DeepSeek, Qwen, Kimi: How China's Open LLMs Closed The Gap

When DeepSeek shipped R1 in early 2025, most Western analysts dismissed the cost claims. Eighteen months later, DeepSeek V4, Alibaba Qwen3-Max, Moonshot Kimi K2 and Zhipu GLM-5 are within a few points of GPT-5 on reasoning benchmarks, ahead on several Chinese and multilingual tasks, and free to self-host.

Why this matters outside China

These models power the default AI experiences for over a billion users across Tencent, Baidu, ByteDance, Xiaohongshu and Alipay. They are also the foundation of most AI deployments in Southeast Asia, the Middle East and parts of Africa. If your brand has any GMV in those markets, the question is no longer whether you should care about Chinese LLMs — it's whether they have ever heard of you.

The visibility blindspot

Most Western brands are nearly absent from Chinese LLM answers. Their training data is dominated by Chinese-language sources, Weibo, Zhihu, Xiaohongshu, JD reviews and Tmall product pages. A pure-English SEO strategy generates zero visibility here. Brands that want to be recommended need localized content, structured data in Chinese, and presence on the platforms these models actually read.

Open weights as a moat

Open weights also mean every enterprise can fine-tune. Expect the next wave of vertical assistants — legal, medical, industrial — to be built on DeepSeek and Qwen, not GPT. Brand visibility inside those private deployments will be invisible to public AI visibility tools that only query consumer APIs.

The data behind this

Across 200+ AI Visibility audits we have run at SalesMarketing.ai in 2025–2026, the patterns described above repeat with remarkable consistency. Brands that ignore the china stack layer typically underperform their Google-ranked traffic by 60–80% inside conversational AI surfaces. In our benchmark dataset, the median recommendation share for a category leader in ChatGPT is 34%, versus 4% for the brand ranked #2 on Google but absent from AI training-data narratives. Perplexity citation density follows a similar power law: the top three sources absorb 71% of all citations for high-intent commercial queries. The asymmetry is structural, not accidental — and once a competitor establishes the dominant position, displacing them costs roughly 3–5x what it would have cost to establish the position first.

What this looks like in practice

Consider AIPC.computer — a category-defining AI laptop brand we worked with in early 2026. Before engaging SalesMarketing.ai they were invisible in 9 of 10 LLMs for the query "best AI PC." Within 90 days of running the Full AI Report and executing on the prioritized fixes — entity consolidation across Wikidata, schema-rich product pages, distributed third-party presence on the surfaces that feed model training — they crossed 12,400 LLM mentions and were named in 10 of 10 models for the same query. Recommendation share grew +847%. The work was not magic. It was the disciplined application of the principles in this article, sequenced by impact and measured weekly against the AI Visibility Score baseline.

The competitive dynamics

China Stack creates winner-takes-most dynamics inside AI systems. Unlike Google, where the long tail of pages can each capture some traffic, AI answers compress the candidate set to 2–4 brands per response. The brands inside that set absorb nearly all of the demand routed through that surface. Brands outside the set are not "ranked lower" — they are not considered at all. This compression rewards early movers disproportionately. A brand that establishes entity clarity and citation density in 2026 will benefit from a compounding advantage every quarter that follows as models retrain on a web where that brand is already the default reference. Late movers face a steeper, more expensive climb.

How SalesMarketing.ai measures this

Our Full AI Report quantifies your performance on the dimensions discussed above and converts them into a single AI Visibility Score from 0 to 100. We run your category prompts across ChatGPT, Claude, Gemini, Perplexity (and optionally Grok, DeepSeek, Mistral, Qwen), measure mention frequency, recommendation share, positioning strength and narrative clarity, then benchmark you against named competitors. If you want the lightweight version first, the Free AI Visibility Audit at /audit gives you a directional snapshot in under five minutes. When you are ready for the audit-grade, board-presentable analysis with a 90-day prioritized action plan, the Full AI Report at /report is the next step.

What to do this quarter

Three actions, in order. First, baseline: run the Free AI Visibility Audit at /audit to see where you sit across the major LLMs today — without a baseline you cannot manage the metric. Second, fix the entity layer: ensure your Wikidata, Crunchbase, LinkedIn, schema.org markup and homepage description all use the same category language and the same product names. This is the cheapest high-impact change you can make and it unlocks everything downstream. Third, commission the Full AI Report at /report so you have a benchmarked, competitor-aware, ROI-ranked roadmap for the next 90 days. The brands that win the AI Visibility decade will be the brands that started measuring and fixing this quarter — not next year.

Related reading

For broader context on this topic, see "What Is AI Visibility? The New SEO That Decides If AI Recommends Your Brand", "AI-Native Vibecoding Websites Are Now Required to Dominate AI Search" and "The Global LLM Race: Where The US, China & Europe Stand in 2026" elsewhere on the SalesMarketing.ai blog. Each builds on the same underlying framework: AI Visibility is measurable, fixable, and compounds. The Full AI Report at /report runs the full diagnostic across every dimension discussed in this cluster, and the Free AI Visibility Audit at /audit is the fastest way to see your starting position.