Claude Opus 4.6 vs GPT-5.3 Codex: The Full Breakdown
Both dropped on February 5, 2026. One dominates autonomous coding, the other excels at speed. Here's how they actually compare.
February 5, 2026 was a big day for AI. Anthropic launched Claude Opus 4.6. OpenAI launched GPT-5.3 Codex. Same day, two flagship models, two very different bets on what matters most.
Opus 4.6 set a new SWE-bench record at 80.8%. GPT-5.3 Codex posted the highest Terminal-Bench score at 77.3%. Neither model dominates every benchmark, and that's what makes this comparison worth doing properly.
What shipped on February 5
Claude Opus 4.6
Anthropic's new flagship extends the Claude 4 family with some significant firsts. The headline number is a 1 million token context window, up from 200K in the previous generation. That's roughly 2,500 pages of text in a single prompt.
On the coding front, Opus 4.6 scored 80.8% on SWE-bench Verified, making it the first model to cross the 80% threshold on that benchmark. It also scored 72.7% on OSWorld, the desktop automation benchmark that tests whether models can actually use computers like humans do.
Two new features stand out. Adaptive Thinking lets the model dynamically adjust its reasoning depth, spending more compute on hard problems and less on easy ones, without you having to specify. Agent Teams enables multi-agent orchestration where specialized sub-agents work on different parts of a problem in parallel.
Safety metrics are notably strong. Opus 4.6 scored 1.8 out of 10 on the misalignment benchmark, the lowest (best) score of any frontier model tested. That matters if you're deploying in regulated industries or building user-facing products.
Pricing: $5 per million input tokens, $25 per million output tokens.
GPT-5.3 Codex
OpenAI's release takes a different approach. GPT-5.3 Codex is built for speed and mathematical reasoning, and the benchmarks reflect that.
It scored 77.3% on Terminal-Bench, the highest score from any model on that coding evaluation. Terminal-Bench measures interactive coding ability: multi-step debugging, iterative problem solving, and working in a terminal environment. GPT-5.3 also posted 74.9% on SWE-bench Verified, a strong score in its own right.
Where GPT-5.3 really separates itself is math and science. 94.6% on AIME 2025 and 92.4% on GPQA Diamond. Both best-in-class numbers. AIME is the American Invitational Mathematics Examination, a notoriously difficult test. GPQA Diamond covers graduate-level science questions that even domain experts find challenging.
OpenAI reports that GPT-5.3 is 25% faster than its predecessor at inference, with a sub-1% hallucination rate on factual queries. That speed advantage is noticeable in practice, especially during interactive coding sessions.
API pricing: TBA at time of writing.
The benchmarks, head to head
Here's how the two models stack up across the major evaluations:
Missing bars indicate scores not yet reported by the model maker.
| Benchmark | Claude Opus 4.6 | GPT-5.3 Codex | Winner |
|---|---|---|---|
| SWE-bench Verified | 80.8% | 74.9% | Opus 4.6 |
| Terminal-Bench | 65.4% | 77.3% | GPT-5.3 |
| AIME 2025 | - | 94.6% | GPT-5.3* |
| GPQA Diamond | - | 92.4% | GPT-5.3* |
| OSWorld | 72.7% | - | Opus 4.6* |
| Hallucination Rate | - | <1% | GPT-5.3* |
| Misalignment Score | 1.8/10 | - | Opus 4.6* |
| Context Window | 1M tokens | 128K tokens | Opus 4.6 |
| Inference Speed | Baseline | +25% | GPT-5.3 |
* One-sided comparison. The other model's score has not been published.
Two things stand out. First, neither model sweeps the board. Opus 4.6 wins on autonomous coding (SWE-bench) and computer use (OSWorld). GPT-5.3 wins on interactive coding (Terminal-Bench) and dominates math and reasoning.
Second, both companies are being selective about which benchmarks they highlight. Anthropic hasn't published AIME or GPQA scores for Opus 4.6. OpenAI hasn't published OSWorld or misalignment scores for GPT-5.3. The benchmarks a company doesn't report can tell you as much as the ones it does.
The Architect vs The Speedster
The numbers tell part of the story. The qualitative differences tell the rest.
Scores based on published benchmarks, reported capabilities, and early user testing.
Claude Opus 4.6 is the Architect. Its strengths cluster around autonomous, long-running tasks. The 1M context window means it can hold an entire codebase in memory. Agent Teams lets it decompose complex problems into parallel subtasks. SWE-bench dominance confirms it can handle real-world software engineering (finding bugs, understanding codebases, shipping patches) without constant hand-holding.
This is a model you point at a problem and walk away. It figures out the plan, works through the steps, and delivers a result. The Adaptive Thinking feature reinforces this: it allocates compute where it matters, spending more cycles on the hard parts of a task and breezing through the straightforward ones.
GPT-5.3 Codex is the Speedster. It's 25% faster, better at math-heavy problems, and excels at interactive coding sessions where you're iterating quickly. Terminal-Bench measures exactly this kind of back-and-forth coding, and GPT-5.3 leads there convincingly.
This is a model built for the developer who lives in their terminal, rapidly testing ideas and iterating on solutions. The sub-1% hallucination rate means you can trust its outputs without double-checking every line. The math and reasoning scores mean it won't choke on algorithm-heavy tasks.
The tradeoff is real. If you need a model to autonomously work through a 50-file codebase refactor overnight, Opus 4.6 is your pick. If you need rapid-fire code generation while pair programming, GPT-5.3 gets you there faster.
Where each model actually wins
Theory is fine. Practical recommendations are better.
| Use Case | Better Choice | Why |
|---|---|---|
| Large codebase refactoring | Claude Opus 4.6 | 1M context + Agent Teams handles multi-file changes |
| Pair programming | GPT-5.3 Codex | 25% faster responses, strong Terminal-Bench scores |
| Bug investigation in legacy code | Claude Opus 4.6 | Can load entire repo into context, SWE-bench leader |
| Math-heavy applications | GPT-5.3 Codex | 94.6% AIME, 92.4% GPQA, best-in-class reasoning |
| Regulated industry deployment | Claude Opus 4.6 | 1.8/10 misalignment, strongest safety profile |
| Quick prototyping | GPT-5.3 Codex | Speed advantage matters when iterating fast |
| Multi-agent workflows | Claude Opus 4.6 | Native Agent Teams feature, built for orchestration |
| Data analysis and science | GPT-5.3 Codex | Superior math and reasoning benchmarks |
No model is universally better. The right choice depends entirely on what you're building and how you work.
The context window gap
The single biggest architectural differentiator is context window size.
Opus 4.6 offers 1 million tokens. GPT-5.3 Codex sits at approximately 128K tokens, consistent with OpenAI's previous generation, though the exact number hasn't been confirmed for this release.
That's roughly an 8x difference. In practice, 1M tokens gets you:
- An entire mid-sized codebase (~25,000 lines) in a single prompt
- A complete technical specification with all appendices
- Months of conversation history in an ongoing project
- Multiple large files analyzed simultaneously without chunking
For autonomous coding agents (the kind that need to understand how a change in one file affects behavior three directories away), this gap is meaningful. It's the difference between the model understanding your whole project and only seeing a slice of it.
It also matters for retrieval-augmented generation. More context means you can stuff more relevant documents into a prompt, which means better answers with fewer hallucinations. The 128K ceiling on GPT-5.3 is still generous by historical standards, but against 1M tokens, it's a real constraint for certain workloads.
What the pricing tells you
Claude Opus 4.6: $5 input / $25 output per million tokens.
GPT-5.3 Codex: TBA.
OpenAI hasn't announced pricing, which makes direct cost comparison impossible today. But there's useful context. Claude Opus 4 was priced at $15/$75, so Opus 4.6 represents a 3x price drop at significantly higher capability. That's a meaningful trend.
Assuming OpenAI prices GPT-5.3 competitively with previous GPT-4 tiers, the models will likely land in a similar range. The real cost difference will come from usage patterns: Opus 4.6's larger context window means longer prompts (more input tokens), while GPT-5.3's faster inference means shorter wall-clock time per request.
When GPT-5.3 pricing drops, we'll update this comparison.
Which one should you use
If you're building autonomous coding agents, automated code review pipelines, or any system that needs to understand large codebases as a whole: Claude Opus 4.6. The context window and SWE-bench performance make it the clear pick for agentic workflows. Agent Teams is purpose-built for this.
If you're doing interactive development, math-heavy research, or rapid prototyping where speed directly impacts your productivity: GPT-5.3 Codex. The 25% speed advantage and superior reasoning benchmarks shine here. It's the better pair programmer.
If you can only pick one for general use, this is genuinely close. Opus 4.6 edges it out on versatility. The context window gives it a wider range of applicable tasks, the safety profile makes it easier to deploy in production, and autonomous coding ability covers the broadest set of real-world engineering work. But GPT-5.3 is a better daily driver for developers who value speed above all else.
The honest answer: the best teams will use both. These models have complementary strengths, and the cost of API access is low enough that switching between them based on the task is entirely practical. Use Opus for the big stuff. Use GPT-5.3 for the fast stuff. Don't force a single model to do everything.
February 5, 2026 gave us two exceptional models. The real winner is anyone building with them.