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llms.txt GeneratorAverage Brand Overlap
The percentage of queries where ChatGPT, Claude, and Gemini recommend the same primary brand.
Trakkr analysis · directional estimate
Executive summary
This research explores the fragmentation of brand visibility across leading AI platforms. By analyzing thousands of categorical queries across ChatGPT, Claude, and Gemini, we identified significant variance in how AI models prioritize and recommend brands. While legacy search engines often showed high consensus on top results, AI platforms utilize distinct training data weights and retrieval mechanisms, leading to a ‘fragmented recommendation landscape.’ Brands that dominate visibility on one platform frequently vanish on others, suggesting that a single-platform optimization strategy is insufficient for the AI era. The findings indicate that platform-specific algorithms respond differently to factors like technical documentation, social proof, and recent news mentions.
Key findings
Cross-platform brand consistency is highest in the consumer electronics sector and lowest in professional services.
Gemini shows a 42% higher correlation with traditional search engine results compared to Claude or ChatGPT.
Only 12% of brands maintain a top-three position across all three major platforms for high-intent keywords.
Platform-specific bias is evident, with Claude favoring brands with extensive long-form documentation.
Brand age and domain authority are less predictive of AI recommendation than recent ‘contextual density’ in training sets.
- Method in brief
- Trakkr Research analyzed 5,000 product and service queries across three major LLMs (ChatGPT-4o, Claude, and Gemini). We utilized a standardized prompting framework to minimize ‘hallucination’ and measured brand presence in the first three recommended positions.
Methodology
- Approach
- Automated prompt testing using API-based retrieval across multiple geographic nodes to ensure localized data variance was captured.
- Data sources
- Direct API outputs from OpenAI, Anthropic, and Google
- Publicly available training set metadata
- Third-party SEO visibility benchmarks
- Sample
- 5,000 unique categorical queries across 12 industries
- Timeframe
- Q4 2025 to January 2026
- Where the evidence stops
- Rapid model updates may alter results within weeks
- API temperatures were set to 0.7 which may introduce slight creative variance
- Data excludes private enterprise versions of these models
Key statistics
Sources as reported. None has an attached measurement record, so read the figures as directional.
| Statistic | Context | Trend | Source |
|---|---|---|---|
| 28% OverlapBrands must diversify their content strategy to appeal to different model architectures. | The rate at which ChatGPT, Claude, and Gemini recommend the same brand for a given category. | ↓ Falling | Trakkr analysis |
| 64% ConsistencyCommodity brands have a more predictable path to AI visibility. | The high consensus rate for ‘commodity’ products like office supplies compared to specialized software. | → Stable | Platform data |
| 3.5x Higher VisibilityCommunity engagement is now a primary driver of AI brand authority. | Brands with active Reddit and community discussions are 3.5x more likely to be recommended by Claude. | ↑ Rising | Expert consensus |
| 18% Citation MatchMultiple sources of truth are being used; no single ‘backlink’ strategy works for all AI. | The frequency with which platforms cite the same source material for their recommendations. | ↕ Varies | Industry estimate |
| 45% VolatilityAI visibility is less stable than traditional SEO rankings. | The rate at which recommendations change when the prompt is slightly rephrased. | ↑ Rising | Trakkr analysis |
| 52% Gemini-Google CorrelationTraditional SEO still heavily influences Gemini’s recommendation engine. | Gemini aligns with Google Search Top 3 results more often than any other AI platform. | → Stable | Platform data |
| 9% Brand LoyaltyVisibility can be lost instantly during model retraining cycles. | The percentage of brands that remain in recommendations across consecutive model versions (e.g., ChatGPT to ChatGPT). | ↓ Falling | Analyst projections |
| 71% Response UniquenessThere is significant ‘long-tail’ opportunity for smaller brands on specific platforms. | Percentage of AI responses that include at least one brand not mentioned by competitors. | ↑ Rising | Trakkr analysis |
Data visualizations
Cross-Platform Brand Agreement by Category
Brand Recommendation Volatility Over Time
Platform Source Reliance
Recommendation Overlap: ChatGPT vs Gemini
Analysis
The retrieval gap
Architectural differences drive recommendation variance.
The primary reason for low consistency across platforms lies in the ‘Retrieval Augmented Generation’ (RAG) processes used by each provider. While ChatGPT relies heavily on a mix of its pre-trained weights and specific Bing-powered web searches, Gemini is deeply integrated with Google’s Knowledge Graph. Claude, conversely, appears to prioritize high-token-count context windows that favor detailed, long-form technical documentation. This means a brand with a great knowledge base might win on Claude, while a brand with high social media engagement wins on ChatGPT.
Data points
- Claude cites technical docs 2x more than ChatGPT
- Gemini prioritizes local map-pack data in 60% of service queries
What it means
- Brands must audit their visibility per-platform
- A ‘one size fits all’ content strategy is failing
Frequently asked questions
This is typically due to different data sourcing. Gemini is tightly coupled with Google Search results and the Knowledge Graph, whereas ChatGPT may be relying on training data that includes more social media discussions or specific web crawls. If your brand is strong in traditional SEO but weak in community mentions, you will likely see this discrepancy.