The Technical Context
I love discussions like this not for the flame wars, but for the real signals from the field. The picture here is very down-to-earth: Codex and GPT are praised for debugging complex problems, especially when the model acts as an AI layer on top of the terminal—kubectl, DNS, logs, all that wonderful pain. But when it comes to agentic coding and long, iterative tasks, people seem to trust Claude more.
Three things caught my attention. First: Codex is called imperfect for long-running flows because it uses context inefficiently and can run several compaction cycles even on a simple plan. Second: its sub-agents have been improved, which is a good sign—OpenAI is clearly refining the architecture for more complex scenarios. Third: its price point looks more attractive than Anthropic's, and that's an argument for a team's budget, not just a chat debate.
On the other hand, the original discussion has an important clarification: some impressions are tied to personal feelings about GPT's “literalness.” One participant put it very accurately: GPT adheres better to instructions from `agents.md` and requires fewer workarounds with prompt injection hooks, but Claude seems to better grasp the nuances of a task. I've experienced this myself: one model executes instructions with discipline, while the other is better at catching subtleties. And those are not the same thing.
Another nuance concerns timing. I wouldn't treat the background noise about “GPT 5.4” as a confirmed fact in my conclusions. For March 2026, it’s more reliable to base decisions on user practices and available public comparisons of current models, rather than on vague naming from chat rooms. Otherwise, the solution's AI architecture starts being built on rumors, which is a poor foundation.
What This Changes for Business and Automation
If you translate this entire debate from developer-speak into business language, it becomes very simple. There is no single “best” model for the entire team. There's a stack of tasks: terminal debugging, agentic development, frontend, long workflows, internal assistants—and the winner might be different for each layer.
Here's how I'd break it down today. If I need AI automation around infrastructure, engineering support, and incident analysis, Codex/GPT looks like a viable option. This is especially true where disciplined adherence to instructions is critical and the feedback loop is set up correctly: the model receives command output, adjusts, and moves on.
But if the task is to live inside a large project for a long time, maintain the thread of a multi-step process, and not fall apart at every turn, Claude currently looks more reliable. That's why in developing AI solutions for teams, I increasingly see routing instead of an “either-or” choice. One engine goes into the debug pipeline, the other into complex agentic scenarios.
The losers here are those who try to implement artificial intelligence by thinking, “let’s take the top model and stick it everywhere.” It doesn't work like that. I've seen companies overpay for a powerful model where a cheaper tool for a narrow function would have sufficed, and vice versa—stifling a use case that needs a long context and careful planning with a budget model.
At Nahornyi AI Lab, we usually don't start with the question “which is cooler, Claude or Codex?” but with a route map: where do we need an agent, where an orchestrator, where strict instructions, where a human-in-the-loop. That's where a proper AI integration is born, not just a chatbot for the sake of having one.
My brief conclusion is this: Claude currently wins more trust in agentic coding, while Codex/GPT is a strong candidate for debugging, terminal scenarios, and more budget-friendly automation. It's not the model itself that wins, but how you build the feedback loop, context, and constraints around it.
This analysis was written by me, Vadim Nahornyi from Nahornyi AI Lab. I don't collect benchmarks for tweets—we build AI solutions for businesses by hand, test agents in real workflows, and see where they save time and where they just burn tokens.
If you'd like, I can help you calmly break down your use case: what to give to Claude, what to give to OpenAI, and how to implement AI automation without unnecessary magic and excessive bills. Write to me—let's discuss your project together.