When to Use Claude Code and When to Use Codex

The two frontier coding agents right now, by a long shot, are Claude and Codex; however, I have noticed significant differences in when the two models are superior, and I have noticed real downsides to each of them.I've thus implemented a sort of internal classification system that I use every time I start a new task, where I decide whether I should be using Claude Code or Codex to perform the task.In this article, I want to discuss this internal classification system that I'm using, sharing my thoughts on when which model is superior, and help you get the most out of these coding agents by using them on the tasks they perform best at.This infographic highlights the main contents of this article. I'll discuss the pros and cons of Claude Code versus Codex and when you should use which model. Image by ChatGPT.Why change coding agent based on tasks?Around six months ago, it was a pretty easy decision on which coding agent you should be using. Anthropic, with their Opus series model, was just far superior in all coding tasks. Of course, you could be using other models such as Google's model for coding or Codex even at that time, but in my opinion there was a very significant difference in the performance of these models compared to Opus.However, the competitive landscape of coding agents has changed significantly in those six months, and I now believe that there are two frontier models, Codex and Claude Code, with several other interesting competitors very close by. For example, GLM 5.3 or Kimi K3, which are both excellent coding agents, though not quite at the performance of Claude Code or Codex.Thus I'll be focusing on the two frontier coding models for now, though I believe in a few months this might change and we might have another frontier coding agent. However, the learnings I'll discuss in this article are pretty generic, and it's about how to recognize when a coding agent performs better and in which situations a coding agent struggles. I'll discuss the different weaknesses and strengths of Claude Code and Codex and also discuss how you can discover these issues and help you choose the best coding agent for the task that you're working on both now and in the future, once the landscape of coding agents changes significantly.Strengths and weaknesses of Claude Code and CodexFirst, let's discuss the strengths and weaknesses of Claude Code and Codex. To keep it super simple, I would explain the different situations where you should use each model with the following sentence.Codex is far superior when working on a single specific difficult task that you want to drive to completion, while Claude Code is superior at orchestrating agents to quickly get through a bunch of smaller tasks.Now let me elaborate a bit on each point. I'll start with Codex. Overall I think I have a preference for the Codex coding agent currently, which is based on a few factors: one is that Claude Opus 5 is way too talkative, and I have to tell the model to be more concise in its responses multiple times per day, even though I have very strong points in my markdown files highlighting that the model should be concise.I don't have this issue at all with Codex. Codex is more straight to the point, and I also feel a tendency that Codex is more eager to just get work done instead of asking me questions all the time, while Claude leans more toward asking me questions and stopping without finishing all of the work. At least if I don't actively use the /goal command.So essentially, when driving a single, typically more difficult task, I have a strong preference for using Codex because it just has a better ability to get that stuff done correctly.You might think that Codex having this trait makes it the superior model in all coding tasks. However, unfortunately, I find that now I'm doing so many tasks in parallel because a lot of tasks that come in, typically through product feedback, are smaller quick fixes that you don't need a super smart model to complete.Naturally, I don't want to have to manually spin up separate agents for each such smaller task because I can have between 50 and 100 such tasks come in each day, and it would take a lot of effort from me personally to spin up all of those sessions myself.Thus, I do wanna have an orchestrator agent that orchestrates sub-agents to complete each of these smaller tasks individually. And this is where I notice Codex really struggles.Codex is impressively bad at orchestrating a lot of different agents to get a lot of different smaller tasks done. In general, if you just ask Codex to complete two tasks, especially if they're not very strongly related to each other, I find that Codex many times forgets about one of the tasks and doesn't complete it.This, of course, makes Codex a hopeless model when it comes to organizing a lot of smaller tasks and getting such tasks done. Thus, my high-level classification system works like the following.For each day I get a lot of smaller tasks in and I have a single Claude Code session where I organize all these smaller tasks and have Claude complete them with sub-agents. Then whenever I have bigger tasks coming in or bigger projects, I always spin up a single Codex session per such project or task and have that completed. Also I have a preference for using Claude Code when it comes to design tasks or implementing front end only changes (though these are almost always quick fixes, so I do them with Claude anyways)Now I do want to note that this might change very soon. OpenAI might come with some upgrades to their harness, or they might release a new model that is stronger at orchestrating tasks. And in this instance, if this is the case, I'll move over to Codex full time, basically.How to discover where a model excels and where it strugglesNow that I've discussed my preferences on Claude Code and Codex and when to use each model, I want to move on to a more general topic, which is how to discover where a model excels and where it struggles. To start off, I'll highlight how I discovered the issues I mentioned above with both Claude Code and Codex.On a high level, I think this topic is about sometimes paying attention to how your coding agents work and, when they do work, analyzing what they do, how they did it, and how long they took. To do this analysis, you can, of course, use a coding agent to, for example, look into metrics such as:Average time to dev for a single taskNumber of PR review roundsAnd many other metrics, of course. In general, you can also just follow your intuition and see when you feel like a task is taking longer than it should. For example, one strong thing I noticed is that when I was using Claude Code to fix single tasks, it had a strong tendency to always stop and ask me for stuff, even though I didn't want it to. And then whenever it asked me stuff, it included way too many words, and it made it very difficult for me to understand what the model actually wanted from me.Thus, I started testing Codex on the exact same tasks and noticed a stark difference. It was more able to just complete the task and make assumptions that were, for the most part, right, which basically made it more effective at completing the task for me. So, essentially, what I did to compare them is that I just ran the same task with both models, which, of course, costs some extra tokens, but it's worth it to find the optimal model for a task that you're working on, at least as something you can do every now and then.And now, on the other side, the way I discovered that Codex was bad at orchestrating smaller tasks and working on a lot of smaller tasks was that I would, in some instances, have Codex work on a single task, then I might ask for a small amendment to that task or to fix something kind of related to that task, but on the side, and I would notice that on a surprisingly common basis, Codex would just simply forget about doing one of the tasks, and I'd have to remind it about it. And in many cases, it forgot the task again. Of course, this is hopeless and sometimes difficult to detect because when I hand tasks off to agents, I expect them to remember the task, complete it, and ask me for approval before forgetting about it themselves.Thus, I started orchestrating such tasks with Claude Code instead and testing the exact same tasks, and I noticed it was much better at remembering outstanding work that it had to do and at orchestrating sub-agents to do a lot of smaller tasks.ConclusionIn this article, I discussed when to use Claude Code and when to use Codex for your coding. Currently, these are two frontier models in my opinion, though this might change significantly in the coming months, and especially exciting is that we have a lot of open-source models which are performing incredibly well and at a much lower price point than frontier models. I discussed the pros and cons of both Claude Code and Codex, and when I use each model with my internal classification system. I then started talking a bit more in general about how you can discover where a model excels and where it struggles. It all comes down to having a feeling for when the models are performing well and when they're being slow. Additionally, I use more quantitative measures by having a coding agent every now and then go through my metrics, such as average time to dev or how many review rounds to get code to dev. I believe you should run these tests regularly to make sure that your tech stack is optimized.👋 Get in Touch👉 My free eBook and Webinar:🚀 10x Your Engineering with LLMs (Free 3-Day Email Course)📚 Get my free Vision Language Models ebook💻 My webinar on Vision Language Models👉 Find me on socials:💌 Substack🔗 LinkedIn🐦 X / Twitter