GPT-5.2 vs GPT-5.2-Codex: What's the Difference? [Dec 2025 Guide]

On 18 December 2025, OpenAI released GPT-5.2-Codex. If you read that and immediately thought "Wait, didn't GPT-5.2 just come out last week?", congratulations. You've stumbled into another episode of OpenAI's ongoing naming chaos.

GPT-5.2 launched on 11 December. Seven days later, GPT-5.2-Codex arrives. And if you're wondering what Codex even means anymore, or whether you need this new model when you've already got GPT-5.2, you're asking exactly the right questions.

I've spent 20 years building web systems, and I've watched OpenAI's naming conventions go from confusing to borderline absurd. (Remember when we had four different GPT-5.1 variants with names like "Max" and "Instant"?) But this latest release takes the confusion to a new level, because "Codex" has been at least four different things since 2021.

Let's sort this out.

The Codex Identity Crisis: A Brief History

Here's something that'll make you feel old. Codex has been around since 2021, and it's reinvented itself more times than a startup pivoting to avoid bankruptcy.

August 2021: OpenAI launches the original Codex. It's an autocomplete engine, basically GPT-3 fine-tuned on code. GitHub Copilot is built on it. Developers love it. It's brilliant at finishing your functions and suggesting implementations. This is the Codex most people remember.

March 2023: OpenAI deprecates Codex. Just kills it. The API shuts down. GitHub Copilot moves to GPT-4. The name "Codex" effectively dies for about two years. I had clients asking me what happened to it, and honestly, I didn't have a great answer beyond "OpenAI decided to move on."

May 2025: Codex comes back from the dead, but now it's an "AI coding agent". Not autocomplete anymore. Now it's supposed to handle long-running coding tasks, massive refactors, entire feature implementations. Different product entirely, same name.

December 2025: We get two new Codex models in two weeks. GPT-5.1-Codex-Max drops on 4 December. Then GPT-5.2-Codex arrives on 18 December, just one week after the base GPT-5.2 model.

So when someone says "Codex" to you, they could mean:

• The 2021 autocomplete engine (dead)
• The GitHub Copilot foundation (migrated to GPT-4)
• The 2025 agentic coding model (current)
• Any of the three specific model releases in the past six months

That's not documentation. That's archaeology.

GPT-5.2 vs GPT-5.2-Codex: What's Actually Different?

Right, so you've got access to GPT-5.2 already. It launched on 11 December, it's brilliant for general tasks, and it beat Claude Opus 4.5 on several benchmarks. Why would you want GPT-5.2-Codex?

The short answer is: you probably don't. Unless you're doing very specific things.

GPT-5.2-Codex isn't just GPT-5.2 with a fancy label. It's a specialised variant with coding-specific fine-tuning. Think of it like the difference between a general practitioner and a surgeon. Both are doctors, but you wouldn't want the GP doing your heart surgery.

Here's what OpenAI says Codex is optimised for:

• Long-horizon agentic work: Tasks that take hours or days, not minutes. Think entire feature migrations, not fixing a bug.
• Context compaction: Handling massive repositories where you need to understand how 50 files interact.
• Large-scale refactors: Renaming patterns across thousands of files, restructuring codebases, upgrading frameworks.
• Windows environments: Apparently this matters enough to call out specifically. (I'm on Windows, so I appreciate it.)
• Cybersecurity tasks: Vulnerability research, attack simulation, security analysis.

But here's the thing developers are noticing. GPT-5.2-Codex might have an older knowledge cutoff than GPT-5.2. I haven't seen OpenAI confirm this officially, but multiple developers on X are reporting that Codex doesn't know about things from November or December 2025 that the base GPT-5.2 model handles fine.

That's a real problem if you're trying to use cutting-edge libraries or frameworks. You might get better code structure from Codex, but if it doesn't know about the API changes that shipped last month, you're debugging outdated suggestions.

The Benchmarks: Where Codex Actually Wins

Let's talk numbers, because this is where it gets interesting.

SWE-bench Pro (professional-level software engineering tasks):

• GPT-5.2-Codex: 56.4%
• GPT-5.2 Thinking: 55.6%
• Claude Opus 4.5: 52%
• GPT-5.1 Thinking: 50.8%
• Gemini 3 Pro: 43.3%

Zoom

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SWE-Bench Pro benchmark comparison showing GPT-5.2-Codex at 56.4%, GPT-5.2 Thinking at 55.6%, Claude Opus 4.5 at 52%, GPT-5.1 Thinking at 50.8%, and Gemini 3 Pro at 43.3%

That's not a massive difference. Less than 1 percentage point between GPT-5.2-Codex and GPT-5.2 Thinking. If you're using the base model for coding tasks, you're not missing much. But notice how far behind Gemini 3 Pro falls here, trailing by over 13 percentage points.

SWE-bench Verified (verified real-world GitHub issues):

• Claude Opus 4.5: 80.9%
• GPT-5.2: 80.0%
• Gemini 3 Pro: 76.2%

Wait, where's Codex? OpenAI didn't report its SWE-bench Verified score. That's interesting. If it crushed this benchmark, you'd think they'd shout about it.

Terminal-Bench 2.0 (terminal command generation and debugging):

• GPT-5.2-Codex: 64.0%
• Claude Opus 4.5: 59.3%
• GPT-5.2: ~47.6%

Now this is where Codex pulls ahead significantly. If you're building tools that generate bash scripts, automate deployments, or work heavily with command-line interfaces, Codex has a real advantage. That's nearly 17 percentage points over the base GPT-5.2.

Cybersecurity (OpenAI's own internal benchmarks):

• Network Attack Simulation: 79%
• Vulnerability Research: 80%

These numbers come from OpenAI's system card, so take them with appropriate scepticism. But if they're even remotely accurate, this is a genuinely useful model for security teams. Finding vulnerabilities in code is time-consuming, tedious work. If Codex can do it at 80% accuracy, that's a real productivity gain.

Who GPT-5.2-Codex Is Actually For

Let me be blunt. This isn't a ChatGPT feature. You can't just pop open the ChatGPT interface and select "GPT-5.2-Codex" from a dropdown. This is an API model designed for agentic workflows.

That means it's for:

• Enterprise development teams with massive codebases. If you're maintaining a repo with thousands of files and you need to refactor architecture, Codex might save you weeks. We're talking about projects where understanding the full context is humanly impossible.
• Agentic workflow developers building custom tools. If you're using Cursor, GitHub Copilot Workspace, or building your own AI coding pipelines, Codex is designed for you. It's built to run autonomously on long tasks, not to sit there waiting for you to prompt it every 30 seconds.
• Security researchers doing vulnerability analysis. That 79-80% benchmark performance on cybersecurity tasks isn't marketing fluff. If you're hunting bugs in code or simulating attacks, this model might genuinely be better than alternatives.
• Windows development shops (apparently). OpenAI called out Windows specifically in their announcement, which makes me think they've done real optimisation work here. If you're building .NET applications or working in the Microsoft ecosystem, that's worth knowing.
• Teams doing massive migrations. Moving from AngularJS to React? Upgrading a Python 2 codebase to Python 3? Restructuring 200 components because your architecture was wrong three years ago? These are Codex tasks.

Who GPT-5.2-Codex is NOT For

Just as important is understanding who this model isn't designed for.

• ChatGPT users. If you're using ChatGPT for coding help, you're not getting Codex. You're getting GPT-5.2 (or 5.1, depending on your subscription). Codex is API-only.
• Casual coders wanting autocomplete. The original 2021 Codex was brilliant at finishing your functions. This isn't that. If you want autocomplete, use GitHub Copilot or Cursor. They're better tools for that job.
• Anyone needing cutting-edge knowledge. If Codex really does have an older knowledge cutoff (and developers are reporting it does), then you'll struggle with new libraries, recent framework updates, or APIs that changed in the last few months.
• Solo developers on small projects. You don't need an agentic model designed for long-horizon tasks if you're building a React dashboard with 20 components. GPT-5.2, Claude Opus 4.5, or even GPT-4 will handle that just fine. Codex is overkill.
• People who just want "better GPT-5.2". This isn't an upgrade. It's a specialisation. In many general coding tasks, it'll probably perform slightly worse than GPT-5.2 because it's optimised for different things.

What Developers Are Actually Saying

The reaction from developers has been, well, mixed. Some people are excited about the terminal and cybersecurity benchmarks. Others are confused about why this exists.

There's a broader pattern emerging in how developers are using these models. Nobody's sticking to just one anymore. You use Claude for complex reasoning, GPT-5.2 for speed, Codex for long refactors, and Gemini for cost. It's model switching based on the task, not brand loyalty.

That's honestly the right approach. I've been doing this for 20 years, and the developers I respect most are pragmatic. They don't care about being an "OpenAI shop" or a "Claude shop". They care about shipping features and solving problems.

The Naming Problem (Again)

We need to talk about OpenAI's naming strategy, because this is genuinely becoming a problem.

In the past month, we've had:

• GPT-5.1-Max
• GPT-5.1-Instant
• GPT-5.1-Codex-Max
• GPT-5.2
• GPT-5.2-Codex

And that's just December. If you go back to May, there are even more variants.

Compare this to Anthropic's approach. They release Claude Opus 4.5. That's it. One model, clear name, everyone knows what it is. Six months later, they'll release Claude Opus 5. Simple.

Google's doing the same thing with Gemini 3 Pro. One model. Clear versioning. No "Instant" or "Max" or "Codex" variants muddying the waters.

I get why OpenAI is doing this. They're trying to serve different use cases with specialised models. But the naming is creating cognitive overhead. Every time a new model drops, developers have to spend 30 minutes figuring out what it actually is, whether they need it, and how it relates to the model they're already using.

That's not a great developer experience.

Should You Switch to GPT-5.2-Codex?

Here's my honest take after watching this model release and reading through the benchmarks and developer reactions.

If you're using ChatGPT for coding: You can't switch to Codex. It's not available in ChatGPT. Keep using GPT-5.2.

If you're using the OpenAI API with GPT-5.2: Try Codex for terminal-heavy tasks and large refactors. But keep GPT-5.2 as your default for most coding work. Codex is specialised, not better across the board.

If you're building agentic coding tools: Absolutely test Codex. This is what it's designed for. If you're running long-horizon tasks where the model needs to maintain context across hours of work, Codex might be worth the switch.

If you're doing security research: The cybersecurity benchmarks are compelling. Worth testing against your current workflow to see if it actually finds more vulnerabilities.

If you're on Windows and working in .NET: The specific Windows optimisation is interesting. I'd test it against GPT-5.2 on your actual codebase to see if there's a noticeable difference.

For most developers, though? GPT-5.2 is probably still the better default. It's faster, it has more up-to-date knowledge, and it handles general coding tasks just as well. Use Codex when you need its specific strengths, not as a replacement.

Key Takeaways

• Codex has been four different things since 2021. The original autocomplete engine is dead. This is an agentic coding model for long-running tasks.
• GPT-5.2-Codex launched one week after GPT-5.2 (11 December vs 18 December). OpenAI's naming chaos continues.
• Codex is specialised, not universally better. It excels at terminal commands (64% vs 47.6% for GPT-5.2) and cybersecurity tasks, but might have an older knowledge cutoff.
• It's API-only. If you're using ChatGPT, you're not getting Codex. This is for agentic workflows, custom tools, and enterprise integrations.
• Best use cases: Massive refactors, long-horizon agentic tasks, terminal automation, cybersecurity research, Windows development.
• Not ideal for: Casual coding, autocomplete, bleeding-edge framework knowledge, small projects, general ChatGPT usage.
• Multi-model approach is becoming standard. Developers are switching between Claude, GPT-5.2, Codex, and Gemini based on specific tasks, not sticking to one model.

The AI model landscape is moving fast, and OpenAI's naming isn't helping anyone keep up. If you're confused about whether you need GPT-5.2-Codex, you're in good company. Most developers are still figuring it out too.

Welcome to the club.