title Codex VS Claude date 2026-04-15 description The developer's dream or nightmare?

Codex vs Claude

OpenAI's Codex and Anthropic's Claude have been going head to head for a few months now in their really successful attempts to grab the attention of developers worldwide. These tools are extensively used to speed up production in prototyping, developing and shipping of products. Do note that this comparison is focused on the AI models more than the UI and how to use these tools.

Today, we find out the real differences between these 2 tools and conclude which might be the most useful for you!

Comparison

Let's start by comparing the tools itself. Codex is available on web, the Windows or Mac app, the IDE extention or the CLI. Claude is availible on the web, the app, CLaude Code or the CLI. Codex uses OpenAI's ChatGPT models while Claude uses Anthropic's Claude models. I will break down the different models into more detail further on. Now that we have the basics pretty much covered, we can move on to the actual comparison

Codex

So what is Codex? Well, OpenAI Codex is an artificial intelligence coding agent developed by OpenAI that helps developers write, understand, and manage code. Instead of just suggesting snippets, Codex can perform full software engineering tasks such as building features, fixing bugs, running tests, and even proposing code changes. It works by understanding natural language instructions (like “create a login system”) and turning them into working code across multiple programming languages. Unlike traditional coding tools, Codex can operate more like an autonomous assistant, completing tasks in the background and handling entire workflows, making software development faster and more efficient.

Models used

As you can probably infer by now, Codex uses proprietary models. Models used are:

1. GPT-5.4
2. GPT-5.4-mini
3. GPT-5.3-Codex
4. GPT-5.2

What these models are actually good at

While these models are extremely powerful, what really sets Codex apart is how well it performs end-to-end software engineering tasks, not just code generation. Instead of only suggesting snippets, Codex can handle entire workflows such as writing features, fixing bugs, generating tests, and even proposing complete changes to a codebase.

One of its biggest strengths is reliability in execution. When given a task, Codex does not just stop at generating code — it can iterate, test, and refine its output until it works properly. In more advanced setups, it can even simulate a full developer workflow: identifying a bug, reproducing it, fixing it, validating the fix, and pushing changes.

Another key advantage is its ability to work across entire codebases, not just single files. Codex can analyse large projects, understand how different components interact (frontend, backend, database), and make coordinated changes across multiple files. This makes it especially useful for real-world applications, where most problems are not isolated to one function.

In terms of flexibility, Codex supports both frontend and backend development. It can generate UI components, APIs, and even connect them together in a full-stack workflow. However, while it is capable of frontend work, it generally performs best in structured, logic-heavy tasks such as backend systems, APIs, and data handling.

Another important factor is efficiency. Codex models are designed to balance performance with token usage, allowing developers to run multiple tasks without excessive cost. This makes it more practical for long-term development compared to models that prioritise heavy reasoning at the expense of efficiency.

Overall, Codex’s models are best described as execution-focused. They are designed to take a clear instruction and carry it through to completion in a reliable and structured way. While they may not always produce the most creative or visually polished results, they excel at ensuring that things actually work — which is often the most important part of building real software.

Claude

If you have search for vube-coding tutorials or AI coding before, you have definitely heard of Claude. Claude is an extremely powerful tool when it comes to reasoning and building apps with just 1 prompt. In fact, Anthropic just passed OpenAI in annualized revenue which is pretty big considering OpenAI was one of the pioneer AI companies. Claude is pretty overpowered. Let's take a look at the different models used.

Models used

1. Opus 4.6
2. Sonnet 4.6
3. Haiku 4.5

Of the lot, Opus 4.6 is the best model. However, you will need a Claude subscription to use it. Both Sonnet and Opus are extremely good when it comes to coding. It handles deep and complex reasoning well and can make really good decisions on its own. This is why it is considered the best AI for vibecoding. It is also excellent for agentic purposes like being a personal assistant with the Claude SDK but that's a story for another day.

However, when there are pros, there are also cons. These models are extremely inefficient when it comes to credit/token usage. Even with a subscription, you will run out of credits/tokens really quickly. Even with a Pro subscription, you will run out of credits basically 1 session in. What's worse is that unlike Codex, it stops everything it is doing the moment you run out. Codex still completes the task before telling you you are out of credits.

That's where Haiku 4.5 comes in. At least that's what Anthropic said. Haiku was built to be the model that is both efficient and as good as a model like Sonnet. It was a good attempt but based on personal experience, the trade-off is pretty huge. For one, it lacks actually deep reasoning like the other models. Furthermore, it is reported to hallucinate a lot and be really rude. It has even gained the nickname of "little liar". This just shows how horrible Haiku really is. Well, this was horribly biased so my editor tells me I need to say how Haiku is good too. Haiku will probably be better as something you use to build your AI apps on. For example, if you want a good AI assistant, it will be a good choice as it is cheaper than other models and still has some sort of good reasoning.

Fortunately for Anthropic, the success of Sonnet and Opus has covered for the lack of Haiku.

Overall, the most useful model will be Sonnet. Unless you are getting a Max plan, the extended use of Opus will cause you credits/token to disappear super quickly. Yes, there is a 5 hour top up but realistically if you are building a full app, you will run out within the hour.

Conclusion

To conclude this article, I will put this very simply. Picture this. There is a very smart but very lazy developer, and a developer not as smart but extremely hard-working. That is basically this in a nutshell. Claude as a whole is extremely smart but loves to leave things blank or act like it did something. For example, if I tell it to make a function to write articles for me, it will do it. Right? Wrong. It will fake it in the easiest way possible and act like it actually did it. Codex is the less brilliant but more hardworking developer. Yes, its not as good when it comes to deep reasoning and the choices it makes, but it makes sure things are done properly and will not just act.

The key difference is that Codex focuses more on execution and completing tasks reliably, while Claude focuses more on reasoning, planning, and generating higher-level solutions.

Do note that this is a personal opinion. While I try to keep it as unbiased as possible, some form of bias might still slip in

Thanks for reading!!

-Ethan