We don't have a user-facing model picker in
@Basedash.
Tools that do (e.g. Cursor) are primarily optimized around a single persona: engineers. For that audience, model selection can make sense.
But we have a product that spans sales, ops, marketing, finance and, yes, engineering. In a cross-functional environment, a model picker introduces friction and doubt, without any benefit to the end user.
In the best cases, a model picker still has tradeoffs. One customer (a CTO) recently told us, half-jokingly, "I still have PTSD" from a specific model they were using in Cursor. Even AI-native users don't actually want to be the one accountable for picking the model behind every answer; they just want the answer to be right.
Scenarios like this are exactly what we want to avoid.
Selecting the right model for a workload is a design decision. You have to account for context windows. Reasoning quality. Speed. Latency. Cost. Failure modes. Plus a bunch of other stuff.
In our view, design decisions should be made by us, not by the end user. That's what we get paid for. We want our customers to have fewer things to think about in their day.
That's why we focus on routing to the best model per task: chart generation, chat response, and SQL writing each have different optimal models. We continuously benchmark against our own evals, and swap the moment something better ships.
The routing is invisible, but it is governed. Our evals catch regressions and our workflows preserve consistency.
Best of all? Customers get the benefit of model improvements, without having to manage the complexity themselves.