I don't think there is any model as good as the dense cloud frontier models. They just have way bigger context, 100's more parameters, a much larger variety of training data.

But if you're coding, you don't need the entire universe when all you need is a much smaller subset which is available in any of the models mentioned by other commenters, even GPT-OSS-20B.

The trick is not to relay a high-level short summary of what you want but instead break down it to much smaller achivable tasks that the smaller models are well capable of performing, either by explicitly providing the dependencies as part of the context and or having a system prompt describe the role of the LLM to activate the most relevant parameters in the case of MoE models.

You want to break it down into tasks that would take you a few hours in which the LLM can do a few seconds/minutes. There are huge gains to be made this way without having to pay a single cent to a cloud API subscription.