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/Right-Performance-93]

Worth adding a budget dimension to the risk/testability rule everyone's using here: with Max 20x or Pro 20x, a weekly pool of roughly 500M-2B tokens (cache-inclusive, varies a lot by plan) means burning the top model on a task you could've verified cheaply isn't just wasted spend, it's quota you don't get back until the reset. I keep a running tally of which tasks actually needed the escalation in the post-mortem, and most of the "just in case" escalations turned out unnecessary once I checked the diff against tests.