GPT 5.2 Codex Really Changed and What Didn’t
OpenAI says GPT 5.2 Codex is the strongest coding model so far. Better tool calling. Smarter token use. More reliable reasoning. On paper, it sounds like a clear step forward. So the obvious question is simple. Does it actually feel better when put to real work?
To find out, the model was tested against five others using the exact same challenge. Same codebase. Same analysis rules. Same evaluators. No shortcuts, no special treatment.
The short answer is no. Not really.
There were no dramatic jumps. No big “wow” moments. In some cases, GPT 5.2 Codex even slipped a little compared to the non Codex version. It stayed solid, just not noticeably ahead. That alone makes the results worth digging into.
The real world problem used for testing with GPT 5.2 Codex
The test case was not some clean demo project. It was an old Chrome extension written years ago for personal use. The kind of code that works, but was never meant to impress anyone.
The extension adds small dots next to NFL games on YouTube TV. Those dots let users mark how interested they are in a game. Mild interest. High interest. Or not interested at all. Simple idea.
The tricky part is persistence. Reload the page and those choices must still be there. That sounds easy until you realize YouTube TV gives no stable IDs for games. Everything is dynamic. Rows change. HTML shifts. Even the same game can look different later.
So the extension does something messy but clever. It tries to infer a game’s identity using team names, dates, thumbnails, and URLs. Multiple fallback strategies stacked together. It works, but it’s fragile. Change the page slightly and things can break.
That fragile logic is the heart of the challenge.
What GPT 5.2 Codex and other models were asked to do
Each model was given a detailed product document explaining what the extension does, why it exists, and what constraints cannot change. The task was not to rewrite everything. The goal was to understand the system, identify risks, spot weak areas, and suggest safe improvements.
The models also had to explain their thinking. Not just list problems, but show how they arrived there. The final output had to feel like a single clear map of the system. Something a technical person and a decision maker could both understand.
This part turned out to matter more than expected.
GPT 5.2 without Codex still holds strong
The non Codex GPT 5.2 did a surprisingly good job. It explained what the extension does, outlined the architecture, and listed risks clearly enough. The suggestions made sense. Scope the extension tighter. Reduce fragile string handling. Improve separation of concerns.
The communication was not perfect, but it worked. A reader could follow the logic with some effort. The “why” was mostly there, even if it took time to digest.
In evaluator scoring, it landed around a 27. Not amazing. Not bad either.
GPT 5.2 Codex did more work but explained less
GPT 5.2 Codex found almost the same issues. That part stayed consistent. The problem was how it explained them.
The output leaned heavily into visual structure and abstract summaries. Bars. Sections. Clean layouts. But when it came to explaining trade offs or risks, things felt thin. Why does this change matter? What happens if it’s skipped? Is this worth the added complexity?
Those answers were often missing.
It felt like reading a technical dashboard without enough labels. Technically correct, but harder to trust. The evaluator score dropped slightly to 26. A small difference, but noticeable when comparing side by side.
Another issue showed up too. Codex was slower. Token use was higher. Communication felt more stiff. For a model focused on coding, that last part matters more than expected.
Claude Opus compared against GPT 5.2 Codex
Claude Opus 45 in planning mode ended up setting the bar.
Instead of listing findings, it told a story. It explained the extension the way a real system would, including the actual frustrations behind it. The core problem was framed in simple, human terms. Trying to identify games without stable IDs was likened to recognizing people only by what they’re wearing clothes that change every single day. That kind of framing sticks.
It walked through how the system works, why it’s fragile, and where it could fail. File names were referenced. Risk areas were highlighted. Fixes were suggested with clear reasons behind them.
This wasn’t just analysis. It was context mapping. A way of showing not just what the model found, but how it thought about the problem from start to finish.
That difference mattered more than raw scores.
Gemini performance compared to GPT 5.2 Codex
One result was honestly shocking. Gemini.
Across multiple runs, Gemini only identified issues that were already spelled out in the product document. It did not discover a single new problem. None of the deeper risks around ID inference. None of the mitigation logic others flagged instantly.
It wasn’t that Gemini coded poorly later. It fixed what it saw reasonably well. The issue was vision. It simply didn’t look beyond what was handed to it.
For a model often praised for coding strength, this was a red flag. It worked inside a narrow box and never stepped outside it. That kind of blind spot is dangerous in real systems.
Why communication matters more than coding skill
After days of testing, one thing became clear. The biggest difference between models was not raw coding ability. It was communication.
How clearly can a model explain what it considered? What risks it weighed? Why it chose one path over another? That handoff of thinking is becoming critical, especially as AI agents start working in longer chains.
This idea is best described as context mapping. A shared mental model between human and machine. Without it, even correct answers feel shaky.
Right now, most models still struggle here.
Is GPT 5.2 Codex worth using right now
Yes, but with caveats.
If security is the main focus, GPT 5.2 Codex deserves attention. That area was clearly emphasized in its design. Windows tool calling is another strong point. In long running agent workflows, these advantages may stack up over time.
But for everyday analysis and communication, Codex does not yet feel like a clear upgrade. It is good. Just not better in ways that matter most right now.
If choosing blindly, Codex is still a safe option. It was built for this work. Just don’t expect miracles yet.
Final thoughts
The testing showed something important. Model progress is no longer just about smarter answers. It’s about clearer thinking trails. The ability to explain decisions, risks, and trade offs without forcing blind trust.
That gap is still wide. Some models are closer than others, but none have fully solved it.
Keep an eye on how models communicate, not just what they output. That’s where the real changes are starting to happen.
