A week of using Codex more than Claude

I've noticed that too with Sol (xhigh) it starts with pretty good architectural goals, but really get lost in the weeds with certification and validation. Like it won't trust native tools like (in my case packer, Ansible, gcloud) to fail reliably under possible error conditions, and insists on implementing custom verification.

It also doesn't have a clear idea of what the actual threat model is, and builds all kinds of extremely defensive systems to account for imagined hostile actors. I'm like "Dude, it's only our systems that are creating these SVGs, they're never going to be user supplied, so you don't need to write an entire validation and sanitation framework here."

It also seems to treat the desired initial state of something as a permanent invariant and designs elaborate tests to ensure that it remains that way. Then when you make one little change it has to go and update a ton of tests it created.

I've had to rip out a bunch of overengineered jank from several feature implementations, and in doing so I ended up having to create retrospective documents that warn against this kind of behavior that I'll have the model review whenever a plan begins to go sideways.

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When discussing Claude vs. Codex, etc. I find it necessary to make the distinction between the models and the harness.

Claude's models in my experience do a better job of inferring my intent, or to say it does a better job of giving me the result I imagined in my mind. A recent example was a UI prototype I was building for a desktop application. I had asked GPT's 5.6 Sol to update the open document in the prototype to better reflect the context of the feature I was designing, and 5.6 Sol took it very literally and had just added some text to the currently open document, not what I had in mind. I tried again with Claude Opus 5 and it added a completely new tab with a complete new document that, although imperfect, much better matched my expectations.

You could say this was a prompting skill issue, but seeing how many people are prompting their AI I believe the labs are incentivized to continue to improve their ability to infer intent.

When it comes to the desktop applications though, I find Claude Desktop's output to be incredibly verbose and full of jargon. I feel like it hits me with an entire essay and the UI doesn't have enough typographic hierarchy to make it easy to scan. ChatGPT Desktop is much better in this regard, I feel the output is concise, clear, and gives me just enough info to feel in the loop without being overwhelmed. Even though I have the setting on for technical language, it feels more understandable than Claude. I also feel that ChatGPT's desktop app has a better design and much more polish.

I do not really like how bloated both applications have become though. This weird segmentation of Chat, Work, and Code all just seems like it's pushing a technical limitation onto the user. The other day I opened a document in ChatGPT and asked it to do something, then it told me it could only do it in work "mode", so it then created an entirely new conversation with a reference to the previous conversation. It wasn't a completely new area of the UI either, it just added a "Work" badge to the new conversation in the list. Feels a bit unnecessary, like couldn't you just keep it all within the same conversation?

I did the same this week.

The speed is the first big contrast; I have a routine multi-step skill that I run several of per week. Opus 5 was routinely taking 2 hours to do it, while older Claude models took around 20 mins; Codex restored that speed.

Second is legibility. Somebody wrote in one of the related discussions yesterday that Claude's current linguistic contortions could legitimately be considered damaging to mental health, which doesn't seem (too) hyperbolic to me. Codex (Sol) isn't perfect but it's much more direct. And so far I haven't seen it display much of an attitude, vs Opus's infuriating passive aggressive sulky know it all personality.

I slightly prefer Anthropic to OpenAI as a company, but I will vote with my wallet and discontinue my max subscription unless Anthropic does some serious damage control within the next week or two.

“Extreme defensiveness” is a good characterization.

I wonder if it’s an artifact of OpenAI’s values or rl training approach.

Also, it prob does make it perform better just not more efficient.

Great for the OpenAI employee working on security scanning who doesn’t have to pay for their tokens.

Not so much for the dev building their web app who is trying maximize their subscription.

Like hiring an aerospace engineer to build you a shed.