GPT-5.3-Codex Was the First AI to Pass My Real-World Test

I have spent the last few months switching between AI coding tools: Claude Code CLI, Cursor, and Codex UI. Like most engineers, I want the best model available. But I also care about two practical things: speed and cost.

This was not a same-day, side-by-side benchmark. The Webpack 4 to webpack 5 migration in VegVisits had been sitting in the background as an impending task, blocking us from upgrading tooling more broadly. As I rotated tools in normal work, I used that migration as a recurring real-world task.

That migration sounds simple on paper. In practice, it is where build configs, plugin compatibility, loader behavior, and small legacy assumptions all collide. It is exactly the kind of task that reveals whether an AI can reason through a messy codebase or just produce confident-looking guesses.

The Attempts

I first tried Sonnet 4.5. It made progress, but I ended up in a rabbit hole: partial fixes, regressions, and too many loops.

At best, I could sometimes get either production to build or development to run, but only through overengineering and too many hacks. Whichever environment I managed to make work, the other one would break.

Then I tried Cursor Composer 1.5. Similar story. Good moments, but not enough end-to-end reliability for this migration.

Again, the result was usually one environment or the other, not both. If production built, development was unstable; if development ran, production failed. It was too fragile and too hacky to accept.

Finally, I ran the task with GPT-5.3-Codex.

That was the first time this long-pending upgrade actually crossed the finish line.

I did not run this same test on Sonnet 4.6 afterward. Once webpack was upgraded, there was no reason to repeat it, because I was not trying to benchmark models against each other.

Why This Task Matters

“Upgrade Webpack 4 to webpack 5” is a perfect example because it is not a toy problem. It requires:

• understanding build-system history
• adjusting multiple moving parts together
• catching edge cases instead of patching one error at a time
• getting to a stable, working result

For me, GPT-5.3-Codex was the first AI that passed that test fully.

The Bigger Lesson: Capability Is Not the Only Metric

Getting access to the latest and greatest model is exciting, and it absolutely matters. But model quality alone is not enough in day-to-day development.

We need balance across:

• output quality
• response speed
• overall cost efficiency

In real workflows, the “best” tool is the one that gets reliable outcomes quickly, without burning budget.

For me, one of the highest-value uses of AI is exactly this: avoiding and removing technical debt before it compounds. Closing these pending upgrades is not glamorous, but it unlocks everything that comes next.

Where I Landed

I am still tool-agnostic and still evaluating. But this experience changed my baseline: GPT-5.3-Codex proved itself on a migration that others could not close out cleanly in my environment.

That does not mean one tool wins forever. It means we should judge tools by real engineering outcomes, and when we do compare tools, include cost and speed as well.

For me, Webpack 4 to webpack 5 on VegVisits was a real technical debt blocker. GPT-5.3-Codex was the first one that helped me close it out cleanly.