2026 GEO Citations Report: How 5 AI Models Cite Sources

Emergine 2026 GEO Citations Report: 5,000 AI search conversations. Five models. Ten industries.

Using our own tool, our team has been studying how mainstream AI models select and cite information across different industries. For brands across industries, the early finding is clear: GEO is not simply about producing more content.

It is about understanding: which sources each model tends to trust, how citation patterns change by industry, where brands need to build stronger evidence, and how GEO strategies should differ across models.

Our CEO Alex Li previewed this research at the MARKETECH APAC What's NEXT in Marketing conference in Singapore in July, where we presented the data in the form of a 2026 GEO Cheatsheet. You can request it here:

Download the 2026 GEO Cheatsheet

How we ran the study

We collected 5,000 AI search conversations in July 2026 across five leading AI models: ChatGPT, Gemini, DeepSeek, Doubao, and Qwen. Web search was enabled for every model, so the citations reflect live retrieval rather than training memory, and the dataset is a snapshot of that period.

The prompts covered ten industries, split evenly.

Five B2B: Manufacturing, ICT, Finance, Logistics, and Enterprise Services.

Five B2C: Automotive, Consumer Electronics, Hotel & Travel, Education & Training, and Sports & Outdoor.

Every citation was tagged into one of twelve source categories: Official Sources, Community/Q&A, B2B Directories, Encyclopedias, Short Video/Social, News/PR, Industry Media, E-Commerce, Reviews/Rankings, Academic Databases, Government/Associations, and Travel Platforms (OTA).

Before getting into the findings, a word on how to read the numbers: the percentages are normalized source category shares within the observed Top 5 citations. Where we name a domain, it is the top site within that category, not the domain's own share; the exception is each model's "top observed domain" figure, which is domain-level. The results indicate directional trends rather than precise rankings, market share, or product quality. Treat everything here as a hypothesis to test in your own category.

The five models, side by side

Each model has a recognizable personality once you watch how it answers and where it looks.

ChatGPT (1.1B MAU) answers in summaries and comparisons, makes recommendations in a balanced, scenario-led way, and proves points with specs and trade-offs framed for the current year.

Community and Q&A content leads its citations, with Reddit far ahead of any other single domain.

Gemini (900M MAU) favors use-case explanations and is the most cautious recommender of the five, leaning on principles and standards with trend-aware framing. Official sources dominate its citations, which tend to be spread across many sites rather than concentrated in any one domain.

DeepSeek (130M MAU) prefers comparison tables and scenario-based recommendations, backed by specs and certifications with a latest-first bias. Industry media tends to lead in its citations, with Baidu the most-cited single domain.

Doubao (382M MAU) writes step-by-step playbooks, gives the most decisive recommendations of the group, and packs in dense costs and details. Short video and social content lead here by a wide margin, with roughly half everything it cites coming from Douyin alone.

Qwen (167M MAU) reads more like structured consulting, balanced and practical with an emphasis on costs and compliance. Industry media leads its citations, with Baidu again the most-cited single domain.

The same buyer question walks through a different front door in each model: Community leads for ChatGPT, official sources for Gemini, industry media for DeepSeek and Qwen, and short video for Doubao.

Insight 1: There is no single "AI Search Web"

ChatGPT, Gemini, DeepSeek, Doubao, and Qwen don’t rely on the same types of sources. Some models lean more heavily on official websites. Others draw more from industry media, communities, or short-form content.

That means a brand can be highly visible in one model and nearly absent from another, even when users ask similar questions. A page that earned you a citation in Gemini, with its 45% weighting toward official sources, may do nothing for you in Doubao, which drew 58.1% of its citations from short video and social.

Even models that look alike are not interchangeable. The closest pair we measured was Qwen and DeepSeek, at 90.1% category-mix similarity, and the caution still applies here: a similar mix does not mean identical domains. Two models can both lean on industry media for their answers, and still cite different publications for the same question.

The implication for GEO: do not publish everywhere. Build the right evidence for the models that matter to your audience.

Insight 2: The same industry can produce opposite source strategies

Even within the same industry, different AI models may rely on completely different source ecosystems. One model may favor official company content. Another may prioritize social platforms, vertical media/trade publications, or third-party evidence.

Two examples from our data show how far results within the same industry can flip. In Finance, Gemini drew 100% of its citations from official sources, while Doubao drew 64.7% from social. In Consumer Electronics/3c, Doubao drew 87.0% of its citations from short video and social while DeepSeek drew 89.4% from industry media.

So it’s not enough to simply ask the question "which sources matter in finance, automotive, or consumer electronics?" The better question is: which sources matter for this industry, on this specific AI model? Track the model industry combination, not averages alone.

Insight 3: B2B and B2C brands do not compete in the same evidence system

We want to be careful with this one, as it is a pattern from the current dataset, not a permanent rule. But it is an important starting point. In our data, B2B categories showed a stronger reliance on official sources: 23.4% of citations, against 7.9% for B2C.

Within B2C, categories were more distributed, with industry media at 27.5% versus 12.9% for B2B, alongside reviews, communities, and creators. This suggests two different GEO priorities for marketers:

For B2B, strengthen authoritative owned evidence, meaning products, use cases, implementation details, and comparisons. Much of the material these models cite is yours to write.

For B2C, build broader third-party validation across the platforms and sources consumers already trust, meaning vertical media, reviews, communities, and creators. Less of this sits under your direct control, so earning it becomes the actual work.

Inside the B2B heatmap

The full grid from the B2B industries on our cheat sheet deserves a careful read, as a few patterns stand out. ICT and Enterprise Services are some of the industries where publishing your own material pays off most directly. Official sources led both, with China-based developer communities like CSDN also carrying real weight in ICT, and third-party reviews mattering more in Enterprise Services than anywhere else on the B2B side.

Logistics looks completely different. Citations there concentrated in industry media, led by vertical trade sites, with official sources well down the list.

Finance had the most even spread of any industry we measured, dividing its citations across industry media, official sources, and short video in nearly equal parts, and it was the only industry where government sources made a visible showing.

For us, Manufacturing results were surprising. For a nominally technical B2B category, its citations skew toward short video and encyclopedia entries rather than company content. Notice how far Manufacturing sits from Enterprise Services, though both are nominally B2B – one rewards video and encyclopedia/directory presence; the other rewards your own product pages and third-party reviews.

Inside the B2C heatmap

The consumer categories are where vertical media and platform-native content take over, and the chart above shows how differently that plays out by industry. Automotive is the starkest case. Nearly three quarters of its citations came from industry media and automotive news sites, while brand-owned pages barely registered. Almost everything a model says about your automotive brand was written by someone else.

Consumer Electronics split almost evenly between short video and industry media, which is exactly the divide behind the Doubao and DeepSeek numbers in, with reviews taking a meaningful third place.

Sports & Outdoors followed a similar shape, relying on short video and review platforms.

Hotel & Travel was the one category where booking platforms led, with Ctrip out front for the Chinese models, followed by short video and news. Education & Training behaved unlike the rest of B2C: industry media dominated, but official sources held up better than in any other consumer category. The pattern here was closer to a B2B pattern than a consumer one.

One pattern held across every B2C category we tested: Reddit was the leading Community/Q&A site each time.

Taken together, the data show that the same audience question can require two completely different content strategies. A winning approach may involve strengthening owned content, building third-party media coverage, improving community visibility, or creating platform-native evidence, and often some combination.

GEO works less like a channel and more like an evidence system.

GEO is not a one-time optimization project. Models change. Sources change. Competitors publish. Citation patterns shift. A citation mix that describes your category in August is going to drift by year end, which is why a snapshot like this one has a shelf life.

A sustainable GEO programme needs a continuous operating rhythm:

1. Monitor prompts, models, competitors, and citations.
2. Diagnose visibility, narrative, and source gaps.
3. Ship stronger owned and third-party evidence.
4. Re-measure movement and choose the next priority.

Then the loop starts again. The brands that succeed in AI search will not be the ones that optimize once, but the ones that keep learning.

Where Emergine Fits

We built Emergine to make the monitoring and diagnosis steps of that loop fast and defensible. The first thing that sets us apart is coverage. Emergine tracks the Western engines your global stakeholders use (ChatGPT, Perplexity, and Google AI/Gemini) alongside the Chinese engines that decide your visibility in China (DeepSeek, Qwen, Doubao, and Ernie). Most tools cover one side of that divide. We cover more engines, in one place, so you are not stitching together two blind spots.

That depth on the Chinese side comes from where we started. Emergine is built by the team behind KAWO, the social media management platform that has spent more than a decade helping global brands operate on China's platforms. Reading the Chinese internet, and the AI models trained on it, is familiar ground for us.

The second is the experience. Emergine is quick to set up and quick to read, and it is priced to start small: our Lite tier is $29 USD a month, with a 7-day free trial, so you can see how your brand shows up across models before committing.

Start with what your buyers actually ask

The question is no longer only whether your page ranks. It is whether the model chose your evidence when it built the answer, and that choice happens differently in every model and every industry. Emergine helps teams monitor brand visibility across AI search environments, compare performance against competitors, and identify the content gaps that affect discovery.

Start with the one-page version of these findings below. How does this line up with what you’re seeing in your industry? We genuinely value your feedback.

Download the 2026 GEO Cheatsheet

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