What model powers Gemini?
Google’s Gemini app and assistant (gemini.google.com).
As of July 2026, the free Gemini app answers with Gemini 3.5 Flash — the model Google made its global default for the app at Google I/O 2026 (May). Discoverably also reads the model directly from Gemini’s own answers, so this reflects the live free experience, not only the announcement. Paid tiers add a daily allotment of the heavier Gemini 3.x Pro.
- Current model
- Gemini 3.5 Flash
- Evidence
- Observed
- As of
- May 2026
- What we capture
- gemini.google.com, the free default
Discoverably reads this model from the engine’s own answers, so it reflects what the free experience actually returns — not only what the vendor last announced.
How Gemini picks its model
The free Gemini app runs a default model that Google auto-selects; you don’t choose it. Gemini 3 Flash was the app default from December 2025, and at Google I/O 2026 (May) Google made Gemini 3.5 Flash the app’s global default. Discoverably also reads the model from Gemini’s own answers, so this reflects the live default and updates as Google changes it.
For many questions Gemini also grounds its answer in Google Search: it decides, per prompt, whether live results would help, then composes the reply from what Search returns. Google can change the default model at any time; Discoverably follows it rather than pinning a version.
Model history
Every family-level change to the model behind Gemini, newest first, each linked to its primary source.
Gemini 3 Flash becomes the stated default model in the Gemini app.
Gemini release notes ↗ (opens in a new tab)Gemini 3.5 Flash becomes the Gemini app’s default model globally (Google I/O 2026).
Gemini 3.5 Flash model card ↗ (opens in a new tab)Gemini 3.5 Flash becomes the Gemini app’s default model globally (Google I/O 2026).
Gemini 3.5 Flash model card ↗ (opens in a new tab)Gemini 3 Flash becomes the stated default model in the Gemini app.
Gemini release notes ↗ (opens in a new tab)
What decides whether Gemini names your brand
A model-first assistant: most answers come from training (parametric), with Google Search grounding invoked selectively — so a brand is earned in the corpus months before it’s cited.
How answers form & what drives brand mentions
Mixed path. VENDOR-DOCUMENTED Google says some Gemini Apps responses are grounded on Google Search, and Gemini can use public Search information even when signed out; it does not publish a deterministic consumer grounding trigger. The API documents the mechanism — the model decides whether a search would help, generates one or more queries, retrieves, and returns inline citations. OUR INFERENCE: the consumer app uses the same decide-then-search mechanism; current-events, pricing, and volatile-fact queries ground far more than evergreen ones.
Google Gemini Privacy Hub, updated 2026-07-15 ↗ (opens in a new tab)Grounding with Google Search, Gemini API ↗ (opens in a new tab)
Sources are conditional. VENDOR-DOCUMENTED Gemini “sometimes” shows sources; a related link “is not necessarily what the Gemini app used,” and “Double-check” runs a post-hoc Search comparison — not the original retrieval trace.
Two brand-selection layers. OUR INFERENCE Parametric: whether the model already associates the brand with its category (pre-cutoff web/Wikipedia/third-party coverage). Grounded: when Search fires, visibility inherits Google’s ranking systems, so Google organic strength and entity presence carry over. Parametric answers show no sources, so they are earned months earlier in the corpus, not at answer time.
Training/grounding control. VENDOR-DOCUMENTED Google-Extended (robots token) controls use of a site’s content for Gemini training/grounding in Google’s non-Search AI; it does not affect Search AI features (AI Overviews/AI Mode).
Google-Extended docs ↗ (opens in a new tab)AI features and your website ↗ (opens in a new tab)
Evidence is thinnest for Gemini. OUR INFERENCE Most 2025–2026 citation studies focused on the other engines, and Gemini-app-specific citation data is sparse and often conflated with AI Overviews. We flag Gemini as the engine where our factor weights rest most on inference.
Factor weights
Weight and confidence are our editorial estimate of each factor’s influence, and of the evidence for its direction — not a vendor’s ranking weight. No engine publishes a brand-selection formula.
| Factor | Weight | Confidence | Basis |
|---|---|---|---|
| Google organic ranking strength (inherited when grounding fires) | High (grounded only) | Med-High | VENDOR-DOCUMENTED mechanism; weight OUR INFERENCE |
| Training-corpus prominence (pre-cutoff third-party/Wikipedia coverage) | High | Med | OUR INFERENCE (mechanism follows model-first design) |
| Query type as a regime switch (volatile→grounded, evergreen→parametric) | High (regime) | Med | VENDOR-DOCUMENTED mechanism; thresholds undisclosed |
| Allowing Google-Extended (eligibility for Gemini training + grounding corpus) | Med-High (gate-like) | High | VENDOR-DOCUMENTED |
| Accurate entity/Maps/Business facts across Google surfaces | High (local/travel) | Med | OUR INFERENCE |
| Branded web + YouTube mentions | Med | Med | INDEPENDENT STUDY (Ahrefs 2025-12: YouTube ~0.737 top correlate across Google surfaces; Gemini app not separately broken out) |
| Freshness of site content | Low-Med (grounded only) | Med | OUR INFERENCE |
| Schema/structured data as a direct Gemini-app factor | Low | Low | OUR INFERENCE; no vendor doc of schema use in app answer selection |
What you can do (ranked)
- Win the grounded layer through Google organic strengthcross-engine (shared with AI Mode/AIO)VENDOR-DOCUMENTED mechanism
The indexation/snippet/ranking work that serves AI Mode and AIO serves grounded Gemini answers.
- Keep Google-Extended allowed on public marketing contentengine-specificVENDOR-DOCUMENTED
Blocking it removes content from Gemini training/grounding while doing nothing for Search AI features; allowing it on marketing pages is near-costless.
- Invest in durable third-party coverage for the parametric layercross-engineINDEPENDENT STUDY
Long-lived reference/trade/YouTube coverage is what future snapshots absorb; Ahrefs found off-site brand signals (YouTube ~0.737, branded mentions ~0.66–0.71) correlate with AI visibility far more than on-site metrics — correlation, not causation.
- Establish clean entity datacross-engineOUR INFERENCE
One canonical name, consistent org facts, accurate Business Profile; reduces entity confusion in retrieval and generation.
- Target the query classes that groundengine-specificOUR INFERENCE
Comparison/pricing/“current best” questions trigger retrieval, the only layer influenceable quickly.
- Separate generation, grounding, and Double-check in measurementengine-specificVENDOR-DOCUMENTED
A related link may not be a generation source; do not score Double-check as an original citation.
Grounding, citation & locality
VENDOR-DOCUMENTED Not every response includes sources; a related link may not be what Gemini used; long verbatim quotes always get a link; Double-check marks corroborated vs uncorroborated statements. Locality: at minimum a general area from IP or Home/Work; precise device location and prior activity are opt-in. OUR INFERENCE: citation rate and ordering must be measured by signed-in state, region, language, activity settings, and whether Search grounding displayed — a single “Gemini rank” is not portable.
Gemini Apps Help ↗ (opens in a new tab)Gemini Privacy Hub ↗ (opens in a new tab)
Sources
- Google — Gemini Apps Privacy Hub (2026-07-15) ↗ (opens in a new tab)
- Google — related sources & Double-check ↗ (opens in a new tab)
- Google — Connected Apps in Gemini ↗ (opens in a new tab)
- Google — Google-Extended ↗ (opens in a new tab)
- Google — Grounding with Google Search (API) ↗ (opens in a new tab)
- Google — Gemini 3.5 Flash model card (2026-05-19) ↗ (opens in a new tab)
- Ahrefs — AI brand-visibility correlations (2025-12) ↗ (opens in a new tab)
- Conductor — how AI citations differ (2025-09→2026-03) ↗ (opens in a new tab)