What You’ll Learn: A data-driven comparison of GPT-5.3 Codex and Claude Opus 4.6, the two most powerful AI coding models ever built. Released 20 minutes apart on February 5, 2026. Benchmarks, pricing, features, and which one to pick for your workflow.
On February 5, 2026, Anthropic dropped Claude Opus 4.6 at 6:40 PM. Exactly 20 minutes later, OpenAI fired back with GPT-5.3 Codex. Two flagship models, same day, same target: the AI coding market.
This wasn’t a coincidence. Both companies are racing to own the enterprise developer workflow. And for the first time, their best models are genuinely hard to pick between.
I’ve tested both extensively. Here’s what the numbers say, and what they don’t.
Quick Specs
| Spec | GPT-5.3 Codex | Claude Opus 4.6 |
|---|---|---|
| Release Date | Feb 5, 2026 | Feb 5, 2026 |
| Context Window | 400K tokens | 1M tokens (beta) |
| Max Output | 128K tokens | 128K tokens |
| API Input Price | $1.75/MTok | $5.00/MTok |
| API Output Price | $14.00/MTok | $25.00/MTok |
| Cached Input | ~90% off | 75% off ($1.25/MTok) |
| Speed | 50 tok/s, 25% faster than 5.2 | Slower, deeper reasoning |
| API Access | Codex app + coming soon | Available now |
GPT-5.3 Codex costs roughly 2.8x less per input token. Opus 4.6 has 2.5x more context. That tradeoff defines the entire comparison.
Head-to-Head Benchmarks
Neither model wins everything. The gaps tell you exactly what each one is built for.
| Benchmark | GPT-5.3 Codex | Opus 4.6 | Winner |
|---|---|---|---|
| Terminal-Bench 2.0 (CLI tasks) | 77.3% | 65.4% | Codex |
| SWE-Lancer IC Diamond (real bugs) | 81.4% | N/A | Codex |
| Cybersecurity CTFs | 77.6% | N/A | Codex |
| SWE-bench Verified (coding) | 80.0% | 80.8% | Opus |
| OSWorld (computer use) | 64.7% | 72.7% | Opus |
| GDPval-AA (knowledge work) | 70.9% ties | 1606 Elo | Opus |
| BrowseComp (web research) | 77.9% | 84.0% | Opus |
| ARC-AGI-2 (abstract reasoning) | ~54% | 68.8% | Opus |
| Humanity’s Last Exam | ~50% | 53.1% | Opus |
| MMMLU (general knowledge) | 89.6% | 91.1% | Opus |
Codex dominates terminal work and cybersecurity. Opus dominates reasoning and agentic tasks. On pure code editing (SWE-bench), they’re nearly identical.
Where GPT-5.3 Codex Wins
Terminal and CLI work is where Codex pulls away hard. It hit 77.3% on Terminal-Bench 2.0, up from 64.0% on GPT-5.2 Codex. That’s a 13-point jump in one generation. If your workflow involves shell scripts, DevOps pipelines, or command-line debugging, Codex is significantly better.
Cybersecurity is a new frontier. GPT-5.3 Codex scored 77.6% on capture-the-flag competitions, becoming the first model OpenAI classifies as “High capability” for cybersecurity. This matters for security teams running penetration tests, code audits, or vulnerability scanning.
Speed is a real advantage. Codex generates tokens 25% faster than its predecessor and outputs around 50 tokens per second. For interactive coding sessions where you’re waiting on responses, that adds up. Opus 4.6 trades speed for deeper reasoning, which means longer wait times.
It helped build itself. This sounds like marketing, but it’s technically interesting. OpenAI used early versions of GPT-5.3 Codex to debug its own training process, manage deployment, and diagnose test failures. That self-bootstrapping loop is a first.
Cost efficiency matters at scale. At $1.75/$14 per million tokens, Codex runs about 2.2x cheaper than Opus for the same volume. With caching, input costs can drop to around $0.18 per million tokens. For teams processing thousands of code reviews daily, the savings are significant.
Where Claude Opus 4.6 Wins
Agentic computer use is Opus territory. On OSWorld, which tests real desktop automation (clicking buttons, filling forms, navigating UIs), Opus scores 72.7% vs Codex’s 64.7%. That 8-point gap means Opus handles complex multi-step computer tasks more reliably.
Abstract reasoning isn’t close. Opus hits 68.8% on ARC-AGI-2, nearly doubling its predecessor and crushing Codex’s ~54%. This benchmark tests novel problem-solving that doesn’t rely on memorized patterns. For tasks that require genuine creative thinking, Opus has a clear edge.
The 1M token context window changes the game. Codex tops out at 400K tokens. Opus can handle 1 million tokens in beta, scoring 76% on MRCR v2 for 8-needle retrieval at that scale. You can feed it an entire codebase, multiple legal contracts, or a full research paper collection in one conversation. Codex simply can’t do that.
Knowledge work is Opus’s biggest lead. On GDPval-AA, which measures real-world performance across finance, legal, and professional tasks, Opus leads Codex by 144 Elo points. It produces higher-quality outputs about 70% of the time on enterprise knowledge work. Its BigLaw Bench score of 90.2% makes it particularly strong for legal teams.
Agent Teams is a killer feature. Opus 4.6 introduced multi-agent coordination in Claude Code, where multiple AI agents work on different parts of a project simultaneously and coordinate autonomously. Combined with context compaction (automatic context summarization mid-session), Opus can handle much longer, more complex development sessions than Codex.
Pricing Breakdown
Here’s what a typical development team workload costs. Assume 10M input tokens and 2M output tokens per day.
| GPT-5.3 Codex | Claude Opus 4.6 | |
|---|---|---|
| Daily Input Cost | $17.50 | $50.00 |
| Daily Output Cost | $28.00 | $50.00 |
| Daily Total | $45.50 | $100.00 |
| Monthly Cost | ~$1,365 | ~$3,000 |
Codex is 2.2x cheaper at the same volume. But Opus often needs fewer turns to solve complex problems, which can narrow the effective gap. For long-context tasks (like processing a 500K-token codebase), Opus handles it in one pass where Codex might need multiple chunked requests.
Which One Should You Pick?
Pick GPT-5.3 Codex if:
– You work heavily in terminal/CLI environments
– Speed and low latency matter for your workflow
– You’re on a budget or running high-volume API calls
– Security auditing and penetration testing are part of your job
– You want tight GitHub Copilot integration
Pick Claude Opus 4.6 if:
– You’re doing large refactors across massive codebases
– You need to process very long documents in one conversation
– Abstract reasoning and novel problem-solving are critical
– Enterprise knowledge work (legal, finance, compliance) is your domain
– You want multi-agent workflows that coordinate autonomously
The real answer? Use both. Codex for fast, iterative terminal work and daily coding. Opus for deep reasoning, complex architecture decisions, and anything that needs massive context. They’re built for different jobs, and the best developers in 2026 aren’t picking sides.
Frequently Asked Questions
Which model is better for coding in 2026?
It depends on the type of coding. GPT-5.3 Codex leads on terminal and CLI tasks with 77.3% on Terminal-Bench 2.0, while Claude Opus 4.6 leads on agentic software engineering with 80.8% on SWE-bench Verified and 72.7% on OSWorld. For quick, interactive coding sessions, Codex is faster. For complex refactors and multi-file changes, Opus is more reliable.
Were GPT-5.3 Codex and Claude Opus 4.6 really released on the same day?
Yes. Anthropic announced Claude Opus 4.6 at approximately 6:40 PM on February 5, 2026. OpenAI launched GPT-5.3 Codex exactly 20 minutes later. Industry observers called it the opening shot of the AI coding wars, with both companies targeting the enterprise developer market.
Which AI model has a bigger context window?
Claude Opus 4.6 supports up to 1 million tokens in beta, while GPT-5.3 Codex supports 400K tokens. Both models can output up to 128K tokens. For processing entire codebases, long legal documents, or multiple research papers in a single conversation, Opus 4.6’s 2.5x context advantage is significant.
Is GPT-5.3 Codex cheaper than Claude Opus 4.6?
Yes. GPT-5.3 Codex costs $1.75/$14 per million input/output tokens, while Opus 4.6 costs $5/$25. That makes Codex roughly 2.2x cheaper for the same token volume. Codex also offers approximately 90% off cached inputs, making it even more cost-effective for repetitive workloads.