Which coding tasks are worth the highest-capability model in your workflow?

[u/whereismy1]

I am trying to separate coding work that needs deep reasoning from work that mainly needs reliable execution. Designing a change across an unfamiliar codebase, diagnosing a subtle regression, and reviewing a risky patch seem worth a stronger model. Formatting, small translations, and clearly specified edits seem better suited to a faster path.

The decision is less obvious for medium-sized tasks: adding more context may be enough, but sometimes the task remains ambiguous even with all the relevant files included. Do you use a fixed escalation rule based on risk and testability, or decide case by case?

Which coding tasks do you consistently send to the most capable model?

I recently came across Flatkey while testing this kind of coding-task split. It is an OpenAI/Anthropic-compatible gateway that can be evaluated by changing the base URL while keeping the existing SDK and request format. That makes it possible to compare routine edits with a stronger path without rewriting the coding workflow. Actual savings depend on the model mix and current supply, so I would measure patch quality, tests, latency, retries, and total cost.

[u/TikiMagic]

I haven't figured it out yet. With Claude, I blow a zillion tokens with Opus or Fable and get pretty good results. Sometimes brilliant. Or hideous overthinking and overbuilding and Claudish glossolalia. You pays your money, you takes your chance.

So I step down to Sonnet for what I think are well-defined tasks and get janky brittle code, irrelevant questions, or just broken shit. And sometimes perfectly serviceable code.

Then I glance over at Haiku, but it still has gum in its hair and spit up on its shirt. But it seems so happy.

GPT Sol has been pretty good for me. But I don't have as much workflow and structure built up around GPT yet.