Different AI models cite sources at dramatically different rates. Grok cites external sources in 32% of responses, while Claude cites in only 0.3% of responses, a 100x difference. This variation means the same content strategy will produce completely different results depending on which AI platform you target.
In this article, we analyze citation behavior across 10 major LLM platforms, explain why these differences exist, and show how to optimize your strategy for each platform type.
How do citation rates compare across LLM platforms?
Our analysis of 189,439 AI responses shows striking differences:
| LLM Platform | Total Responses | Citation Rate | Total Citations |
|---|---|---|---|
| Grok | 15,306 | 32.18% | 688,525 |
| Perplexity | 15,571 | 15.03% | 154,963 |
| Google AI Mode | 29,344 | 13.16% | 280,057 |
| Mistral | 13,743 | 10.86% | 518,195 |
| DeepSeek | 13,765 | 9.71% | 205,879 |
| Gemini | 15,531 | 4.84% | 88,868 |
| Copilot | 15,475 | 3.84% | 156,497 |
| Google AI Overview | 28,398 | 1.63% | 57 |
| ChatGPT | 28,547 | 1.30% | 183,139 |
| Claude | 13,759 | 0.31% | 913 |
Source: Superlines analysis of 189,439 AI responses, December 2025 to January 2026.
Why do citation rates vary 100x across platforms?
The spread between highest and lowest citation rates reflects fundamental differences in how these systems are designed:
- Grok: 32.18% citation rate (cites in 1 out of 3 responses)
- Claude: 0.31% citation rate (cites in 1 out of 323 responses)
According to xAI documentation, Grok uses real-time web search for every query. In contrast, Anthropic’s model card indicates Claude prioritizes conversational responses from training data over real-time retrieval.
Why do Google’s AI products behave so differently?
- Google AI Mode: 13.16% citation rate (280,057 citations)
- Google AI Overview: 1.63% citation rate (57 citations)
Google AI Overview prioritizes direct answers over source attribution, while AI Mode (the full conversational interface) cites more frequently. According to Google’s AI blog, AI Overviews are designed for quick answers, while AI Mode supports deeper exploration with sources.
This has significant implications for tracking: the same “Google AI” traffic behaves completely differently depending on the interface.
Which platform types cite most frequently?
Platforms cluster into three distinct groups based on citation behavior:
| Platform Type | Avg. Citation Rate | Examples |
|---|---|---|
| Research-focused | 10 to 15% | Perplexity, Mistral |
| Conversational | 0.3 to 1.3% | ChatGPT, Claude |
| Hybrid | 13 to 32% | Grok, Google AI Mode |
What source types do different LLMs prefer?
Different LLMs show distinct preferences for source categories:
| Platform | Primary Source Types | Secondary Sources |
|---|---|---|
| Grok | Community content, news publications | Industry blogs |
| Perplexity | Community platforms, professional networks | Video content |
| Mistral | Official documentation, academic papers | Analyst reports |
| DeepSeek | Technical documentation, research papers | Developer resources |
| Google AI Mode | Professional networks, community platforms | News media |
| Google AI Overview | Video platforms, community content | Product pages |
Community content performance
Reddit and LinkedIn dominate across multiple platforms. Community-generated content appears in:
- Grok: 27,000+ citations from reddit.com
- Perplexity: 17,900+ citations from reddit.com
- Google AI Mode: 16,800+ combined citations from reddit.com and linkedin.com
Research from Pew Research Center shows that user-generated content now represents the majority of sources cited in AI responses for product and service queries.
Technical documentation performance
Research-focused models (Mistral, DeepSeek) heavily cite:
- Official documentation (developers.google.com)
- Academic papers (arxiv.org)
- Industry analyst reports
For these platforms, comprehensive technical content outperforms marketing-focused material.
Superlines.io citation performance
In our dataset, superlines.io receives 4,880 citations from Grok across 243 unique prompts. This places Superlines in the top tier of cited domains for AI search optimization queries on Grok.
How does brand visibility differ from citation rate?
Citation rate tells only part of the story. Brand visibility (how often a brand is mentioned, regardless of citation) reveals different patterns:
| LLM Platform | Citation Rate | Brand Visibility |
|---|---|---|
| Grok | 32.18% | 17.04% |
| Mistral | 10.86% | 14.34% |
| DeepSeek | 9.71% | 12.44% |
| Google AI Mode | 13.16% | 11.43% |
| Gemini | 4.84% | 9.32% |
| Claude | 0.31% | 9.18% |
| Copilot | 3.84% | 8.01% |
| ChatGPT | 1.30% | 7.95% |
| Google AI Overview | 1.63% | 7.58% |
| Perplexity | 15.03% | 6.41% |
Claude mentions brands at 9.18% rate despite citing at only 0.31%. This suggests Claude recommends brands in conversational answers without providing source links.
Similarly, ChatGPT’s brand visibility (7.95%) far exceeds its citation rate (1.30%). These platforms matter for brand awareness, just not for driving referral traffic.
How should you optimize for each platform type?
High-citation platforms (10%+ citation rate)
For Grok, Perplexity, Google AI Mode, and Mistral:
- Invest in citable content with structured data, statistics, and quotable statements
- Build presence on Reddit and LinkedIn where these platforms source citations
- Update content regularly (79% of cited content was updated within 12 months)
Medium-citation platforms (3 to 10% citation rate)
For DeepSeek, Gemini, and Copilot:
- Balance citation optimization with brand mention optimization
- Focus on technical documentation for DeepSeek
- Create comprehensive answers for Gemini’s general queries
Low-citation platforms (under 3% citation rate)
For Google AI Overview, ChatGPT, and Claude:
- Focus on brand mentions and training data presence rather than citation optimization
- Build authority signals that affect how these models discuss your brand
- Track brand visibility metrics instead of citation counts
How do citation structures vary by model?
Our data shows different models extract citations differently:
| Pattern | Platforms | Tracking Impact |
|---|---|---|
| Full URLs with trailing slashes | Grok | Requires URL normalization |
| Domain-level normalization | Perplexity | Aggregates to domain |
| Subdomain preservation | Mistral | Separates blog.domain.com from domain.com |
| Mixed formatting | Google AI Mode | Requires flexible matching |
This affects tracking methodology. Ensure your analytics account for URL normalization differences across platforms.
What metrics should you track across platforms?
| Metric | High-Citation Platforms | Low-Citation Platforms |
|---|---|---|
| Primary KPI | Citation rate | Brand mention rate |
| Secondary KPI | Citation volume | Sentiment score |
| Content focus | Citable facts | Brand consistency |
Key takeaways
- Citation rates vary 100x between platforms (Grok 32.18% versus Claude 0.31%)
- Grok generates the most citations with 688,525 in our dataset
- Community content dominates citation sources for Grok and Perplexity
- Technical documentation performs best on Mistral and DeepSeek
- Low-citation platforms still matter for brand mentions (Claude mentions brands 30x more often than it cites them)
Understanding these differences lets you allocate GEO resources where they will have the most impact.
Methodology
This analysis covers 189,439 AI responses tracked between December 9, 2025 and January 8, 2026 across 50 tracked brands in our Superlines dataset. Citation rate equals responses with at least one citation to tracked domain divided by total responses. Brand visibility equals responses mentioning brand divided by total responses.
Frequently asked questions
Which AI assistant cites sources most often?
Grok cites sources most often at 32.18% of responses. Perplexity follows at 15.03%, then Google AI Mode at 13.16%. ChatGPT and Claude cite sources in less than 2% of responses.
Why does ChatGPT rarely cite sources?
ChatGPT primarily relies on training data rather than real-time web search, resulting in a 1.30% citation rate. Its design prioritizes conversational responses over source attribution.
Should I optimize differently for different AI platforms?
Yes. High-citation platforms (Grok, Perplexity) reward citable content with statistics and structured data. Low-citation platforms (ChatGPT, Claude) require focus on brand mentions and training data presence instead.
How can I track my citations across multiple AI platforms?
Use AI visibility tracking tools like Superlines that monitor citations and brand mentions across all major LLM platforms simultaneously, accounting for URL normalization differences.
Does brand visibility matter on platforms that do not cite?
Yes. Claude mentions brands at 9.18% rate despite citing at only 0.31%. These mentions influence user perception and purchasing decisions even without driving direct traffic through citations.