I'd be interested to see how these models perform when they're given only parts of the spec, not the entire thing. That would be much reflective of how I use Kilo Code for cheap, high-quality outputs: expensive planner/orchestrator, cheap implementer.
"If you can absorb imperfect output, the math changes."
This is the takeaway I've been saying for months. "Good enough" is usually better than "perfect" - especially if all you're trying to save is a little time. And saving time is a management problem, not a technical issue.
In other words, does a nice cleanup pass which takes a little more time worth going broke?
Nice post! Thank you. I came to the same conclusion while working with backend tasks: DeepSeek V4 Pro > Kimi K2.6 > V4 Flash. Opus 4.7 might yields better output quality but the pricing is insanely high and it's quite lazy (sometimes refuses to implement)
Thanks for this comparative deep dive, it would be nice if we could also see gpt 5.5 medium comparison in the same way.
I'd be interested to see how these models perform when they're given only parts of the spec, not the entire thing. That would be much reflective of how I use Kilo Code for cheap, high-quality outputs: expensive planner/orchestrator, cheap implementer.
"If you can absorb imperfect output, the math changes."
This is the takeaway I've been saying for months. "Good enough" is usually better than "perfect" - especially if all you're trying to save is a little time. And saving time is a management problem, not a technical issue.
In other words, does a nice cleanup pass which takes a little more time worth going broke?
Nice post! Thank you. I came to the same conclusion while working with backend tasks: DeepSeek V4 Pro > Kimi K2.6 > V4 Flash. Opus 4.7 might yields better output quality but the pricing is insanely high and it's quite lazy (sometimes refuses to implement)