When a team picks an AI coding tool, the decision usually comes down to two things: the feature list and the benchmark scores. Claude Code and Codex both get compared on what they can do and how well they score on coding tests. That is fair, but it leaves out a quieter factor that often matters more once the novelty wears off, which is operational reliability. How does the tool behave on a real machine, day after day, across weeks of actual work?
There has been community discussion, traced to a public GitHub issue, about Codex writing heavily to disk during normal use. As of late June 2026 this is community-reported rather than confirmed, and we are not going to get into the internals of anyone else's tool or how to change them. What is worth pulling out is the principle: the way a tool behaves on your hardware is part of its cost, and it deserves a place in the decision.
Why reliability is a business cost, not a developer gripe
It is easy to file reliability under developer preference, the kind of thing engineers grumble about but managers ignore. That is a mistake. A tool that stalls, eats memory, or chews through hardware does not just annoy people. It costs money. Every interruption breaks a developer's focus, and deep focus is exactly what you are paying senior engineers for. Add up the lost time across a team and the flaky tool quietly becomes the expensive one, whatever its sticker price.
Hardware wear is part of the same picture. A tool that hammers a laptop's storage or runs the fans constantly shortens the life of machines you have paid for. None of this shows up on the software invoice, which is precisely why it gets missed when tools are compared on features alone.
Stability: does it hold up across long sessions, or does it slow and crash after a few hours?
Resource use: how much memory, processor, and disk activity does it generate while idle and under load?
Predictability: does it behave the same way each day, so developers can trust it?
Recovery: when something goes wrong, does it fail cleanly or take your work with it?
Support: when a problem appears, is there a clear path to getting it fixed?
What to actually test before you commit
Benchmarks are run in a lab. Your team works in the real world, so test there. Give each tool a two-week trial on the actual hardware your developers use, on the actual codebase they work in, and watch what happens. Keep an eye on resource use over a full day, count the interruptions, and ask the team how it felt to live with. If a developer's time is worth around $120 an hour, even one lost hour a day across a handful of engineers adds up to several thousand dollars a month. That number dwarfs the difference in licence fees, which is why reliability deserves to be measured, not assumed.
Write down what you see rather than trusting a gut feel at the end. A short daily note from each developer on what worked and what got in the way will tell you more about the real cost of a tool than any benchmark table. It also gives you something concrete to weigh when the trial ends and a decision has to be made.
Where Claude Code fits
We use Claude Code as a daily driver, and the reason is less about any single feature and more about it being a steady tool to work alongside. For an Australian team, a coding assistant that behaves predictably and stays out of the way is worth more over a year than one that scores a point higher on a benchmark but makes the machine fight back. That is the honest case for it: not that it wins every test, but that it is calm to live with.
To be fair to the alternatives, both tools improve quickly, and a reported issue today may be fixed next month. So the right approach is not to take a side based on a forum thread. It is to run your own trial, weigh capability and reliability together, and pick the tool that gives your team the lowest total cost across a year, including the hours and hardware that never appear on the bill.
If you want help choosing and rolling out an AI coding setup that holds up in daily use, we can run the comparison with your team. You can book a brainstorm and we will design a trial that measures what actually matters.
