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/dvduval]

Almost all my work involves databases and repose and customer questions and coordinating these three things often through a longer conversation.

We might be creating a new feature and we need to keep that conversation going for a while and it might go out with another thing we’re building and we have to coordinate that with a different feature.

I just find using extra high reasoning is simply more accurate. I know when I’m answering the customer or pushing out the feature that a lot of thought went into it and there’s very little slop.