Model Buzz Report

How to Pick an AI Model Without Burning Your Budget

A new model drops roughly every week, and almost none of the coverage answers the question you actually have: should I use this for what I’m doing right now, or keep using what I’ve got?

I learned to answer it the expensive way. I swapped an unfamiliar model in through OpenRouter because it was cheap, went to get coffee, and came back fifteen minutes later to a fifteen-dollar bill. It couldn’t handle tool calls, so it looped. That’s the whole lesson: cheap and broken is more expensive than expensive and correct. The price that matters is per finished task, not per token.

So I stopped trusting any single ranking and started cross-checking three signals. Volume on OpenRouter shows what people are trying, but free promos inflate it. Head-to-head votes on the Arena show what people prefer blind, from one prompt and a snap judgment. Lived experience on Reddit shows what broke after weeks of real use, loudly and late. Each one is wrong on its own. The answer is where they agree.

The other half is reading the leaderboard right. The top of the board is compressed into a rating spread smaller than the noise, while prices for those same models differ by ten times or more. The question is never “what’s the best model.” It’s “what’s the cheapest model still in striking distance of the best, for this kind of work.” That’s the centerpiece of The Cheapskate’s Guide to the Arena Leaderboard, the one to read if you only read one.

The method doesn’t change. The answers change every week. So a Claude Code skill runs the triangulation for me and writes it up, which is how this series started. Building a Cost-Saving Skill That Accidentally Became Its Own Newsletter tells that story.

Each Model Buzz Report applies the method to whatever happened that week. They’re listed newest first, because the newest one is the one you want.