generic name) recognition: 67% overall, but Perplexity is frequently the model that fails to recognize even the INN, while OpenAI and Gemini typically succeed

When two out of three models recognize your drug's generic name and the third does not, that third model becomes a black hole for your brand's AI visibility.

Key Takeaway: Perplexity's parsing failures and recognition gaps make it the highest-risk platform for pharmaceutical brands. Do not assume that optimizing for OpenAI and Gemini will automatically translate to Perplexity visibility.

Who Should Still Monitor Perplexity

- Brands targeting research-oriented audiences who favor Perplexity's citation model

- Teams conducting competitive intelligence (Perplexity's weaknesses may also affect competitors)

- Any brand experiencing the parsing problem, as it may signal deeper content structure issues

Cross-Model Consistency: The Hidden Risk

Beyond individual model performance, the gap between models on the same brand reveals a risk most pharma teams have not considered.

The Best Case: Beyfortus

Beyfortus offers a near-ideal cross-model profile:

Model Score
OpenAI 66
Gemini 70
Perplexity 63

That is a 7-point band across all three models. Tight. Predictable. Manageable. If every brand performed this consistently across platforms, AI optimization would be far simpler.

But most brands do not look like Beyfortus.

The Typical Case: Wide Gaps

Many brands in our dataset show score gaps of 20, 30, or even 48 points between their best-performing and worst-performing model. This inconsistency creates a fragmented patient experience. A patient asking ChatGPT about a drug might receive a comprehensive, accurate answer, while the same patient asking Perplexity might receive no mention of the drug at all.

Benefits and Risks Consistency

The cross-model consistency problem extends beyond overall scores to specific content dimensions:

Metric Entyvio Ledaga
Benefits consistency across models 13% 0%
Risks consistency across models 3% 0%

Ledaga shows 0% consistency on both benefits and risks across models. This means the three AI platforms are telling fundamentally different stories about the same drug's benefits and safety profile.

For pharmacovigilance and medical affairs teams, this is a critical concern. Inconsistent risk information across AI platforms can lead to [patient safety gaps](/blog/pharmacovigilance-blind-spot-ai) and regulatory exposure.

There is one exception to the inconsistency pattern: when a brand is represented by a single model (for example, Dupixent in certain query contexts), consistency is 100% by definition. But this "consistency" comes at the cost of zero visibility on two out of three platforms.

Key Takeaway: Cross-model consistency is the hidden metric most pharma teams are not tracking. A 7-point band (like Beyfortus) should be the benchmark. Anything wider demands platform-specific investigation and intervention.

What This Means for Your AI Strategy

The data from 23 brands leads to one unavoidable conclusion: a single-platform AI strategy is a liability.

Three Principles for Multi-Model Pharma AI Strategy

1. Do not rely on a single model.

Patients and HCPs do not all use the same AI platform. Your drug needs to be accurately represented across OpenAI, Gemini, and Perplexity. Monitoring only ChatGPT because it has the largest market share ignores the significant (and growing) audiences on Gemini and Perplexity.

2. Prioritize by audience behavior.

- If your primary audience is general patients, OpenAI's reliability makes it the priority.

- If your audience includes specialists making comparative decisions, Gemini's competitive framing is an advantage.

- If your audience includes researchers and evidence-driven professionals, Perplexity's citation model matters, but its parsing problems require active monitoring.

3. Track cross-model consistency as a KPI.

Add model-to-model score variance to your AI monitoring dashboard. A [benchmarking framework](/blog/benchmarking-23-pharma-brands-ai-visibility) that only reports average scores across models will hide the platform-specific gaps that create patient safety and brand reputation risks.

Model-Specific Optimization Tactics

Understanding the profiles is step one. Here is how to act on them.

Optimizing for OpenAI (ChatGPT)

- Focus on source authority. OpenAI leans on authoritative sources. Ensure your drug's prescribing information, clinical trial data, and medical society guidelines are current, well-structured, and easily accessible to web crawlers.

- Structure for factual retrieval. OpenAI favors clear, factual content. Use structured data markup, concise headings, and direct answers to common clinical questions.

- Accept the neutral sentiment. Do not try to push promotional framing into content designed for OpenAI ingestion. It will either be ignored or could trigger reliability issues. Instead, ensure the neutral framing is complete and accurate.

- Maintain your [five-source foundation](/blog/five-source-rule-pharma-ai-visibility). OpenAI's source accuracy advantage depends on having high-quality sources available.

Optimizing for Gemini

- Invest in competitive comparison content. Gemini's higher scores are driven by its willingness to present drugs in competitive context. Publish clear, evidence-based content that articulates your drug's advantages relative to alternatives.

- Leverage structured clinical data. Gemini rewards well-organized efficacy and safety data. Treatment comparison pages, mechanism-of-action explainers, and indication-specific content tend to perform well.

- Monitor for sentiment drift. Gemini's slightly positive sentiment is an advantage today, but it can shift. Track whether Gemini's framing of your drug remains accurate and balanced over time.

Optimizing for Perplexity

- Address the parsing problem first. If your brand is returning "Unable to parse response" on Perplexity, this is a structural issue that no amount of content optimization will fix. Investigate whether your online content creates ambiguity that Perplexity's parsing engine cannot resolve.

- Ensure basic recognition. Before optimizing for scores, confirm that Perplexity mentions your drug at all. If it returns "Not Found," the priority is establishing a baseline presence through clear, unambiguous content using both brand and INN names.

- Optimize for citation inclusion. Perplexity's model is citation-driven. Ensure your key content assets (clinical studies, FDA labels, treatment guidelines) are structured in a way that Perplexity can identify and cite them.

- Do not deprioritize Perplexity entirely. Its audience skews toward research-oriented users who may be making or influencing prescribing decisions. A "Not Found" on Perplexity for this audience is a missed opportunity.

Frequently Asked Questions

Which AI model is best for pharmaceutical drug information?

No single model is universally best. OpenAI (ChatGPT) offers the highest reliability (73--89%) and most accurate sourcing, making it the strongest platform for baseline drug information. Gemini delivers the highest individual scores (up to 71) and better surfaces competitive positioning. Perplexity underperforms on both metrics. The right priority depends on your audience and therapeutic category.

Why does Perplexity perform worse than ChatGPT and Gemini for pharma brands?

Perplexity exhibits two distinct problems. First, it has a parsing failure where it returns "Unable to parse response" for multiple pharmaceutical brands, suggesting structural issues with how it processes drug-related queries. Second, it frequently returns "Not Found," meaning it does not mention the product at all. Its lower reliability range (52--81%) reflects these compounding issues.

How consistent are AI models when answering questions about the same drug?

Consistency varies dramatically. Beyfortus shows a tight 7-point range across all three models (OpenAI 66, Gemini 70, Perplexity 63), representing near-ideal consistency. However, other brands show gaps exceeding 40 points. Benefits and risks consistency can drop as low as 0% across models, meaning platforms deliver fundamentally different information about the same drug's safety and efficacy.

Should pharma companies optimize for all three AI models or focus on one?

Optimize for all three, but with differentiated strategies. A single-platform approach ignores that patients and HCPs use different AI tools. Prioritize OpenAI for reliability and accuracy, Gemini for competitive positioning, and monitor Perplexity for recognition gaps. Track cross-model score variance as a key performance indicator alongside individual platform metrics.

Do AI models show bias toward or against pharmaceutical brands?

Our data shows that all three models exhibit 0% brand name recognition, responding only to generic (INN) names. OpenAI maintains overwhelmingly neutral sentiment, Gemini skews slightly positive (particularly for drugs with clear competitive advantages), and Perplexity is neutral when it responds at all. The primary concern is not bias but inconsistency and omission, especially on Perplexity.

How often should pharma teams audit their AI model visibility?

Given the pace of model updates and training data changes, monthly monitoring across all three platforms is the minimum recommended frequency. Major model updates (such as new GPT or Gemini releases) should trigger immediate re-evaluation. Use a [standardized benchmarking framework](/blog/benchmarking-23-pharma-brands-ai-visibility) to ensure consistent measurement over time.

Conclusion: The Multi-Model Imperative

The AI landscape for pharmaceutical information is not a single arena. It is three distinct stages, each with different rules, different audiences, and different outcomes for your brand.

OpenAI is the reliable baseline. It will represent your drug accurately and consistently, but it will not champion your competitive advantages. Optimize for factual completeness and source authority.

Gemini is the opportunity platform. It delivers the highest scores and surfaces competitive positioning. For brands with strong clinical differentiation, Gemini amplifies what you have built.

Perplexity is the risk platform. Its parsing failures, recognition gaps, and lower reliability make it the most likely source of incomplete or absent drug information. Do not ignore it. Monitor it aggressively.

The brands that win in AI-driven pharmaceutical search will not be the ones that optimize for a single model. They will be the ones that understand each model's personality, track cross-platform consistency, and deploy differentiated strategies for each.

A 48-point gap between your best and worst model is not a statistic. It is a patient receiving dangerously different information depending on which AI they happened to open. Close the gap.

Data source: PharmaGEO platform analysis of 23 pharmaceutical brands across OpenAI, Gemini, and Perplexity (2025).

This article is part of the PharmaGEO Insights Series on AI visibility for pharmaceutical brands. Related reading: The Brand Recognition Crisis in AI | The Reliability Tax | Benchmarking 23 Brands on GEO Pharma | The Pharmacovigilance Blind Spot

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Data source: PharmaGEO platform analysis of 23 pharmaceutical brands across OpenAI, Gemini, and Perplexity (2025)