Why the model label matters
Different models can compress context, phrase claims, and display links differently. Mixing their results makes citation changes uninterpretable. Record a name when the interface reports one, and do not infer a version from writing style.
Model is not retrieval
Document retrieval and answer generation are different stages. A model may receive context from search, memory, a user file, or multiple sources. A model naming a page therefore does not prove it found or read that page during the current query.
Minimum log
Save the product, mode, displayed model name, settings, query, input documents, answer, and time. Mark hidden parts as unknown. Honest uncertainty is more useful than false precision and lets you repeat the experiment after a product update.
Verification
Use a control set with the same sources and run it in two modes. Compare facts, links, caveats, and omissions, not just wording. Repeat on another day: stability is a separate observation, not a property implied by a model name.
Limitation
A public interface may hide routing and update a model without changing its displayed name. In public research, answer model is therefore often a label for an observed mode, not a proven description of the internal system. When a provider hides routing, the same model label does not guarantee the same answer model across dates.
What this does not prove
- A public model label may hide routing, updates, and post-processing. An observation about the model therefore does not prove which version or chain formed the answer, and cannot attribute the outcome without preserved context.
Sources
- 1Overview of OpenAI crawlersOpenAI Developers · official · 11 Sept 2026
- 2Top ways to ensure your content performs well in Google's AI experiencesGoogle Search Central · official · 11 Sept 2026
Correction history
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