its not always that simple. dropping in a new model is trivial, but highly specific workflows may rely on specific _invisible_ aspects of a model. when that model gets deprecated, the workflow needs to be rebuilt/re-tuned to work with a different model.

google's inability or unwillingness to provide stable timelines for model deprecation makes it risky to build complex workflows using their models

You would be surprised how much of a difference the model makes for certain niche tasks.

For my use case, `gemini-3.1-flash-lite` is ~20% higher accuracy than the next best model of comparable cost (considering both proprietary and open-weight alternatives)

"Intelligence" being what, math? Coding? Unfortunately there's a billion use cases for LLMs whose performance is not at all captured by the popular benchmarks they're all trying to maxx.