I have had good luck rotating between the three tiers of GPT 5.6 with occasional jumps up to Astra or Fable. Most of my work is with GPT-5.6-Sol. Simple tasks like data extraction or trivial refactors (rename this variable etc) I often push down to Terra or Luna, or even to self-hosted Gemma4:31B. Very tricky stuff, like planning a new feature, design review, or code review of a complex change across multiple repositories is where I leverage Astra.

I've had middling success with models like DS V4.1 Flash and free Gemini. They tend to be pretty good at very easy stuff ... but they're more likely to go down rabbit holes, confidently assert falsehoods, or fix bugs with changes to my test harness rather than my code.

I asked about comparison to the well-known SotA models specifically because I use either Astra or Sol for ~85% of my daily tasks. When I try to use smaller/cheaper models, I have had very mixed success. Sometimes it's perfect, while other times it fails in subtle and hard to catch ways.