# ChatGPT Keeps Its No-Search Top Pick in 7 of 10 Prompts **URL:** https://organikpi.com/blog/geo-ai-search/chatgpt-memory-vs-search-brand-recommendations/ **Published:** 2026-10-28 **Modified:** 2026-10-28 **Author:** Daniel Shashko > ChatGPT keeps the same #1 pick in 110 of 158 prompts whether it searches or not. Memory reruns agree on #1 82.7% of the time, search reruns 74.9%, cross-arm pairs 65.7%. Search adds about 2 brands per prompt and drops 1.7, and about half the swaps appear in only 1 of 3 runs. Of 1,493 brand-prompt pairs, 60.4% appeared in both arms; memory favourites slipped from position 1.92 to 2.29 under search, and 14 of 425 dropped out. The gap between arms varied by category: 14.1 pp for business services, 7.6 pp for developer tools. --- > ChatGPT keeps the same #1 pick in 110 of 158 prompts whether it searches or not. Memory reruns agree on #1 82.7% of the time, search reruns 74.9%, cross-arm pairs 65.7%. Search adds about 2 brands per prompt and drops 1.7, and about half the swaps appear in only 1 of 3 runs. Of 1,493 brand-prompt pairs, 60.4% appeared in both arms; memory favourites slipped from position 1.92 to 2.29 under search, and 14 of 425 dropped out. The gap between arms varied by category: 14.1 pp for business services, 7.6 pp for developer tools. We asked ChatGPT 158 buyer questions three times without web search (“memory”) and three times with it (“search”), 948 answers in all. The #1 recommendation matched in 110 of 158 prompts, and search flipped it in about 3 of 10. Two memory runs agreed on #1 82.7% of the time, two search runs 74.9%, and a memory run against a search run 65.7%. Search swapped about 2 brands in and 1.7 out per prompt. About half of those swaps did not repeat across runs: 55.8% of search-only brand-prompt pairs appeared in just 1 of 3 search runs, and 55.2% of memory-only pairs appeared in just 1 of 3 memory runs. Overlap (shared brands divided by all brands either list named) between memory and search for the same prompt was 63.7%, against 75.2% for a rerun on the same arm. Same #1 pick in 7 of 10 prompts. Search swaps about 2 brands in and 1.7 out, and about half those swaps do not repeat. ## The top pick stays the same 7 times in 10 We took each arm’s most frequent first recommendation across 3 runs. In 110 of 158 prompts the top pick was the same brand. The strict count (excluding prompts where either arm tied) was 104 of 147. Search flipped the top pick in 48 prompts. Memory reruns agreed on the #1 pick 82.7% of the time (392 of 474 pairs). Search reruns agreed 74.9% (355 of 474). Memory-vs-search pairs agreed 65.7% (934 of 1,422). [ChatGPT answers vary from run to run](https://organikpi.com/blog/geo-ai-search/chatgpt-citation-variance-study/), so some of the 48 flips would happen on a rerun within the same arm too. Search flipped the top pick in 48 of 158 prompts. Run counts show how consistent each pick was. Eight categories where search flipped the #1, with run counts: - **Project management:** Asana 3/3 without search, ClickUp 2/3 with search. - **Help desk:** Zendesk 2/3 without search, Freshdesk 2/3 with search. - **HR software:** BambooHR 3/3 without search, Rippling 3/3 with search. - **ERP:** SAP 3/3 without search, Microsoft Dynamics 365 2/3 with search. - **SEO tools:** Semrush 3/3 without search, Google Search Console 2/3 with search. - **Keyword research:** Ahrefs 3/3 without search, Semrush 2/3 with search. - **Landing page builder:** Framer 3/3 without search, Unbounce 2/3 with search. - **AI chatbot:** Intercom 2/3 without search, Tidio 3/3 with search. ## 60% of brand-prompt pairs appear in both arms Pool all three runs per arm and the 158 prompts produce 1,493 distinct brand-prompt pairs. Of those, 902 (60.4%) appeared in both the memory and search union. 321 (21.5%) were search-only and 270 (18.1%) were memory-only. 60% of brand-prompt pairs appeared in both arms. Search added 321 new pairs; memory held 270 that search dropped. The stable variant counts only brands that appeared in 2 or more runs of one arm and 0 of the other. Search reliably added 142 brand-prompt pairs and memory reliably held 121 that search dropped. The shared core per prompt averaged 5.7 brands. Search adds about 2 brands per prompt (mean: 2.03) and drops about 1.7 (mean: 1.71). The stable version is smaller: 0.90 added, 0.77 dropped. About half the swaps are one-run appearances. ## Reruns overlap more than arm switches Overlap on a rerun within the same arm was 75.2%. Switching from memory to search on the same prompt dropped it to 63.7%. The gap is 11.6 percentage points (95% CI: 9.8 to 13.3, p < 10-21). Reruns overlap 75.2% of brand-prompt pairs. Switching arms drops it to 63.7%. Search runs are not less stable than memory runs. The search rerun overlap was 74.2%, memory was 76.3%, and the difference was not significant (p = 0.39). Both arms repeat at about the same rate. MetricMemory (OFF)Search (ON)DifferenceRerun brand overlap76.3%74.2%-2.0 pp (p = 0.39)#1 same across all 3 runs118 of 158103 of 158Per-run #1 agreement82.7%74.9%Brands per answer (mean)5.976.13+0.16 (p = 0.013)Union size (brand-prompt pairs)1,1721,223Answer length (mean chars)3,2443,757158 buyer prompts, 3 runs per arm, ChatGPT, US, October 27, 2026. ## Memory favourites slip but rarely vanish We defined “memory favourites” as brands named in at least 2 of 3 memory runs with a mean position of 3 or better. There were 425 of them. Under search, 14 dropped out entirely. The rest slipped from an average position of 1.92 to 2.29. Of the 411 that stayed, 181 ranked lower, 92 ranked higher, and 138 held their spot. These brands were selected by their no-search position, so some regression toward the mean is expected. The pooled position shift across all 902 shared brand-prompt pairs was small: memory mean 3.62, search mean 3.74, a per-prompt shift of +0.12 (p = 0.00012). A memory favourite also appeared in the search answer on that prompt in all but 14 of 425 cases. This study did not measure how [ChatGPT rewrites queries before searching](https://organikpi.com/blog/geo-ai-search/chatgpt-query-fanout-study/). ## The gap by segment The overlap gap (within-arm minus cross-arm) by segment: - **Business services:** 14.1 pp gap (within 71.0%, cross 56.9%). - **Consumer products:** 12.9 pp gap (77.6%, 64.8%). - **B2B software:** 11.4 pp gap (75.0%, 63.6%). - **Developer tools:** 7.6 pp gap (77.0%, 69.4%). The differences between segments were not tested for significance. ## Overlap with the memory list was about the same whether or not the search answer showed citations 200 of 474 search answers showed [inline citations](https://organikpi.com/blog/geo-ai-search/ai-citation-accuracy-study/). 274 did not. On the 116 prompts where both types appeared, the overlap difference was 0.002 points (p = 0.91). Cited answers clustered by run: 35 in run 1, 83 in run 2, and 82 in run 3. 99.4% of search answers mentioned “2026” in the text, versus 8.4% of memory answers. Search answers averaged 3,757 characters, memory 3,244. ## Default ChatGPT sits slightly closer to the search arm We joined the October data with the [September 2026 ChatGPT answers](https://organikpi.com/blog/geo-ai-search/ai-engines-different-recommendations/) from the engine divergence study. Those September answers used default ChatGPT with no suffix. Default ChatGPT’s overlap with the search arm was 0.628 and with the memory arm was 0.593 (p = 0.0068). Both sit below the within-arm rerun overlap of 0.752. The September answers are a month apart, a different session, and possibly a different model version, so the gap is small relative to the noise. ## What this means for brands - **The no-search top pick held in 7 of 10 prompts.** 60% of recommended brand-prompt pairs came from both arms. Measure your [brand entity coverage](https://organikpi.com/blog/distribution/brand-entity-optimization/) with search off as the baseline. - **About half of search’s additions do not repeat.** 55.8% of search-only brand-prompt pairs appeared in just 1 of 3 runs. Check [visibility](https://organikpi.com/blog/seo-strategy/ai-brand-visibility-tracking-metrics/) across several runs and with search on and off, because [language models pick brands](https://organikpi.com/blog/geo-ai-search/llm-seo/) differently in each mode. Track [share of voice per engine](https://organikpi.com/blog/seo-strategy/ai-search-brand-share-of-voice/) over time. - **A [manual visibility audit](https://organikpi.com/blog/seo-strategy/diy-ai-brand-visibility-audit/) with and without a search instruction** shows whether your brand shows up in both conditions or just one. - **The gap varies by category.** Business services showed a 14.1 pp gap, developer tools 7.6 pp. Compare your category with [the pages AI Overviews cite](https://organikpi.com/blog/geo-ai-search/ai-overview-citations-best-x-queries/) for the same buyer prompts. ## How the study was run The 158 prompts are plain buyer questions (“best crm software”, “best running shoes”). They cover 60 B2B software, 28 developer tools, 40 consumer products, and 30 business services. Each prompt was sent to ChatGPT’s web interface, US, on October 27, 2026, three times with a memory suffix (“Answer from what you already know. Do not search the web.”) and three times with a search suffix (“Search the web before answering.”). A second extraction of 40 answers matched the first at 0.991 overlap (38 of 40 identical). Overlap is the number of brands two answers share, divided by all the brands either one named. The September comparison uses the same 158 prompts from the [engine divergence study](https://organikpi.com/blog/geo-ai-search/ai-engines-different-recommendations/), where ChatGPT ran in default mode in September 2026. Limits: the arms are prompt-instructed and analysed as assigned. 200 of 474 search answers showed inline citations. Data is from one day, US only, and the consumer ChatGPT model is unlabelled. ## Frequently Asked Questions ### Does ChatGPT recommend different brands when it searches the web? Partly. The #1 recommendation stayed the same in 110 of 158 buyer prompts. Search added about 2 brands per prompt and dropped about 1.7, but 60.4% of all recommended brand-prompt pairs appeared in both conditions. ### How much does web search change ChatGPT recommendations? A rerun on the same arm had 75.2% overlap. Switching from memory to search dropped that to 63.7%, a gap of 11.6 percentage points. Memory reruns agreed on the #1 pick 82.7% of the time, search reruns 74.9%, and cross-arm pairs 65.7%. ### Which categories change the most when ChatGPT searches the web? Business services had a 14.1 percentage-point gap between rerun and cross-arm overlap. Developer tools had 7.6 pp. Consumer products and B2B software sat in between. The differences between segments were not tested for significance. ### Do search answers with citations recommend different brands than those without? In this study, no observable difference. On the 116 prompts where both types of search answer appeared, the overlap difference was 0.002 points (p = 0.91). Cited answers clustered by run: 35 in run 1, 83 in run 2, and 82 in run 3. ### Can I download the data behind this study? Yes. The dataset has 5,734 rows, one per brand per answer, in CSV and JSON. It is free under CC BY 4.0 on GitHub alongside other AI search datasets from OrganiKPI.