In January 2025, a single model release wiped roughly a trillion dollars off United States equities in a day, with Nvidia alone losing almost 600 billion dollars of it, the biggest one-day loss in stock-market history, as its shares fell about 17 per cent. The trigger was DeepSeek-R1, a frontier-class reasoning model from a Chinese lab that had spent a fraction of the usual budget, topping the free-app charts on the US App Store almost overnight. Eighteen months on, the panic has cooled and the substance remains. DeepSeek is the fifth most-visited AI product on the open web, the runaway leader inside China, and it runs a live web search that crawls and cites real pages as it reasons. For any brand watching how it is described by AI, DeepSeek is no longer a curiosity to file away. It is an engine whose answers about you are already being read by a very large audience.
This guide explains what DeepSeek is, how its search actually works, how it differs from Western answer engines like ChatGPT and Gemini, and the practical implications for how your brand is described, cited and recommended.
What DeepSeek is
DeepSeek is a generative AI assistant built by a Chinese company of the same name. The lab was established in July 2023 as a spinoff from High-Flyer, a quantitative hedge fund co-founded by Liang Wenfeng, who serves as chief executive of both. That lineage matters. DeepSeek was funded and staffed largely from a trading firm's research operation, which helps explain its obsession with efficiency and its willingness to publish openly rather than guard everything behind an API.
The lab's models are released as open-weight under permissive terms. Headline releases such as the R1 reasoning model and the V3 general-purpose model carry the MIT licence, meaning the parameters are published for broad commercial and private use, even though the training data and full methodology stay proprietary. Open-weight is not the same as fully open source, but it is far more open than the closed models from OpenAI, Google and Anthropic, and it is a large part of why DeepSeek spread so fast through the developer community and into other companies' products.
The current flagship is DeepSeek-V4, released in preview on 24 April 2026 in two variants. V4-Pro is a roughly 1.6-trillion-parameter mixture-of-experts model with about 49 billion parameters active per token, while the lighter V4-Flash totals around 284 billion with 13 billion active. Both ship under the MIT licence, support a context window of up to one million tokens, and offer toggleable thinking and non-thinking modes. For brand-monitoring purposes the headline number matters less than the surface where answers appear: the consumer chat app and website at deepseek.com, and the API that thousands of developers wire into their own tools.
Momentum is not slowing. In June 2026 DeepSeek closed its first external funding round, raising about 7.4 billion dollars at a valuation above 50 billion, with Tencent and CATL among fewer than ten backers and Liang himself reportedly the single largest contributor at around 40 per cent of the round. A company that started as a side project of a hedge fund is now one of the better-capitalised AI labs in the world.
How DeepSeek search works
DeepSeek did something genuinely novel when it added web search. R1 was widely described as the first reasoning-focused model to fold live retrieval into its chain of thought, rather than bolting a search box onto a standard chat model. The result is a search experience you can watch unfold, because the model narrates the steps as it takes them.
When a user enables Search mode, DeepSeek does not simply paste in the top results from a search API. Independent analysis of its behaviour describes a four-stage loop that mirrors how a careful person researches a question:
- Interpret and expand. A smaller, faster model rewrites the query into optimised search keywords, breaking a broad question into specific terms.
- Shortlist URLs. It queries a web index for candidate pages, then judges them by their metadata and snippets the way you skim a results page, keeping the ones most likely to hold the answer.
- Crawl and read. It fetches the shortlisted pages in real time and runs the content through cleaning, relevance scoring and summarisation before anything reaches the model.
- Reason and attribute. R1 feeds the cleaned content into its reasoning, then composes an answer with visible working and inline citations.
Two consequences follow. First, DeepSeek does not run a large proprietary web index of its own the way Google or Perplexity do. It leans on an external search layer to surface candidates, then does its own selective crawling and reading, an approach analysts describe as adapting the retrieval-augmented pattern rather than building bespoke crawl infrastructure. Second, recency drives where it looks. A question about a fast-moving topic pushes it towards fresher pages, while a stable factual question leans on established reference sources. Because the model exposes its reasoning, users can often see exactly which sources shaped the reply, which is rarer in Western consumer assistants.
How DeepSeek differs from Western answer engines
On the surface DeepSeek looks familiar: a chat box, a search toggle, an answer with citations. The differences sit underneath, and they shape both how it behaves and how comfortable a given organisation will be relying on it.
| Dimension | DeepSeek | ChatGPT / Gemini |
|---|---|---|
| Owned web index | No, external search plus live crawl | Largely yes |
| Model weights | Open-weight, MIT licence | Closed |
| Reasoning visibility | Shows retrieval and evaluation steps | Mostly hidden |
| Primary audience | China-led, strong in select markets | Global |
| Data residency | Mainland China | US and regional |
| Citation style | Live sources, visible reasoning | Live sources, varies by mode |
The data-residency row is the one with the sharpest consequences. DeepSeek's consumer service stores user data, including IP addresses, uploaded files and even keystroke timing, on servers in mainland China, where law grants the state broad authority to compel access. That single fact has driven a wave of restrictions: Italy's regulator pulled it from app stores in early 2025 over unanswered GDPR questions, and Australia, Taiwan and South Korea have all banned it from government devices and public-sector systems on national-security grounds. The pattern targets official use of the consumer app, not the underlying open weights, which is why DeepSeek-derived models still appear inside products well outside China.
For brands, the bans do not make DeepSeek irrelevant. They make its audience a function of geography and sector rather than a uniform global crowd. You should weight its visibility by where your buyers actually are, instead of treating it as interchangeable with ChatGPT.
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Where DeepSeek sits in the market
Two numbers tell the whole story, and they pull in opposite directions. By raw web traffic, DeepSeek logged about 541 million monthly visits in May 2026, up roughly 11 per cent month on month, which kept it first in China and fifth among all AI products worldwide. By share of the global AI assistant audience, though, it sits under five per cent, well behind ChatGPT, Gemini and Claude.
Both figures are true at once because DeepSeek's traffic is heavily concentrated. Microsoft's usage analysis put its share inside China near nine in ten, with strong footholds in markets such as Russia, Belarus and Cuba, while its share of referrals across the rest of the world stayed under one per cent. DeepSeek is a near-monopoly at home and a challenger almost everywhere else. The Western chatbot race, meanwhile, has its own churn: ChatGPT slipped below 50 per cent of the global assistant market for the first time in mid-2026, falling to about 46 per cent, with Gemini near 28 per cent and Claude around 10 per cent on Sensor Tower's figures, a reshuffle we track in the AI chatbot market share figures for May 2026.
Market share (%)
Top four generative AI chatbots compared (ChatGPT figure includes Copilot)
### A simple test for how much DeepSeek should matter to you
Rather than guess, score your situation against three questions. The more you answer yes, the more direct attention DeepSeek deserves in your monitoring.
- Audience. Do you sell into China or into markets where DeepSeek leads, such as parts of Eastern Europe, Central Asia or Latin America? If yes, its descriptions of your brand carry real commercial weight.
- Category. Is your category one buyers research before purchase, where an AI summary can shape a shortlist? Considered B2B and B2C purchases are far more exposed than impulse buys.
- Spread. Do your competitors already surface in DeepSeek answers when you don't? A visible gap is a clearer signal to act than any market-share chart.
For a fuller view of how DeepSeek compares with the wider field, see our roundup of the best AI search engines in 2026, and for the head-to-head between the two engines most Western buyers use, our breakdown of Perplexity versus ChatGPT for brand ranking.
What DeepSeek's rise means for brand visibility
The mechanics of DeepSeek search point to clear, unglamorous priorities. Because it crawls candidate pages in real time rather than serving them from a pre-built cache, your content has to be reachable and fast at the moment it looks. Pages that are slow, intermittently available or blocked to crawlers simply will not make it into the set DeepSeek reads from. If you are unsure which bots can reach you, our AI crawler user-agents reference lists the agents to allow.
Beyond availability, the patterns that help with DeepSeek are the same ones that help across every answer engine. Factually dense, clearly structured content with descriptive headings, lists and data tables is easier for a model to extract and quote accurately, and DeepSeek's source selection is documented to favour exactly that: clear credibility markers, recent dates and clean technical implementation. Valid schema markup such as FAQ and product structured data makes a page's meaning machine-readable rather than something the model has to infer. And third-party credibility still counts: established reference sites, reputable publications and substantive community discussion all feed the picture a model forms of you. This is the same logic that governs how AI models choose which brands to recommend across the board, not only DeepSeek.
There is one DeepSeek-specific upside worth naming. Because the weights are open, the way DeepSeek reads and ranks your content does not stay inside the official app. Self-hosted deployments and third-party tools built on its models inherit similar behaviour, so the work you do to be cited well by DeepSeek compounds across a longer tail of surfaces than a single closed engine ever could.
How to monitor your brand in DeepSeek
Spot-checking DeepSeek by typing in a few queries gives you a snapshot and little else. Because its search assembles sources live, two runs of the same prompt can cite different pages, and a single check tells you nothing about direction of travel. To know whether your visibility is improving or slipping, you need the same prompts run on a schedule and the results compared over time. We have written separately about why spot-checking fails and what consistent measurement looks like instead.
Honeyb tracks how your brand is mentioned, cited and described across the major AI answer engines, runs scheduled scans so you see change rather than a single frame, and benchmarks you against named competitors. If you want a quick read before committing to ongoing monitoring, the free AI visibility checker shows how engines currently describe your brand, and the people also ask tool surfaces the questions buyers are actually putting to these systems.
DeepSeek is unlikely to displace ChatGPT or Gemini across most Western markets, and it does not need to. It is already the default route to answers for an enormous population, its models sit inside products well beyond its own app, and its search rewards exactly the structured, reachable, credible content that good visibility work produces anyway. Treat it as one more engine where your brand is being described, weight it by where your buyers really are, and measure it consistently rather than once.
