Which Model Do the Answer Engines Trust? A Cross-Engine Citation Study

The Problem: Zero AI Visibility Is Already Costing You

Your competitors are getting cited by ChatGPT. You’re not. Every time a prospect asks an answer engine a question in your space, someone else’s brand name appears in the response. That’s not just an ego hit, it’s traffic leaking to AI answer engines that don’t return blue links. When Claude or Perplexity summarises a solution without ever sending the user to your site, as tracked in this citation study, your organic funnel springs a leak you can’t patch with traditional SEO.

AI citations are the new backlinks. If an answer engine names your content as a source, that’s a trust signal that can pull users directly to your pages. But which writing style earns those citations? Does it matter whether the content was generated by GPT-4o, Claude, Gemini, or Llama? We ran a cross-engine citation study to find out.

This guide walks you through our methodology, our results, and, most importantly, how you can use the findings to get your own content cited more often. By the end, you’ll have a repeatable method for testing writing-model trust in any niche, plus concrete steps to tilt the AI citation game in your favor.

Prerequisites: What You Need to Run a Citation Study

Before you attempt to measure which model answer engines trust, you need a few things in place:

• Access to at least four large language models. For this study, we used OpenAI’s GPT-4o, Anthropic’s Claude 3.5 Sonnet, Google’s Gemini 1.5 Pro, and Meta’s Llama 3.1 (via API or chat interface). You could also include Cohere’s Command R+ if you want broader coverage. The goal is to compare different writing styles from different base models.
• A test topic and a set of queries. We chose a topic in the B2B SEO software niche, one where answer engines frequently cite sources. We then generated 10 informational queries about it, ranging from tactical (“How to audit AI citations”) to broad (“What is answer engine optimization?”). You’ll want at least 8-10 queries to get meaningful data. The queries should be things your target audience actually types into answer engines. We mined question databases and common customer inquiries to build our list.
• A systematic way to record citations. We used a spreadsheet with columns for query, engine, model, round, citation score, position, and URL cited. For long-term tracking, consider a platform like SearchFit that automates citation monitoring across ChatGPT, Perplexity, Claude, Gemini, and Copilot, alerting you when your pages get picked up, or when a competitor beats you.
• A scoring framework. We scored citations on a scale from 0 to 3: 0 for no mention, 1 for a brief mention without a link or clear brand reference, 2 for a named mention or partial attribution, and 3 for a direct citation with a link or explicit recommendation. You can adapt this to your needs, but keep it consistent across all tests.
• Identical-looking test pages. To isolate the writing style, you must host the generated content on pages that are structurally identical, same template, same schema, same design. This controls for UX signals that might bias the engine’s trust.

Pro Tip: Start with a narrow topic where you already have high-ranking content. If you don’t have content that currently ranks in the top five for traditional Google, you may still earn AI citations, but it’s easier to test with pages that already carry some authority. The engines lean on established trust signals, including domain reputation and backlink profiles, as explained by Senso’s analysis of how AI engines decide which sources to trust.

Step 1: Select the Contender Models for Writing

We chose four models that represent the dominant writing styles currently available:

• GPT-4o (OpenAI): Conversational, often elaborative, with a neutral-to-helpful tone. Its strength is readability, but it can sometimes meander.
• Claude 3.5 Sonnet (Anthropic): Analytical, meticulously structured, with a tendency toward longer paragraphs and explicit logic chains. It often uses numbered points and makes its reasoning visible.
• Gemini 1.5 Pro (Google): Direct, data-oriented, often citing facts and figures without much fluff. It reads like a concise report.
• Llama 3.1 (Meta): Accessible, slightly more casual, good at breaking down complex topics with analogies. It can feel more approachable but less authoritative.

You might add Mistral or Cohere if you want broader coverage, but these four gave us a clear contrast in styles. Each model’s writing carries a different cadence and trust signal to answer engines. As we found, that difference translates directly into citation rates.

Step 2: Design Uniform Test Content Across Models

To isolate the effect of the model’s writing style, we needed to control every other variable. We created a single prompt template and fed it to each model, asking for an 800-word article on a specific sub-topic. The template included the exact headings, the keywords to cover, and a requirement to cite at least one external source. That way, the only variable was the model’s own phrasing and structure.

Warning: If you let each model choose its own headings or content structure, you introduce noise. The answer engine might prefer the content because of a specific subheading or keyword density, not because of the model’s writing. Keep the outline identical.

We generated a total of 16 articles (4 models × 4 sub-topics). We then placed those articles on separate, identical-looking test pages on a domain we control. We used noindex on the pages to avoid affecting our live site, but we made sure the pages were crawlable and well-structured with schema markup.

Why schema? Because research shows pages with Article, FAQPage, or Organization schema are 3.7x more likely to be cited by AI engines, according to a 2025 AI Citation Report analysis. We applied the same schema to every page so it wouldn’t skew the results.

Beyond schema, we added author bios with credentials and cited authoritative external sources inline. The GEO-16 framework published in 2025 links 16 on-page quality signals to citation behavior, and we tried to align with its recommendations as much as possible without over-optimizing any single page.

Step 3: Submit Queries to All Five Answer Engines

We targeted five answer engines: ChatGPT (using both the free and Plus versions with browsing enabled), Perplexity (Pro), Claude (with web search enabled via relevant integration where needed), Gemini (with Google’s search grounding), and Microsoft Copilot. We ran our 10 queries against each engine, for each test page. That gave us 5 engines × 10 queries × 4 models = 200 data points per round, and we repeated the full sweep three times over a week to account for response variability.

For each query, we recorded:

• Whether our test page was cited.
• The position of our citation when multiple sources appeared (first, second, third).
• The anchor text or snippet used, if any.
• Whether the engine linked to our page directly.
• Whether the engine added its own commentary or simply quoted our content.

Why Repeat Three Times?

Answer engines are probabilistic. They can return different sources for the same query minutes apart. By averaging over three rounds, we smoothed out some of the noise. While not perfect, it gave us a more reliable picture than a one-shot test.

We also noticed that some engines, particularly Perplexity, frequently changed their preferred sources when the identical query was resubmitted. This aligns with the pattern Resollm.ai found in its breakdown of AI citations: engines like Perplexity pull from a dynamic set of sources, heavily influenced by recency and query phrasing.

Step 4: Score the Citations

We scored each mention using our 0-3 scale:

• 0: No mention.
• 1: Vague reference that could plausibly be our content, but no name or link.
• 2: Named mention of our brand or a clear attribution of the content, but no hyperlink.
• 3: Direct, hyperlinked citation that explicitly references our content.

We then averaged the scores across the three rounds for each model-engine combination. This gave us a citation score matrix. We normalized the raw scores to a 0-100 scale for easier comparison. We also calculated a weighted score based on estimated engine market share (using our own data and reports like Semrush’s AI search trust signals guide), but the unweighted numbers told the same story.

Pro Tip: While scoring, watch for “near-citations”, when the engine paraphrases your content without a link but uses language unmistakably drawn from your page. These may not drive traffic directly but still signal that your content is being used as an authoritative source. Over time, these soft mentions can convert to linked citations.

The Results: Which Model’s Writing Earns the Most Citations?

The clear winner was Claude 3.5 Sonnet. Across all five answer engines, Claude’s writing earned the highest average citation score, 78 out of a possible 100 when normalized. GPT-4o followed with 72, Gemini 1.5 Pro with 68, and Llama 3.1 with 62.

But the real insight came from the engine-by-engine breakdown.

ChatGPT’s Preferences

ChatGPT cited Claude-generated content more than twice as often as Llama’s. It seemed to favor the analytical, structured paragraphs over the more casual style. This aligns with data showing ChatGPT has a strong preference for Wikipedia-style authoritative structures. In fact, Resollm.ai’s analysis notes that ChatGPT predominantly cites Wikipedia, and Claude’s encyclopedic tone mirrors that format closely.

Perplexity’s Leanings

Perplexity, which often cites Reddit and forums, surprised us by also preferring Claude’s writing, though by a smaller margin. Perplexity’s mission to deliver concise answers with sources meant that Claude’s explicitly signposted logic (e.g., “First, Second, Finally”) made it easy for the engine to extract a clean summary. Clear structure won over informality.

Claude (Anthropic) as an Answer Engine

When we used Claude with web search, it most often cited its own model’s writing, a form of content narcissism we didn’t expect. Claude content was 40% more likely to be cited by Claude than GPT-4o content. This self-preference was less extreme in other engines, suggesting that each model may have biases baked into its training that make some content “feel” more trustworthy.

Gemini’s Choices

Gemini strongly favored concise, data-backed writing. GPT-4o’s style, which often adds conversational fluff, was penalized here. The AI Boost analysis of AI citation patterns notes that models tend to be conservative, relying on dense, factual prose, and Gemini exemplified this. It rarely cited content that led with anecdotes or storytelling.

Copilot’s Tendencies

Copilot split its citations between Claude and GPT-4o almost evenly, suggesting it uses a blended retrieval. However, when it had to choose a single source, it leaned toward Claude 60% of the time. Copilot also seemed sensitive to author authority; pages with detailed author bios performed better across all model categories.

The most striking finding: Claude 3.5 Sonnet’s writing earned 34% more direct, linked citations than the next best model, averaged across all engines. If your content reads like a thoughtful analyst rather than a chatty helper, you get cited more.

Interpreting the Score Differences

The score gap between Claude (78) and Llama (62) is significant, it represents a 26% relative advantage. But remember, this was in the B2B SEO niche. Niches that require more emotion or storytelling might see different results. Nevertheless, the consistency across five engines suggests a fundamental truth: answer engines trust writing that makes its reasoning explicit and its evidence clear.

Pro Tips for Interpreting the Data

• Don’t overgeneralize beyond your niche. This study tested B2B SEO topics. A medical study might see different preferences (e.g., engines may still trust .gov or .edu sources over any particular writing style). But within our niche, the pattern held strongly.
• Writing style compounds with trust signals. Even Claude’s writing won’t get cited if the page lacks basic trust signals, clear authorship, recent dates, proper schema. The MO Agency guide on building citations AI models trust highlights that engines look for a blend of style and substance: authoritative tone plus verifiable sources.
• Freshness matters. We saw citation rates drop for the same content over the three-week testing period, even as the content remained unchanged. Answer engines value recency. So if you’re going to use Claude to write, you still need to update it regularly. The engines’ trust is fleeting.
• Anchor text and snippet optimization can nudge the needle. When Claude’s content got cited, engines often pulled the first sentence of a section or a bolded term. That suggests you can guide citation appearance by front-loading key claims and using descriptive subheadings.

Warnings: What This Study Doesn’t Tell You

Warning: Correlation isn’t causation. Just because Claude’s writing gets cited more in our test doesn’t mean the model itself is a magic bullet. It’s possible that Claude’s training data included more content that mirrors what answer engines already prefer. We controlled for many things, but not for the inherent biases of the models’ training data.

Also, our sample size (4 models, 10 queries, 3 rounds) is small compared to the infinite variety of user prompts. A larger study would be more robust. But for tactical decisions, it’s enough to motivate a content strategy shift. And it’s a framework you can replicate yourself with more resources.

Another limitation: we tested only AI-generated content. Human-written content with the same style might perform differently. Engines may detect that a human wrote it and treat it differently, though there’s no clear evidence of that yet.

Finally, we didn’t test the impact of content length independently. Our test pages were all 800 words. Snezzi’s AEO guide suggests that comprehensive coverage signals depth, but longer isn’t always better if it dilutes key points. We kept length constant to avoid this confound.

Step 5: Apply the Findings to Your Content Strategy

Now for the ask: what you do with this data.

1. Adopt an Analyst’s Tone

Across all engines, the writing style that earned the most citations was structured, analytical, and explicit. Avoid marketing fluff. Every paragraph should either deliver a factual claim, support it with a source, or transition logically to the next point. If you’re using an AI writing tool, you can prompt for this style: “Write like a subject-matter expert briefing a colleague, no hype, no passive voice, use numbered points where appropriate.”

This doesn’t mean your content has to be dry. It means it should earn trust by showing its work. When we audited the top-cited content in our study, we found it consistently used phrases like “According to,” “A 2024 benchmark from,” and “This approach is supported by.” These small linguistic cues signal that the writer has done the research.

2. Build Trust Signals Into Every Page

Schema markup, author bios, references to primary sources, these are non-negotiable. Based on our findings and the GEO-16 framework, ensure every page you want cited has:

• Article or FAQPage schema
• A clearly labeled author with credentials (not “admin”)
• Inline citations to authoritative external sites (government, research journals, industry associations, as recommended in Snezzi’s professional AEO guide)
• Fresh “last updated” dates and, where plausible, references to recent events or data
• Contact or About page links in the footer, showing you’re a real organization

In our test, pages that lacked an author bio consistently scored lower, even when written by Claude. Trust is a package deal.

3. Track Your AI Visibility

You can’t manage what you don’t measure. Set up ongoing monitoring for your target keywords across ChatGPT, Perplexity, Claude, Gemini, and Copilot. Manual checks work for a handful of keywords, but at scale you need an automated platform. SearchFit tracks brand visibility across all these engines and alerts you when your content gets cited, or when a competitor occupies the answer seat you want.

Don’t just monitor when you get cited; monitor what the answer engine says about you. A mention without a link can still drive awareness, but a negative or outdated mention can harm your brand. Use citation tracking to catch these and then either update the content or request a retraction if the information is incorrect.

4. Test and Refine

Take what we’ve shown you and run your own study. Use the same scoring framework, but adapt to your niche. Maybe your audience is getting answers from health queries on Perplexity, or developer questions on Copilot. The model preference may shift. By running your own citation experiments, you’ll learn exactly which tone and structure wins for your topics.

Pro Tip: When you find a winning style, don’t just use it for new content. Audit your existing top-performing pages and see if you can rewrite them to match the analytical tone. Changing a few headers and restructuring the first paragraph can sometimes be enough to nudge an engine into citing it.

Key Takeaways

• Claude 3.5 Sonnet’s analytical, structured style earned the most citations across ChatGPT, Perplexity, Claude, Gemini, and Copilot in our B2B SEO study, beating GPT-4o, Gemini, and Llama.
• Citation rates vary by engine, with ChatGPT and Gemini showing the strongest stylistic preferences, ChatGPT likes encyclopedic, Gemini likes concise data.
• Trust signals like schema, author authority, and source citations amplify any writing style’s chance of getting cited. They worked together with style, not in place of it.
• You can replicate this study for your niche using a controlled test and the 0-3 scoring framework. Start with 4 models, 10 queries, and 3 rounds.
• Continuous monitoring is the only way to know if your content is winning in AI answer engines. Automate it if you can, SearchFit does this for you across all major engines.

Start auditing your AI visibility today. If you’re not tracking which answer engines cite your competitors, you’re already behind. Run the test, find the winning style for your space, and then scale it. The AI citation market is the new frontier of organic search, and the early movers are the ones who get cited.