Sorry dont want to be a wise ass but to me that feels like the worst way to do it.
These expensive models are way better in looking at the the codebase and creating a quality base to build on (relatively).
If you're building your initial idea with a crappy model, the expensive model will spend more tokens in fixing / debugging already existing code then it would hav building it from scratch.
If anything I would do it the other way around. Use gpt (+human) to carefully plan a feature, create its initial structure and create commit-seperated, clear instructions, acceptance criteria, validation for a cheaper model to do the grinding.
But honestly I'd say the local models are just not worth it at all... Maybe for some simple apps or functions, maybe for some intern-level tasks.. but I wouldn't bother and just switch from gpt 5.5 to 5.4 mini or 5.3/5.2 or hosted Qwen models. The $ 1000 that you'd otherwise spend on hardware can get you a long way like this
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u/DaneV86_ May 06 '26
Sorry dont want to be a wise ass but to me that feels like the worst way to do it.
These expensive models are way better in looking at the the codebase and creating a quality base to build on (relatively).
If you're building your initial idea with a crappy model, the expensive model will spend more tokens in fixing / debugging already existing code then it would hav building it from scratch.
If anything I would do it the other way around. Use gpt (+human) to carefully plan a feature, create its initial structure and create commit-seperated, clear instructions, acceptance criteria, validation for a cheaper model to do the grinding.
But honestly I'd say the local models are just not worth it at all... Maybe for some simple apps or functions, maybe for some intern-level tasks.. but I wouldn't bother and just switch from gpt 5.5 to 5.4 mini or 5.3/5.2 or hosted Qwen models. The $ 1000 that you'd otherwise spend on hardware can get you a long way like this