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OpenAI’s API did not reliably reproduce the pages cited by ChatGPT in a 2026 preprint study. When researchers combined Google’s organic results with repeated web-enabled OpenAI Responses API queries, they recovered only 26.2% of the exact pages cited by GPT-5.5 Instant, although they identified 48.7% of the cited sites. GPT-5.3 Instant showed a similar gap: 28.3% of pages and 50.3% of sites. For anyone auditing AI visibility, matching a domain is therefore a much weaker result than reproducing the exact article a user saw.
What the study measured
The paper compared citations displayed in ChatGPT’s web application with two stand-ins: Google organic search results and web-enabled queries sent through OpenAI’s Responses API. The experiment used 80 questions from Search Arena—40 in English and 40 in Chinese—covering recommendations, factual lookups, information synthesis and analysis.
Researchers ran each question repeatedly in ChatGPT, separately collected up to five Google organic-result pages, and issued repeated API queries. They then compared ChatGPT’s cited URLs and the sites hosting those URLs with what the combined Google-plus-API process retrieved. Data collection ran mainly from March 29 through July 29, 2026. GPT-5.2 Instant and GPT-5.4 Thinking were decommissioned before all planned runs finished, so the most complete comparisons concern GPT-5.3 Instant and GPT-5.5 Instant.
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The figures come from a preprint, not a peer-reviewed journal article, and describe those model versions and that collection period rather than every current or future release.
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Exact pages were much harder to reproduce than sites
| ChatGPT model | Exact cited pages recovered by the combined stand-in | Cited sites recovered | Distinct pages appearing in only one of 20 runs |
|---|---|---|---|
| GPT-5.5 Instant | 26.2% | 48.7% | 44.5% |
| GPT-5.3 Instant | 28.3% | 50.3% | 40.8% |
The page figures are the strict test: they ask whether an evaluator found the same article or URL that ChatGPT cited. Site-level recall asks only whether the same publisher or domain appeared somewhere in the retrieved material. A tracker can therefore report that a publisher was visible while missing the specific page that informed the answer.
Run-to-run variation explains part of the difference. Of all distinct pages returned for a question, 44.5% for GPT-5.5 Instant and 40.8% for GPT-5.3 Instant appeared in only one of 20 runs. Pages that appeared more often were more likely to be cited, but the report does not establish that frequency alone caused citation selection.
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ChatGPT did not usually search the user’s wording verbatim
In visible-search runs, GPT-5.3 Instant and GPT-5.5 Instant converted the original question into one or more more-specific search terms. This query rewriting means that sending the user’s exact sentence to an API endpoint is not a faithful reproduction of ChatGPT Search.
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What the findings mean for citation monitoring
Measure URL recall and domain recall separately
Report exact-page matches as the primary audit metric when the question is whether a reader could verify ChatGPT’s citation. Keep domain or site matches as a secondary, directional measure. Combining them into one score hides the difference between finding the right publisher and finding the right article.
Repeat prompts instead of trusting one answer
A single response can overstate or understate visibility because the cited set changes between runs. Record a run number and use a fixed repetition count. The study’s 20-run analysis provides a practical illustration of why one screenshot is not a stable benchmark.
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Capture the access path and model
Log whether a result came from the ChatGPT interface or the Responses API, the exact model/version label, the search condition, the prompt language and the collection date. Model updates can change retrieval and citation patterns, so a benchmark should be rerun after a version change.
Preserve rewritten queries when available
If the interface exposes search terms, store them alongside the original prompt. Without those transformed queries, an auditor cannot tell whether a mismatch came from ranking, language selection, query interpretation or a different retrieval source.
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Why API-only tests can miss deployed ChatGPT behavior
A related audit by Encarnación, Behzad, Lurie and Metaxa evaluated 401 prompts and 4,812 responses across ChatGPT’s interface and OpenAI’s API, with and without web search. It measured accuracy, response consistency, text similarity, citation grounding and abstention. In that benchmark setting, enabling web search changed accuracy by as much as eight percentage points, and repeated runs were inconsistent for up to 21% of prompts.
Those results do not prove that one access path is always better. They show that modality and search condition are part of the behavior being measured. An API-only, single-run evaluation can miss differences visible in the deployed chat product, just as a Google-only check can miss pages selected through ChatGPT’s own retrieval process.
Surfaced citations are not the same as proof of source use
OpenAttribution’s May 2026 dataset recorded 307 API responses with no surfaced citations even though web search was enabled; 281 of those responses came from OpenAI. A missing citation is a transparency and auditability problem, but it does not demonstrate that the system used no sources internally. Monitoring should therefore distinguish between “no citation displayed” and “no retrieval or source use occurred.”
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A reproducible audit workflow
- Freeze the test set. Keep the exact prompts, including punctuation, language and any location or date qualifiers.
- Identify the model and interface. Record the ChatGPT model/version, API model, web-search setting and collection date for every run.
- Run each prompt repeatedly. Use a predetermined number of runs rather than selecting a convenient answer. Store every cited URL, title and displayed source label.
- Collect comparison results. Save the first five Google organic pages where that is your chosen limit, and run the corresponding web-enabled Responses API queries repeatedly.
- Preserve search transformations. Record ChatGPT’s rewritten queries and the language of each query whenever the product reveals them.
- Calculate two recall measures. Count exact URL matches separately from matches at the publisher or domain level; report the denominator and run count.
- Re-run after changes. Repeat the benchmark after a model replacement, search-system change or major index update. Do not carry old percentages forward as if they were permanent.
How to read the percentages without overclaiming
- The 26.2%, 28.3%, 48.7% and 50.3% figures describe the tested questions, models and dates, not a universal API-versus-ChatGPT conversion rate.
- The combined Google-plus-API process is a comparison stand-in, not a claim that Google and the API are the components ChatGPT uses internally.
- Site-level agreement can indicate that a publisher is discoverable while saying nothing about whether its particular article was cited.
- High variation across runs means a visibility score should include a distribution or repeat-rate, not only one pass.
- Because the page-overlap work is pre-peer-review, its methods and estimates should be treated as provisional evidence and rechecked against later releases.
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