Claude Opus 4.6 vs GPT-5.3-Codex: which AI coding model should you use?

On February 5, 2026, Anthropic launched Claude Opus 4.6. Twenty minutes later, OpenAI dropped GPT-5.3-Codex. Two flagship AI coding models, same day, practically the same hour.

If you’re trying to figure out which one to use for your projects, you’re not alone. Let’s break down what actually matters.

What we’re comparing

Claude Opus 4.6 is Anthropic’s most capable model. It powers Claude Code, the terminal-based AI coding assistant that plans, writes, and debugs code through natural conversation.

GPT-5.3-Codex is OpenAI’s latest model built specifically for their Codex platform. It combines frontier coding performance with reasoning capabilities, and it’s 25% faster than its predecessor.

Both models represent genuine leaps over what came before. This isn’t a minor version bump situation.

The specs at a glance

Here’s how the two stack up on paper:

	Opus 4.6	GPT-5.3-Codex
Context window	1M tokens (beta)	~400K tokens
Max output	128K tokens	Not disclosed
API pricing (input)	$5/M tokens	Not yet available
API pricing (output)	$25/M tokens	Not yet available
Access	API, claude.ai, Claude Code	Codex app, CLI, IDE extension
SWE-Bench Pro	Strong	56.8% (state-of-the-art)
Terminal-Bench 2.0	Highest reported	77.3%
Self-improving	No	Yes (helped build itself)

A few things jump out. Opus 4.6 has a massive context window advantage at 1M tokens. GPT-5.3-Codex doesn’t have public API pricing yet, which makes direct cost comparison tricky. And both claim top scores on Terminal-Bench 2.0, which tells you how competitive this space has gotten.

Where Opus 4.6 shines

Deep reasoning on hard problems. Opus 4.6 thinks more carefully and revisits its reasoning before settling on an answer. Anthropic’s own team describes it as bringing “more focus to the most challenging parts of a task without being told to.” If you’re working through a complex architecture decision or debugging a gnarly issue, this matters.

The 1M token context window. This is a first for an Opus-class model. You can load an entire codebase into context and ask questions about it. For code review, refactoring, and understanding unfamiliar projects, this is a genuine superpower. Sonnet 4.5 scores just 18.5% on the 8-needle 1M variant of MRCR v2 (a needle-in-a-haystack benchmark). Opus 4.6 scores 76%.

Code review and debugging. Multiple early access partners called out Opus 4.6’s ability to navigate large codebases and catch bugs. Cursor’s CEO said it’s “the new frontier on long-running tasks.” Cognition’s CEO said it “considers edge cases that other models miss.”

Agent teams. Claude Code now supports spinning up multiple agents that work in parallel. You can coordinate a team of subagents for codebase reviews and large refactoring tasks. That’s a workflow no other tool offers right now.

Transparent pricing. $5/$25 per million tokens, same as before. You know exactly what you’re paying.

If you want to go deeper, check out our Claude Code tutorial for beginners.

Where GPT-5.3-Codex shines

Speed. GPT-5.3-Codex is 25% faster than its predecessor and uses fewer tokens than any prior model on Terminal-Bench. If you’re running lots of tasks in parallel, speed adds up fast.

Interactive steering. This is a genuinely novel feature. You can talk to GPT-5.3-Codex while it’s working, redirect it, ask questions, and discuss approaches without losing context. It’s less “fire and forget” and more “working alongside a colleague.”

Beyond code. GPT-5.3-Codex isn’t just a coding model. It handles slide decks, spreadsheets, data analysis, and other knowledge work. It scored well on GDPval, which measures performance across 44 different occupations. If your workflow goes beyond pure code, this flexibility matters.

Self-improvement. This is the first model that helped build itself. OpenAI’s Codex team used early versions to debug training, manage deployment, and diagnose evaluations. That’s a fascinating milestone, even if the practical benefit to you as a user is mostly that OpenAI could iterate faster.

Web development aesthetics. OpenAI specifically called out improvements in how GPT-5.3-Codex handles frontend work. Simple prompts now produce more polished, production-ready output with sensible defaults.

Want to try it yourself? Here’s our Codex tutorial for beginners.

The honest take

Here’s what nobody in the AI space wants to admit: these models are converging. Both are excellent at coding. Both handle complex, multi-step tasks. Both can work autonomously for extended periods.

The differences are real but they’re getting narrower with every release. Six months ago, the gap between competing models was obvious. Today, you have to squint at benchmark tables to find meaningful separation.

What actually matters more than the model is how you use it. A well-structured prompt on either model will beat a lazy prompt on the “better” one every time. Your workflow, your project structure, and how you iterate with the AI matter more than which model sits behind it.

That said, there are real differences in how these tools feel to use day-to-day. And that feel matters when you’re spending hours working with them.

Which one should you pick?

The best answer? Try both. Seriously. These tools are different enough in how they work that personal preference matters a lot. Some people love the Claude Code terminal flow. Others prefer the Codex app’s visual interface. You won’t know until you try them with your own projects.

Get started with both

You don’t have to choose one or the other. Many developers use different AI tools for different tasks, and that’s the smart play.

The AI coding space is moving fast. Two flagship models launching 20 minutes apart is proof of that. The good news? You’re the one who benefits. Pick the tool that fits how you think, and start building.