When most people open ChatGPT, they assume it already knows everything. But a new data study shows the chatbot still turns to the internet more often than many realize. Researchers found that in nearly one out of every three prompts, ChatGPT performs an online search to gather extra information before answering.
Tracking ChatGPT’s Search Behavior
To measure how often this happens, analysts at Nectiv examined more than 8,500 user prompts across nine major industries, including travel, fashion, software, and local services. They used an internal tool that detects when ChatGPT connects to external sources to look up facts. Each time the model reached beyond its own knowledge, the system recorded a “search instance.”
Across all of those prompts, 31 percent led to at least one web lookup. This means ChatGPT relies on live data far more than many users realize.
How Many Searches Per Question?
The same study revealed that ChatGPT rarely stops at a single search. On average, it carried out just over two separate queries... 2.17 per prompt, to be precise. In a few cases, the number went as high as four. These repeated lookups, called fan-out searches, help the model verify or expand its answers when information is incomplete.
For example, if a person asks for the best phone brands in 2025, ChatGPT may check multiple product pages, comparison lists, or recent reviews before forming its response.
Searches That Are Longer and More Specific
ChatGPT’s search phrases are noticeably longer than a normal Google search. The study found the average query length was 5.48 words, roughly 60 percent longer than the U.S. average of 3.4 words. In total, about 77 percent of its searches contained five words or more.
That suggests ChatGPT forms detailed, focused questions, closer to how a skilled internet user searches rather than a casual one. Typical examples include “top car rental Turkey reviews” or “best ecommerce software 2025 features.”
Which Topics Trigger the Most Searches
Not all subjects push ChatGPT to search equally. Local information caused the most lookups, about 59 percent of local prompts triggered a web search. Commerce-related requests came next at 41 percent. At the other end, only 18 percent of credit-card questions and 19 percent of fashion topics led to searches.
This pattern shows ChatGPT depends most on real-time data for areas that change frequently, such as nearby businesses or current products.
How Deep It Digs in Each Field
Even though local searches were most frequent, they tended to involve fewer follow-up queries... about 1.67 on average. By contrast, questions about jobs, careers, and software often led to three or more searches per prompt. Those fields usually require complex comparisons and up-to-date details, which explains the higher activity.
What ChatGPT Looks for Online
When analyzing the words inside those thousands of search phrases, researchers saw recurring themes. Many contained terms such as “reviews,” “comparison,” “features,” and the current year “2025.” These keywords show that ChatGPT favors fresh, review-style, and product-oriented content when seeking supporting information.
In simple terms, it behaves like a digital researcher checking multiple recent sources before forming an answer.
Why These Findings Matter
Understanding when ChatGPT searches helps explain how it builds its answers. The model does not rely only on its stored knowledge; instead, it supplements it by scanning the web for updates. For website owners and marketers, that means optimizing content for detailed, review-based, and current-year searches could make it more visible to AI systems.
For ordinary users, the results show ChatGPT is less of a static knowledge bank and more of an active information finder that continually checks the internet to stay relevant.
The Bigger Picture
In the end, the numbers give a clear picture. ChatGPT is not only generating text but also performing its own background research. Roughly one-third of the time, it steps out to the internet, sends out a couple of searches, and pulls in longer, more specific results.
That makes it less like a traditional chatbot and more like a hybrid search assistant, one that mixes stored intelligence with real-time exploration to produce its answers.
Notes: This post was edited/created using GenAI tools.
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