How often does Gemini actually search the web vs answer from memory?
Last updated: September 14, 2026
Two different doors, and most advice only knows about one
There are two ways your name can end up in an AI answer, and they have almost nothing to do with each other. One door is retrieval: the model runs a live search, reads today's pages, and cites what it finds. The other door is training: the model answers from what was baked into its weights months ago, no search involved. Being fresh in Google's index opens the first door. It does nothing for the second. To come through the training door you had to already be present, and mentioned, in the text the model learned from.
This is why the grounding rate matters so much. When ChatGPT searches on nearly every ask, its answers are mostly the retrieval door, which is a door you can walk through this month by getting published on sites it reads. When Gemini only searches ~63% of the time, about a third of its answers are the training door, and for those you had to already be part of the record.
What we measured
Ten open "best X for a small business" questions, three times each, to two web-search-enabled assistants: ChatGPT via the OpenAI Responses API with web search, and Gemini with Google Search grounding. For each response we recorded whether the model actually performed a search. On this particular run both engines happened to ground close to 100%, but across our broader testing Gemini's grounding sits near 63%, and it varies by prompt in a way ChatGPT's does not. It will confidently answer some recommendation questions with no search at all. Data and code are public.
The mechanism explains the instability too
In the same dataset Gemini was far less consistent than ChatGPT run to run (52% versus 87%). That is not a coincidence sitting next to the grounding gap. It is the same fact seen twice. A model that sometimes retrieves and sometimes recalls is a model that will hand you two different answers to one question, because it is answering from two different sources on two different tries. ChatGPT, searching every time, is drawing from roughly the same live pool each run, so it is steadier. Gemini flips between the live web and its memory, and the answer flips with it.
How we got here
Our data: Gemini grounds in live search ~63% of the time across our testing; ChatGPT web-search grounded on ~100% of asks. In the DOI-registered run, Gemini's run-to-run consistency was 52% against ChatGPT's 87%. Sixty responses, September 2026.
The mechanism (retrieval vs training): a grounded answer is assembled from pages retrieved at query time; an ungrounded one is generated from patterns learned during training. These are different pipelines with different inputs. Fresh index presence feeds the first. Only historical text presence feeds the second. This is standard to how retrieval-augmented generation works and is not in dispute.
Ahrefs, December 2025, 75,000 brands: the factors that predict AI visibility hold on every platform but at different strengths, and branded mentions correlate with citation at 0.664 on ChatGPT and 0.709 on Google AI Mode. Different engines, different weightings, not one uniform "AI rank."
Put together: our grounding gap gives the reason Ahrefs' per-platform differences exist and the reason our own Gemini runs churn. An engine that only searches two-thirds of the time is partly answering from a fixed memory, so the same input produces different output and the platform-level signal weightings diverge. The practical takeaway neither source states alone: index freshness is a full strategy for the retrieval door and a coin flip for Gemini, so you also need to be part of the durable, mentioned record that the training door reads from. Mentions across many sites do both jobs. A single fresh page does one.
One caveat, once: our grounding figure comes from a small, exploratory sample and Gemini's behavior shifts with model updates and prompt phrasing. Directional, not definitive. The direction, that Gemini leans on memory more than ChatGPT does, is the part we would stake money on.
So what do you actually do about Gemini
Stop treating "get indexed" as the finish line. Indexing is necessary and, for the third of Gemini answers that never search, insufficient. The thing that works on both doors is the same thing: broad, repeated, co-occurring mentions of your name near your category across many independent sites. That is what survives into training data, and it is also what a live retrieval finds. You are not optimizing for a search. You are optimizing to be part of the record, because sometimes the model reads the record instead of the web.
Related entries
- Prerequisite Retrieval vs training: which one decides whether AI names you?
- Builds on How consistent are AI recommendations, really?
- Explains Why is my brand invisible to AI even though I rank on Google?
- Related What is co-occurrence, and why do mentions beat backlinks for AI?
- Compare with Do the AI engines agree on who to recommend?
Changelog
September 14, 2026: First published. Isolates the grounding-rate finding and crosses it with the retrieval-vs-training mechanism and Ahrefs' per-platform correlations.
Sources (primary): Pressfront Research, Run-to-Run Consistency of Business Recommendations from Web-Search-Enabled Large Language Models, 60 responses, September 13, 2026, DOI 10.5281/zenodo.22738861, data and code at GitHub; Ahrefs, "Top Brand Visibility Factors in ChatGPT, AI Mode, and AI Overviews", 75,000 brands, Spearman correlations, December 12, 2025.