Why OpenAI Built GPT-5.2 Codex (And Why It’s Not for Everyone)

OpenAI just released GPT 5. 2 codecs. This isn't a general chat GPT update. It's a model built specifically for professional software engineering and cyber security. And the timing matters. Right now, OpenAI is under pressure, especially from Google. And GPT 5. 2 Codeex tells us a lot about how OpenAI plans to respond. So, let's get into it. Over the past few weeks, Google has been moving fast. They push models like Gemini Flash cheaper, faster, and good enough for many everyday tasks. That puts pressure on OpenAI. If AI becomes only about speed and cost, OpenAI risks losing its advantage. So instead of racing Google headon, OpenAI is doing something else. They're focusing on depth, not breath. GPT 5. 2 Codeex is part of a clear strategy shift. Instead of one model that does everything, OpenAI is building specialized models, models for reasoning, models for research, and now a very focused model for coding and cyber security. Codeex is not meant for casual use. Is meant for people working on real systems over a long period of time. So what exactly is GPT 5. 2 Codeex? According to OpenAI, this is their most advanced agentic coding model and is supposed to be used for complex realworld software engineering problems. GPT 5. 2 codecs is a version of GPT 5. 2 that was released earlier this year, but is optimized for agentic coding in codecs. So, it's supposed to include improvements for long horizon work through context compaction. It's supposed to have stronger performance on large code changes like refactors. and it has improved performance in Windows environments and significantly stronger for cyber security capabilities. That is something that OpenAI is shifting their strategy. They're trying to use GPT 5. 2 codecs as an enterprise tool geared towards real development teams geared towards real enterprises that need to worry about cyber security. And this is supposed to be a model that has the best cyber security capabilities. And to show this, they actually highlight an example where a security researcher using GPT 5. 1 CodeexMax with Codeex CLI found and responsibly disclosed a vulnerability in React that could lead to source code exposure. So now the GPT 5. 2 codec has stronger cyber security capabilities than any of the models they have released in the past and this helps enterprises feel more secure and adopt GPT 5. 2 codecs. Now, when we look at the benchmark results, we can see that this model is improved in software engineering bench pro. It currently sits at 56. 4% while GPT 5. 2 sits at 55. 6 and GPT 5. 1, their older model, sits at 50. 8%. On the Terminal Bench 2. 0 benchmark, GPT 5. 2 Codeex is now at 64% and GPT 5. 2, 62. 2, 2 and GPT 5. 1 C codeex max is at 58. 1. So the software engineering bench pro the model is actually given a code repository and it has to generate a patch to solve realistic software engineering tasks. So you can see that it's achieving about 56. 4 accuracy. Obviously these models are not at a point where they're going to replace actual developer teams but we can see that it's able to achieve 56. 4 accuracy which is huge. And then the terminal bench 2. 0 0 benchmark is used to test AI agents like codecs in real terminal environments and tasks include like compiling code, training the models and setting up servers. Real world tasks that most developers have to do and it's achieving a 64% accuracy which is also a huge advancement and we're only going to see these models get better over time. Another cool thing about GPT 5. 2 codecs is that it has stronger vision performance. to show you guys this. They've actually given an example where they take a mock image that you can see on the side over here where they provided like okay we want the user interface to include these functions have all of these panels on the side and these interaction elements that we are seeing here and then the prototype that's generated by GPC 5. 2 codeex kind of matches that to a T and gives you all those functionalities that we see here. You can click on the audio button. It will open up there images. We can click on the edit file, all of these things that we see here. And then also play around with all of the functionalities, everything like that. Like we can see that the scaling option works here. So why don't we actually test it out? I'm going to give GPT 5. 2 Kodak a simple design mock that we see on the left. Not this one obviously, maybe something else. and let's see if it's actually able to produce something like this or, you know, OpenAI produced a demo that only looks good on paper. So, to test out Codeex's new abilities, what we're going to do is that we're going to take a screenshot of the current screen right now, which is Notebook LM. And I'm going to tell Codeex to make a version of this based off of the screenshot, and let's see what it creates. So, let me just take a quick screenshot of this.

Then I'm going to put in that screenshot and then I'll say okay. So I given it the screenshot and I'm going to tell it to make me a version of this and you can call it whatever you want. So let's see this in action. All right. So this is the end result. It called it luminina notes. So not bad. Interesting name. And it looks pretty similar to what notebook LM actually looks like. And if you look at the code and everything like that, it did try to copy the Google fonts and everything like that. it added in that try deep research note over here. We also have the web feature, the fast research and drive features here that it tried to add as well. You can upload your source, things like that. But as you can see, like all of these buttons are obviously static. They're not actual buttons. It had a little bit of hovering to these buttons here, which is a good touch, but nothing really happens when you click on these buttons. So, obviously, we're not expecting it to fully create a simple app like that. But when it comes to its visual capabilities and understanding what the demo or what the mockup is supposed to look like, it follows it pretty much to a tea. This is quite similar to what notebook element actually looks like. So, I'm not upset with it. This is pretty good. But now that you've seen how Codex behaves in real coding situation, this part should make more sense. Now, what you're looking at here is a real example of how Codeex was used to help uncover a security vulnerability. And it starts with an actual Git repository. It's real production code, so it's not a sandbox project or a simplified example. So, the first thing Codeex does is try a straightforward scan of the codebase to see if anything obvious stands out. In this case, that didn't work. And that's actually important because most real vulnerabilities don't show up immediately and they're hard to find. So, instead of stopping there, the process becomes a little bit more guided through a security researcher. So the researcher starts directing Codex more deliberately asking it to focus on certain parts of the code, reason about where problems might exist and look for areas that could be abused. As this continues, Codeex helps set up tests and supports fuzzing, which is basically throwing unexpected or malformed inputs at the system to see how it would behave under stress. And this is a very common practice. At that point, Codex starts to surface behavior that doesn't quite look right, and the human researcher then steps in, verifies what's happening, builds a proof of concept, and confirms that it is actually a real issue. From there, the vulnerability is responsibly disclosed, and is patched. So, the key thing to understand here is that codec isn't acting on its own. It's working alongside a human expert and speeding up parts of the process that usually take a lot of time. This chart shows how OpenAI's models have performed over time on professional cyber security challenges. These aren't simple tests. They're multi-step problems that require real security knowledge running in realistic environments. What really matters here isn't actually the exact accuracy number. It's the overall trend. You can see a clear jump when open introduced GPT5 codecs and another jump with GPT 5. 1 Codeex Max and now another jump once again with GPT 5. 2 in two codecs. That tells us that the models are getting meaningfully better at reasoning through complex security tasks over time. At the same time, OpenAI is clear that GPT 5. 2 codecs hasn't crossed their highest risk threshold yet, but they're already planning for future models that could. If you enjoy this video, this is what we do here. Fast, clear updates on the biggest moves in AI. If you want to stay ahead of everything happening in this space, make sure you're subscribed. And if you want the hands-on side, demos, tools, workflows, and everything developers can actually build, well, check out the World of AI. We also run a simple no noise newsletter that gives you the most important AI tools and updates in just a couple of minutes. Subscribe here. Follow World of AI. Join the newsletter.