DeepSeek · DeepSeek · Open-weight models, optional web search
How to get cited on DeepSeek
DeepSeek answers many questions from what its models learned in training. Web search is a switch the user turns on; when it's on, answers carry numbered links to the pages it read. That gives you two ways in: be well documented on the web before the model is trained, and be findable when someone searches. We track what DeepSeek says about your brand either way.
How DeepSeek picks what to cite
- With search off, answers come from training data, so long-standing, widely published facts about your brand carry the most weight
- With the Search toggle on in its chat app, it looks up the web before answering and attaches numbered citations to the pages it used
- Its models are released as open weights, so the same model can run inside other apps and products, often without web search at all
- When it does search, it can only cite what it can read: plain text on a public page, not facts hidden behind scripts or logins
What GetCited tracks for DeepSeek
- Whether DeepSeek mentions or recommends your brand for your buyer questions
- The claims it makes about you, and where they're wrong or out of date
- The sources and pages it cites when it searches
- Share of voice against your competitors, tracked daily
Where brands fall short
Common gaps brands have for DeepSeek
Thin coverage before the training cutoff
If your brand has little independent coverage on the web, the model has little to say about you with search off, or repeats an outdated description.
Key facts only visible with JavaScript
When search is on, pages that load prices, specs or claims client-side can be read as nearly empty, so another page gets quoted instead.
Not checking DeepSeek at all
Many teams only check ChatGPT and Google. DeepSeek's answers can differ from both, especially on which competitors it names.
How it compares
DeepSeek vs other AI engines
vs ChatGPT
ChatGPT searches on its own when a question needs fresh information; on DeepSeek, search is a toggle the user turns on. Training-time reputation carries more of the load here.
vs Perplexity
Perplexity is built around live search with inline sources. DeepSeek often answers from memory, so the fix is broad, consistent coverage, not just one well-ranked page.
vs Claude
Both lean on what the model learned in training. Independent, well-sourced coverage of your brand helps on both.
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