Choosing an AI coding setup for debugging, local work, and UI review

Long debugging sessions need more than a single clever answer. This checklist compares Claude and GPT-style assistants by context handling, patch discipline, test follow-through, and cost control before choosing one for a multi-hour bug hunt.

## Start with the failure shape
If the problem is a small syntax error, most strong models are enough. If the problem includes logs, failing tests, framework conventions, and several files, compare how well the assistant keeps the reproduction path intact across turns.

## Patch discipline matters
For debugging, the best model is not the one that suggests the most fixes. It is the one that changes the smallest useful surface, explains why the change addresses the failure, and asks for fresh evidence when the error changes.

## Cost and interruption risk
A long session can waste money if the model repeatedly rereads the same files or resets the investigation. Track how many turns it takes to reach a testable patch, not only the price per message. A cheaper model can become expensive if it loops.

## Practical comparison
Use the same bug report, same failing command, and same file set. Ask each tool to identify the next smallest check. The winner is the one that preserves context, produces a verifiable patch, and stops when the evidence is insufficient.

Claude vs GPT for long debugging sessions: what to compare first

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