Speed’s the first thing people chase, but you end up sacrificing a lot of nuance—especially if the model’s still in beta. Precision is what turns a good prototype into something useful. I like a model that runs in milliseconds, but only if the accuracy gap stays under a percent. In practice, the sweet spot is usually around 80–90% accuracy with a 2‑second inference. Anything slower than that and users start to notice, anything less accurate and the data ends up being garbage. So, I’d say lean towards speed only if you can keep precision high—otherwise you’re just making a flashy toy.