Ecosia’s updated AI Search documentation describes several paths through the same search service: conventional results, generated overviews and conversational answers. For SEO teams, the significance is that the query alone no longer fully describes the experience being measured. Routing, mode selection and user preferences can determine which surface presents a source.
What the official documentation confirms
The Help Center page updated October 2, 2026 identifies Mistral Small 4 as the model for AI Chat and Mistral Small 3.2 for AI Overviews. Overviews summarize search information and link sources above the results. AI Chat includes Web Source Mode, which instructs it to use the live web for all queries, alongside Guided Learning and Think Longer.
Ecosia also documents automatic routing of some complex questions from standard results to AI Chat. Its desktop AI-free setting disables Overviews and AI routing, removes Chat from navigation and autocomplete, and restores the traditional search box. Mobile support is described as future work. AI Chat remains a beta available to some users, so the documentation should not be read as proof that every user receives identical features.
The page’s update date establishes when this description was revised. It does not establish that every listed feature launched together that day. Nor does a model name reveal the full process through which a source is retrieved, selected and presented.
Visibility tests need to record the route
Our editorial interpretation is that Ecosia should be tested as several distinct experiences rather than reduced to one generic “AI search” score. A useful protocol would repeat a controlled set of queries in conventional results, Overviews where available and Chat, with Web Source Mode recorded explicitly. Include an AI-free desktop session as a separate condition rather than blending it into an average.
Capture the date, device, region, account state, enabled settings and whether routing occurred. Then preserve the actual result or answer before classifying brand mentions and source links. Those details help distinguish a genuine change in exposure from a change in the interface used to observe it.
For example, a site appearing in ordinary results may not appear among the sources in an answer. Conversely, an answer may name a business without linking its site. Treat result position, citation presence and accurate representation as separate observations. None alone tells you how many people clicked or whether the business gained a qualified visit.
Live-web sourcing does not establish freshness by itself
Web Source Mode makes the retrieval condition particularly worth testing. Using a live web source does not guarantee that the underlying page is current, that the newest information was selected or that the answer preserves an important qualification. Publishers can make dates, evidence and limitations explicit; measurement must still establish what the assistant actually used.
The AI-free option creates another reason to retain conventional search analysis alongside GEO monitoring. Teams should avoid interpreting a change in chatbot exposure as a complete account of discoverability on Ecosia. The relevant question is which experiences their audience uses, and what each one shows.
That approach fits NetContentSEO’s research-entity recognition experiment, which records observations over time without treating one successful answer as a solved visibility problem. Ecosia’s documentation gives researchers additional conditions to record. It does not supply citation-selection rules or traffic-impact measurements. The practical opportunity is a better controlled audit of where sources appear, how they are described and whether those observations repeat.