Fermisense published a case study on the 27th that’s been sitting near the top of Hacker News since: they took a 9-billion-parameter open model, Qwen3.5, trained it for about $500 in GPU time over three and a half days, and it beat every frontier model they tested — GPT-5.6 Sol, Gemini 3.1 Pro, Claude Opus 4.8, Claude Fable 5 — on a catalogue-review task. Not by a little. The best frontier configuration hit 76.9% of the achievable score. The trained 9B model hit 87.3%. Cost per 1,000 listings reviewed: about 50 cents for the specialist against $19 to $34 for the frontier options (AI Pricing Guru, Developers Digest). It’s their own benchmark, on their own task, not independently reproduced, so I’d hold the exact numbers loosely. But the shape of the result matches something I’ve been doing on a MacBook Air for months, at a much smaller scale, without ever writing it down as a strategy.

I run Ollama locally for what I’ve started calling the donkey work — the parts of a job that don’t need judgement, just volume. Reformatting a pile of text. Pulling structured fields out of something messy. Classifying a batch of anything into buckets I’ve already defined. None of that needs Opus or Codex sitting there thinking about it. What it needs is something cheap that will grind through five hundred of the same small decision without me paying frontier prices for each one.

The way it actually works day to day: I get Claude or Codex to write the instructions — the actual plan for what needs doing and how to check the work — and then hand execution down to the local model, which does the grinding while the expensive one stays free to think about the next problem. It’s slower. An M2 Air running a local model is never going to feel snappy next to an API call to a frontier lab’s cluster. But slower and correct, for free, beats fast and billed by the token when the task itself doesn’t require much intelligence to begin with.

Fermisense spent $500 and three and a half days proving that a small model, trained on the right narrow thing, can outright beat the frontier at its own game. I haven’t trained anything — I’m just routing. But the underlying bet is the same one: stop asking the most expensive model in the building to do work that doesn’t need it, and save that horsepower for the one decision in the pipeline that actually does.