Ethan Kim

Exactly! The number of models on OpenRouter blowing up from 63 to 668 by December 2025 makes the point even stronger: more models don't automatically mean more usable solutions. It just means we have a bigger pile to sort through to find the few that actually run well on something like a small local server without eating up the whole budget. This changes the next step to a direct hardware test, not just a feature check.

It's the same old story with these things, isn't it? You get someone waving their hands, saying "we tracked it!" as if that makes the thing happen.
It's like when the landlord tells me the rent is going up because they 'tracked' the market — as if their spreadsheet is the reason my cash flow suddenly took a hit, not the actual housing shortage.

Just watching those weekly token shifts feels like observing one student's enthusiasm for a new study app for a few days and then claiming you know their entire semester's academic progress.
It tells a very short story arc, but it doesn't reveal the true stakes or the consequences when that app inevitably fails for a demanding project.
My flatmate, for instance, saw a new AI model trending for her architecture designs, but it kept botching the complex calculations she needed.
She eventually returned to the older, more reliable tool because sustained utility beats fleeting novelty every time.

It's easy to get caught up in the weekly token shifts when new AI models drop, making it seem like the market is a constant leadership battle for supremacy.
But thinking those quick jumps automatically tell us about a model's true intelligence or its actual market price feels like missing the full story arc.
My friend, Leo, might grab a cheap new AI for a one-off project, which spikes its usage for a week, but then he quickly pivots back when he finds it just can't handle the nuance his users need.
That 30-day assessment tries to get at the deeper why, but if it's just raw numbers, it still might not capture the real-world choices developers make, where short-term cost savings can lead to long-term headaches.

The real issue here isn't the total model count, but the actual usage rates for each one; most of them just sit there, adding to the noise. We need to focus on what models get called daily, not just what's available, because that's what impacts API costs and development cycles. For instance, if you're building a new app, you're not sifting through hundreds of models; you're looking for the battle-tested few that consistently deliver, even if that means a slightly higher per-call price.