GEO for DeepSeek, Kimi & Qwen: Getting Cited in Alternative LLMs

By Cory Maki · Oct 10, 2026

Almost every GEO guide optimizes for the same three engines: ChatGPT, Gemini, Perplexity. But a growing share of the world asks DeepSeek, Kimi, and Qwen — fast-rising models, huge in China and increasingly used globally. They source the web differently, and the brands that show up in them early will own that visibility as they scale. Here is how generative engine optimization applies to the alternative LLMs, and what transfers from the Western playbook.

The engines, briefly

• DeepSeek — an open, high-performing model from a Chinese lab, widely adopted for its capability-to-cost ratio; used globally via its app and API.
• Kimi (Moonshot AI) — known for very long context and strong web/document grounding, popular in China.
• Qwen (Alibaba) — Alibaba's model family, deeply integrated into Chinese commerce and cloud, with growing international use.

They are not niche. For many buyers — especially in Asia and among developers — these are a primary way they ask questions about products and companies.

How sourcing differs

Two differences matter for visibility:

1. The source pool skews to their ecosystem. Where Western engines fan out to Google/Bing and Reddit, these models lean more on Chinese-language sources, local platforms (think Zhihu, Weibo, Baidu-indexed content) and their own training data. Western review sites and Reddit carry less weight.
2. Web grounding varies by product. Some run primarily from training data with limited live retrieval; others (Kimi especially) ground answers in fetched documents. Whether you can influence an answer now depends on which mode is in play.

What transfers from Western GEO

The fundamentals hold everywhere, because they are about being legible and trustworthy to a model:

• Be the citable, well-structured source — clear answers to specific questions the model can lift.
• Build a consistent entity — the same facts about you everywhere, so the model resolves who you are. Citation share is still how you measure presence.
• Earn authoritative, third-party corroboration — the principle that off-domain authority beats self-published claims does not change across borders.

What is specific to alternative LLMs

• Publish in the language of the audience. A model trained and grounded on Chinese-language content will cite Chinese-language sources. If you want visibility in Qwen or DeepSeek for a Chinese audience, bilingual content is not optional.
• Earn presence on local platforms. The Reddit-equivalent for these engines is the local community and Q&A ecosystem, not Reddit itself. Presence there is the analog of the Reddit authority that drives Western AI citations.
• Measure separately. Run your buying questions through each engine directly; do not assume your ChatGPT results predict your DeepSeek results.

The first-mover case

These engines are early, and so is the competition for citations in them. The brands that build a legible entity and earn local-source authority now will be the default answers as these models grow — the same way early Reddit investment is paying off in Western AI search today.

Want a baseline across engines — including where you stand beyond the usual three? Start with a visibility report, or work through a plan together.