We recently observed an enthusiastic ChatGPT user using the app to look up a statistic, and mentioned that Perplexity might be better for that application. Many people don’t know about Perplexity; as of October 2025, ChatGPT is vastly more popular, with over 800 million weekly active users, compared to recently-cited statistics of perhaps ~25 million active users of Perplexity.
Perplexity offers different features than ChatGPT – or, for that matter, other major AI apps such as Claude and Gemini – and a head-by-head comparison helps elucidate when to use which AI tool. Short version: use Perplexity when you want to find current, citable information, whereas ChatGPT is better for longer-form writing, thinking, and exploration of topics.
This guide aims to provide any individual or small business AI user with a practical understanding of what’s going on “underneath the hood” when you type the same query into Perplexity vs ChatGPT, so that you can understand when one or the other is the best tool.
For those with a deeper understanding, note that we intentionally abstract certain technical details or nuances to keep the discussion readable for a broad audience.
At the simplest level, a large language model is trained on massive amounts of text. Think of it like feeding a super-powered librarian access to a library so enormous that if you read one book per minute, 24 hours a day, it would still take you tens of thousands of years to finish. These “books” include everything from classic literature and open web articles to scientific papers and public code. The model doesn’t memorize the pages; instead, it learns the patterns in how words and ideas connect.
During generation, the model predicts the next token (roughly a word fragment – see our fuller explanation of tokens here). It does so one at a time, based on everything it has “read” before. That’s why AI outputs stream out visibly on screen: you’re watching it make each micro-decision live, choosing what probably comes next in context. This is called autoregressive generation.
Because training happens on a frozen dataset captured at a specific point in time, a model’s built-in knowledge is always as current as that cutoff. Unless it browses the web or uses retrieval from other sources of live data, it can’t know what changed after that date – think of Captain America being frozen in the ice for decades, then waking up in the modern world. Vendors can update models with newer snapshots, but there will always be a gap that only search or external data sources can fill. That’s why a tool like Perplexity, which searches the web by default, behaves differently from a tool like ChatGPT, which only searches when either instructed to do so, or when it decides search is advantageous.
How each tool answers a query
Perplexity default behavior
Perplexity is search first by design. On most prompts, it queries the live web, gathers results from multiple sources, and composes a concise answer with citations in line by default. Perplexity Pro users can choose the underlying model that writes the response, including Perplexity’s own Sonar family or another leading model (such as ChatGPT or Gemini). Regardless of the model you pick, the search behavior remains the same. This is why Perplexity is the best AI tool to use when your task is to find current, source backed facts quickly and you want links you can click to verify.
ChatGPT and Gemini browsing behavior
ChatGPT can answer from its internal knowledge if a search is unnecessary. When a question looks time sensitive or source dependent, ChatGPT can search the web and include a Sources panel in the reply. You can also explicitly turn on Search. However, as we will see in a moment, search functionality does not always trigger when it should. Gemini offers similar browse when needed behavior inside its ecosystem, including automation that can use page context. The net effect is the same. These assistants can browse when needed, but they do not search by default on every query the way Perplexity does.
Real Life Examples of ChatGPT vs. Perplexity
We’d rather show than tell. Let’s start with an identical query: “how common are fires caused by dryer vents?”
ChatGPT provides an answer citing various statistics.
Perplexity provides a similar answer.
This makes sense if you think about it. General statistics and trends in dryer vent fires are unlikely to change dramatically in the short term; in fact, statistics may not even be updated that regularly – so Perplexity gains no advantage from “grounding” its search in the web vs. ChatGPT relying on its training data.
For a more time-sensitive query, however, the differences become stark. We asked ChatGPT how to set temperature for its new GPT-5 model (released in August 2025) via API:
Its answer is amusing on several levels. Not only is it completely unaware of the existence of GPT-5, but it also assumes that even if GPT-5 were to exist, you would be able to set the temperature via API.
That is incorrect (we know, because we’ve worked with the API). OpenAI does not allow a temperature input for GPT-5 via API. Perplexity provided the correct answer.
Finally, it’s worth noting that we recently had a similar experience with Google’s Gemini. We asked it about audio generation in Veo 3 (Google’s video generation tool), and Gemini claimed Veo 3 could not produce audio – when in fact it could! Once again, Perplexity found the right answer.
Choosing the right tool for the job
Use Perplexity when your goals are primarily to find, compare, and cite. Examples include quick landscape scans, checking what changed since a date, market maps with links to pricing pages, and lightweight due diligence that you can verify by clicking through the sources. (Perplexity can still misinterpret sources or hallucinate.)
Use ChatGPT when your task is to think, write, and build. Examples include drafting long form content, planning projects, analyzing files or data, building structured briefs for stakeholders, and running multi step deep research inside an assistant that can also format the finished product. (In a separate article, we will cover how NotebookLM is even better for some of these tasks.)
Conclusion
We hope this was a helpful exploration of the right tool to use for the job. We can build advanced AI automation solutions that integrate Perplexity, ChatGPT, and different tools to accomplish your critical small business goals. If you have questions, please contact us to talk through your use case. If you would like ongoing tips, join our mailing list.
