Cross-Model GEO: One Content, All AI Engines
Key Takeaways
- • 80% overlap in model preferences — Universal best practices work across all AI models
- • Schema markup is universal — All models weight structured data highly
- • Model-specific 20% — Claude favors nuance, GPT favors structure, Gemini favors recency, DeepSeek favors data
- • External citations matter everywhere — But Perplexity weights them most heavily
- • Test across models — What works for one may not work for all
The good news: 80% of what works for one AI model works for all of them. Schema markup, clear heading hierarchy, authoritative external citations, and quality content are universally rewarded. The challenge is the 20% that differs—Claude's preference for nuanced analysis, GPT's affinity for structured formats, Gemini's emphasis on recency, DeepSeek's data orientation.
According to our analysis of 2+ million GEO workflows at Seenos, content optimized for the universal 80% achieves strong performance across all models. Adding model-specific optimizations for the remaining 20% provides incremental gains of 15-25% per model.
This guide provides the complete cross-model GEO strategy: the universal foundation that works everywhere, plus model-specific adaptations for Claude, GPT, Gemini, DeepSeek, and Perplexity.
The Cross-Model Optimization Matrix #
| Optimization | Claude | GPT | Gemini | DeepSeek | Perplexity |
|---|---|---|---|---|---|
| Schema Markup | High | High | Very High | Very High | High |
| External Citations | High | Medium | High | Very High | Critical |
| Heading Hierarchy | High | High | High | High | Medium |
| Content Depth | Very High | High | High | Very High | High |
| Recency/Freshness | Medium | Medium | Very High | High | Very High |
| Author Attribution | High | Medium | High | Medium | High |
Table 1: Cross-model optimization importance matrix
Universal Best Practices (The 80%) #
These optimizations work across all major AI models:
Schema.org Structured Data #
Every model processes Schema markup. Implement at minimum:
- Article schema — For blog posts and news content
- FAQPage schema — For FAQ sections (enables rich results + AI extraction)
- HowTo schema — For tutorial and guide content
- Organization schema — For about/company pages
- Person schema — For author bios
Clear Heading Hierarchy #
All models use headings for semantic parsing:
- One H1 per page (title)
- H2 for major sections
- H3 for subsections
- No skipping levels (H1 → H3 without H2)
- Descriptive, intent-rich headings
External Citations #
Authoritative external links signal reliability:
- Minimum 3 external citations per major article
- Prioritize .gov, .edu, and industry-leading sources
- Link to primary sources, not aggregators
- Cite recent sources (within 1-2 years)
Author Attribution #
Clear authorship improves trust signals:
- Visible author name on every content piece
- Author bio with credentials
- Person schema markup
- Links to author's other work
Model-Specific Optimizations (The 20%) #
Claude-Specific #
Claude rewards nuanced, balanced analysis:
- Acknowledge limitations — Include “Limitations” or “What this doesn't cover” sections
- Multiple perspectives — Present and address counterarguments
- Reasoning chains — Explicit problem → analysis → conclusion flow
- Careful language — Avoid absolute claims; use appropriate hedging
GPT-Specific #
GPT favors structured, actionable content:
- Clear conclusions — Explicit takeaways and recommendations
- Numbered lists — Step-by-step formats perform well
- Tables and comparisons — Structured data in visual formats
- Actionable advice — “Do this” rather than “Consider this”
Gemini-Specific #
Gemini heavily weights recency and Google signals:
- Visible timestamps — “Last Updated” dates are critical
- Recent sources — Gemini penalizes outdated citations
- Google authority — Content indexed in Google Search gets preference
- Factual grounding — Claims must be verifiable
DeepSeek-Specific #
DeepSeek rewards data-rich, technical content:
- Specific metrics — Include precise numbers and statistics
- Technical depth — Don't oversimplify for general audiences
- Bilingual consideration — Chinese + English versions significantly boost visibility
- External data sources — DeepSeek heavily weights external citations
Perplexity-Specific #
Perplexity is citation-obsessed:
- Extensive external links — More citations = higher preference
- Primary sources — Perplexity traces to original sources
- Fact density — More verifiable claims = higher trust
- Recency — Perplexity's real-time search favors fresh content
See ChatGPT vs Perplexity vs Gemini: Platform Adaptation for detailed platform strategies.
Cross-Model Testing Framework #
Validate optimizations across all models:
- 1Baseline measurement — Test content citation rates across all 5 major models
- 2A/B variations — Create optimized variants for testing
- 3Model-specific queries — Test with each model using identical prompts
- 4Track divergences — Identify where model preferences differ
- 5Optimize for consensus — Prioritize changes that improve all models
See Testing Across Models: A/B Framework for implementation details.
Explore the Cross-Model GEO Series #
Claude vs DeepSeek
Platform Adaptation
Model-Agnostic
Future-Proofing
Related: See Why GEO Systems Matter for strategic context. Compare model capabilities in Claude Evolution and DeepSeek Evolution.
Frequently Asked Questions #
Should I optimize for each model separately?
No. Start with universal best practices (80% overlap), then add model-specific optimizations. Separate optimization is inefficient and unnecessary for most content.
Which model should I prioritize?
Prioritize based on your audience. For US/global audiences: GPT (~35%) and Gemini (~25%). For Chinese audiences: DeepSeek (~40%). For technical/research audiences: Perplexity and Claude.
Do model-specific optimizations conflict?
Rarely. Claude's nuance preference and GPT's structure preference can coexist—you can have nuanced analysis presented in a structured format. Conflicts usually arise only with extreme optimization.
How often do model preferences change?
Minor changes with each model update (monthly). Major shifts with major versions (quarterly). Universal best practices remain stable; model-specific preferences may shift with releases like Claude 5 or DeepSeek V4.
Is cross-model optimization worth the effort?
Yes. The 80% universal foundation provides most of the value. Model-specific optimizations provide 15-25% incremental gains per model. Total effort is modest for significant coverage improvement.
How do I test across multiple models?
Use identical queries across each model's interface or API. Track which sources are cited. Compare citation rates for your content across models. Seenos automates this testing.
What's the minimum cross-model implementation?
Implement the universal foundation: Schema markup, heading hierarchy, external citations, author attribution. This alone provides strong cross-model performance. Add model-specific optimizations when you have bandwidth.
Will cross-model GEO remain relevant as models converge?
Models are converging on best practices, making universal optimization more valuable over time. Model-specific differences may shrink, but the foundation remains essential regardless.
Product Manager at Seenos.ai. Pioneer in AEO research since 2024, exploring the convergence of SEO and GEO (Generative Engine Optimization). Led multiple AI-powered content optimization projects that achieved 300%+ citation increases in ChatGPT and Perplexity.