Imagine two users interact with the same LLM. One is told it is the cutting-edge flagship; the other, an older, weaker model. They walk away with markedly different ratings of its usefulness and intelligence, yet they used the *same model*. In a controlled study, 162 participants each used one of six LLMs from two families across three collaborative tasks, after first viewing a landing page that matched, overstated, or understated their model’s true capability. This pre-interaction *framing* persisted through use. Oversold users rated the model more favorably than Undersold users before interaction; afterward, both groups had updated toward reality, but the impression gap did not fully close. Framing also changed how users worked with the model (with Oversold users prompting more directively, Undersold users more collaboratively), but the quality of what they produced together depended only on the model’s true capability, not on what users were told. Participants’ change in model impressions after use, measured across two impression measures, was *not* predicted by task performance (𝛽 = -0.01 and 0.11, both n.s.), but by whether the model met users’ expectations (𝛽 = 0.47 and 0.50, both p < .001) and how confident they felt working with it (𝛽 = 0.47 and 0.36, both p < .001). After interaction, users are still rating the *pitch*, not the *product*: user-elicited LLM evaluations, including the preference data driving public leaderboards, measure expectation management at least as much as the model itself.