# How DeepSeek, Qwen, and Kimi Describe Crypto Projects By Joha J. Sulaymonov, Co-CEO, Lanesra AI · 2026-08-03 · AEO for Web3 > Chinese-origin AI models shape a large share of crypto brand visibility across Asia and price-sensitive markets, yet almost no Western AEO tool tracks them. Here is how DeepSeek, Qwen, and Kimi describe crypto projects, and why it matters. Source: https://lanesra.ai/blog/how-deepseek-qwen-and-kimi-describe-crypto-projects If your AEO strategy only tracks ChatGPT, Gemini, and Perplexity, you are measuring how your crypto project looks to the West and going blind to Asia. A large share of the people forming opinions about your token, protocol, or exchange are not asking ChatGPT. They are asking DeepSeek, Qwen, or Kimi, in Chinese, in Southeast Asian languages, and increasingly in every price-sensitive market where a free, open Chinese model beat the paid Western one to the mainstream. Crypto is one of the most globally distributed industries that exists. Its users, traders, and builders cluster heavily in exactly the regions where Chinese-origin models dominate. That makes these engines not a curiosity but a core visibility surface, and almost no AEO tool tracks them. The short answer **DeepSeek, Qwen, and Kimi are frontier-grade AI models that dominate AI usage across China, Southeast Asia, and price-sensitive emerging markets, exactly where crypto adoption is highest.** They describe, rank, and recommend crypto projects to hundreds of millions of users, yet Western AEO tools do not track them. Lanesra does. ## Why these models are not a niche The instinct to treat Chinese models as a side issue is a Western blind spot, and the data contradicts it directly. 89% DeepSeek AI market share in China by end of 2025 Microsoft AI diffusion report, via eMarketer 130M+ DeepSeek active users, majority based in Asia Business of Apps, 2026 Top 5 of the top 10 developer-used models were Chinese in mid-2026 Fortune, July 2026 1/15 the cost of Western frontier models for comparable workloads TokenMix benchmarks, 2026 The cost gap is why these models spread through emerging markets so fast. DeepSeek reached large market shares not only in China but across price-sensitive regions where paid Western AI stays out of reach. That distribution footprint overlaps almost perfectly with where retail crypto activity is heaviest. > This is not theoretical for crypto specifically. Coinbase's CEO publicly stated the exchange halved its AI spending by moving employees onto Kimi and Z.ai's GLM models. When a leading US exchange runs on Chinese models internally, the idea that these engines do not matter to crypto is already obsolete. ## The three engines, and how they behave These are not interchangeable. Each has a different training profile, retrieval behavior, and market center of gravity, which means each can describe the same crypto project differently. DeepSeek by DeepSeek (High-Flyer), China The model that put Chinese AI on the global map. Open-weight, effectively free to use through its chatbot, and dominant in China with heavy penetration across developing markets. Its user base skews technically literate and workflow-oriented, which for crypto means it is often the engine a developer or serious trader reaches for first in these regions. Strong on reasoning and code, which matters when users ask it to evaluate a protocol's mechanics or a token's contract. Qwen by Alibaba, China Alibaba's flagship family, notable for multilingual strength and very large context windows. Qwen leads among these models on multilingual work, which makes it especially relevant for reaching crypto audiences across Southeast Asia's many languages. It is also being adopted by Western companies for cost reasons, so its reach extends well beyond China. If your project has users in multiple Asian languages, Qwen is likely describing you in all of them. Kimi by Moonshot AI, China Moonshot's model, built with an emphasis on agentic tool use and long-horizon tasks, with very large context windows. Kimi is increasingly the engine behind autonomous agents and research workflows, which matters as crypto discovery shifts from a human typing a question to an agent compiling a due-diligence summary. If agents start evaluating protocols on behalf of users, Kimi is one of the engines doing that evaluation. ## Why the same project gets described differently A model's view of your project is shaped by what it was trained on and what it can retrieve. Chinese-origin models draw on a different corpus than ChatGPT: more Chinese-language sources, different community platforms, different news and exchange coverage. The practical consequences for a crypto brand are concrete. - **Different sources cited.** A model trained heavily on Chinese-language and regional platforms weights different community signals than one trained mostly on Reddit and English media. Your English press coverage may not move your visibility on DeepSeek at all. - **Different sentiment framing.** Regulatory context, exchange listings, and reputation signals differ sharply by region. The same project can read as credible in one model and risky in another purely because of which sources dominate each corpus. - **Language-specific gaps.** A project well-documented in English but thin in Chinese or Southeast Asian languages can be near-invisible or poorly described on the models that serve those users, even while it looks strong on ChatGPT. - **Different recency.** When a model retrieves live sources, which sources it can reach and how fresh they are varies by engine, so your latest listing or partnership may surface on one model weeks before another. ## What Western AEO tools miss The mature AEO platforms, Peec, Profound, Scrunch, Conductor, were all built for Western marketing teams tracking Western engines. None of them were designed to measure how a crypto project appears on the models that actually dominate Asian and emerging markets. Capability Typical AEO tool Lanesra ChatGPT, Gemini, Perplexity tracking Yes Yes DeepSeek visibility tracking No Yes Qwen visibility tracking No Yes Kimi (Moonshot) visibility tracking No Yes Multi-language prompt tracking (CN, SEA) Rare Yes Web3-specific prompt libraries No Yes ## What a crypto team should do about it - **Map where your users actually are.** If a meaningful share sits in China, Southeast Asia, or emerging markets, your visibility on Chinese models is not optional to measure. - **Track all three engines, not just ChatGPT.** Measure visibility, position, and sentiment on DeepSeek, Qwen, and Kimi the same way you would on Western engines, and compare. The gaps are where your risk and opportunity sit. - **Build region-appropriate authority.** English press coverage does not necessarily move Chinese-model visibility. Documentation, listings, and community presence in the relevant languages and platforms do. - **Watch the agentic shift.** As agents built on models like Kimi begin compiling project evaluations autonomously, being accurately represented in the sources those agents retrieve becomes a direct driver of discovery. ## Why now Chinese models reached genuine frontier parity with Western ones in 2026, at a fraction of the cost, and their distribution is still expanding fastest in exactly the markets where crypto adoption is highest. Almost no one is producing quality analysis of how these engines describe crypto brands, which means the projects that measure and act now establish authority in an uncontested space. The visibility that compounds is the visibility you can actually see, and right now most of the industry cannot see half of it. ### See how Chinese models describe your crypto brand Lanesra tracks your visibility, position, and sentiment across DeepSeek, Qwen, Kimi, and every major AI engine, in every market and model. [Start free with Lanesra →](https://lanesra.ai)