ChatGPT vs Perplexity: Source Credibility Changes the Choice

Perplexity has the stronger published result for open-web source credibility. In an EACL evaluation of 100 claims across five misinformation-prone topics, its cited-source credibility averaged 86.30%, compared with 75.16% for GPT-4o and 71.37% for GPT-5. The evaluation reflects web-search behavior from the second half of 2025, rather than a comparison of current deep-research modes.

ChatGPT deep research may be the better choice when an assignment requires approved websites or a report whose source access and direction can be changed during the run. Perplexity is a compelling choice for investigating contested claims on the public web. The deciding factor is the evidence set: whether the assistant must discover credible sources or work within sources you have already defined.

What the credibility finding measures

A citation answers only part of the research question. A linked page may be reputable yet fail to support the sentence beside it; a closely matched page may come from a weak source. The evaluation examined both the credibility of cited domains and whether factual statements were grounded in the material cited. Perplexity led on source selection under those test conditions, giving its open-web case a firmer basis than answer fluency or citation count alone.

The researchers classified cited domains using factuality ratings and categories such as government and research publications. Unrated domains were excluded from the credibility-rate calculation. That makes the result useful for comparing the tested assistants’ source choices, but the percentage is not a probability that any particular answer is correct. The claims concerned topics prone to misinformation, including health, climate change, politics, war and local issues; a course reading list or an organization’s private files pose a different retrieval problem.

Source boundaries favor different workflows

ChatGPT gives deep-research users an explicit choice between limiting web research to entered sites or domains and prioritizing those sites while allowing a wider search. OpenAI’s deep-research guidance also describes an editable proposed plan, progress monitoring and the ability to interrupt a run to change its focus or accessible sources. These controls are useful when admissible evidence is specified before the research begins.

Restrict and prioritize serve different purposes. A student required to use a named archive, for example, needs a boundary; an analyst who wants official filings to lead a broader investigation needs a preference. Those are conditional examples, not claims about either tool’s performance on a particular assignment. The distinction matters because a polished answer drawn from outside the permitted corpus can still be unusable.

Perplexity Projects let users add web links and domains to prioritize, alongside persistent instructions and other project context. Prioritization helps keep recurring work oriented toward relevant material, but it is a different documented control from ChatGPT’s option to restrict deep research to specified sites. For work governed by a strict source list, that difference may outweigh a general web-search credibility result.

Connected material raises a freshness question

ChatGPT deep research can work with uploaded files and eligible connected apps as well as web sources. Access to a connected source depends on the user’s account, workspace settings, app support and permissions at the provider. A report that needs internal documents is therefore only as complete as the material the research run can actually reach.

Perplexity’s Projects guidance describes persistent files, connected tools and source preferences. It also identifies a specific freshness limit: a file added from a connector is a one-time copy, so later changes to the original do not update the Project copy. Perplexity advises fetching the latest version through the connector or adding the file again when current content is needed.

That detail changes how a recurring research workspace should be judged. An answer grounded in a saved copy of a policy, filing or dataset can have a clear citation trail while still missing a later revision. Conversely, access to a current connected file can make a narrower research run more useful than a broad public-web search. The relevant comparison is the provenance and age of the material used in the particular answer.

Citations, reports and control during a run

ChatGPT deep research produces a structured report with citations or source links, a sources-used section and an activity history. Its plan can be reviewed before work starts, then the run can be interrupted to adjust the question or source access. That combination is valuable when a long assignment changes direction after early evidence exposes the wrong jurisdiction, period or type of publication.

Perplexity’s Advanced Deep Research guidance describes clarifying questions before a broad task, follow-up questions while research is running, a progress view showing sources being read, and reports that can be edited and shared. It also describes direct work with uploaded documents. Perplexity therefore has meaningful controls during research, even though its documented follow-up questions and Project source preferences are not the same as an explicit site restriction.

For either product, citation traceability depends on the claim, not the number of links at the end of a report. A useful citation identifies the material behind a consequential sentence and lets a reader inspect whether that material actually supports it. The published evaluation’s separation of source credibility from groundedness explains why both checks matter: a well-supported statement can still rest on an unreliable page.

Why a product update does not transfer the result

Research modes and model configurations change. Perplexity’s February 2026 product update described an upgraded Deep Research system combining available models with its search engine and sandbox infrastructure, along with organizational controls over feature and model access. Those are features of a later product configuration; they do not turn the earlier web-search evaluation into a score for that configuration.

The same boundary applies to ChatGPT. Results for GPT-4o and GPT-5 using web search cannot be assigned to every later model, connected app or deep-research setting. Topic and question framing also affected retrieval in the evaluation. The published finding is strongest when the task resembles its tested setting: checking disputed claims through open-web search.

Which one fits the assignment?

Choose Perplexity when discovering credible public-web sources is the central difficulty, especially for a contested claim. Its measured source-selection advantage gives that choice a concrete basis, while the final claims still need citations that lead to supporting material. Choose ChatGPT deep research when the assignment starts with an approved domain list, eligible connected data or a report whose plan and source boundaries may need adjustment during the run.

If both tools can reach the required material, the remaining difference is what control the researcher needs over it. Perplexity’s Projects and research reports support continuing work across files, sources and follow-up questions. ChatGPT’s explicit site restriction is the clearer fit when evidence outside a defined set would make the result unusable.