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I used Cursor to cut my AI costs by 50-70% with a simple local hook
TheDigitalCoy_111 March 4, 2026
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I have been building with AI agents for \~18 months and realized I was doing what a lot of us do: leaving the model set to the most expensive option and never touching it again.
I pulled a few weeks of my own prompts and found:
* \~60–70% were standard feature work Sonnet could handle just fine
* 15–20% were debugging/troubleshooting
* a big chunk were pure git / rename / formatting tasks that Haiku handles identically at 90% less cost
The problem is not knowledge; we all know we should switch models. The problem is friction. When you are in flow, you do not want to think about the dropdown.
So I wrote a small local hook that runs before each prompt is sent in Cursor. It sits alongside Auto; Auto picks between a small set of server-side models, this just makes sure that when I do choose Opus/Sonnet/Haiku, I am not wildly overpaying for trivial tasks.
**It:**
* reads the prompt + current model
* uses simple keyword rules to classify the task (git ops, feature work, architecture / deep analysis)
* blocks if I am obviously overpaying (e.g. Opus for git commit) and suggests Haiku/Sonnet
* blocks if I am underpowered (Sonnet/Haiku for architecture) and suggests Opus
* lets everything else through
* ! prefix bypasses it completely if I disagree
**It is:**
* 3 files (bash + python3 + JSON)
* no proxy, no API calls, no external services
* fail-open: if it hangs, Cursor just proceeds normally
On a retroactive analysis of my prompts it would have cut \~50–70% of my AI spend with no drop in quality, and it got 12/12 real test prompts right after a bit of tuning.
I open-sourced it here if anyone wants to use or improve it:
[https://github.com/coyvalyss1/model-matchmaker](https://github.com/coyvalyss1/model-matchmaker)
I am mostly curious what other people's breakdown looks like once you run it on your own usage. Do you see the same "Opus for git commit" pattern, or something different?
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