The video compares Anthropic’s Opus 4.6 and OpenAI’s Codex 5.3, highlighting that Codex is more thorough and reliable for large, complex codebases, while Opus is faster and excels at front-end and design tasks. Pricing, workflow, and language strengths differ, so the presenter recommends using both models as needed to maximize productivity and code quality.
The video provides an in-depth, hands-on comparison between two leading closed AI coding models: Anthropic’s Opus 4.6 (via Claude Code) and OpenAI’s Codex 5.3. The creator, who has spent extensive time using both models for real-world coding tasks, emphasizes that while both are highly capable, they have distinct strengths and weaknesses. Codex is generally preferred for complex, large-scale codebase work due to its thoroughness and reliability, whereas Opus is faster, more pleasant to interact with, and excels at front-end and design tasks. The presenter notes that neither model is perfect, and their performance can vary depending on the specific use case and workflow.
Pricing and usage are discussed in detail. Opus is more expensive per token, especially in fast mode, but Codex tends to generate more tokens per task, which can make actual costs comparable or even higher for Codex in some scenarios. However, Codex’s subscription plans are currently more generous, making it harder to hit usage limits compared to Opus. The presenter also points out that Codex 5.3 is not yet available via API for most users, which complicates direct cost comparisons. Both models offer $200/month subscription tiers that are difficult to max out for most developers, but the $20/month plans have significant limitations, particularly in speed and usage caps.
In terms of coding intelligence and problem-solving, Codex is described as the “measure twice, cut once” model: it is meticulous, rarely misses important details, and is less likely to introduce subtle bugs or security flaws. Opus, on the other hand, is likened to a highly caffeinated engineer who prioritizes shipping quickly, sometimes at the expense of completeness or correctness. Opus is more likely to skip over blockers or trim scope to get something working, which can result in faster progress but also more technical debt and cleanup. Codex is better at following existing patterns in large codebases and is more resistant to making unsafe or insecure changes.
The video also covers specific language and workflow preferences. Opus is notably better at front-end development, UI design, and working with modern tools and frameworks, while Codex excels in languages like Rust and in navigating and updating large, established codebases. For tasks involving system configuration or quick terminal commands, Opus is preferred for its speed and willingness to take shortcuts. However, for critical engineering work, code reviews, and security-sensitive tasks, Codex is trusted more due to its diligence and lower propensity for introducing errors.
Ultimately, the presenter recommends experimenting with both models to find the best fit for individual workflows. Codex is the default recommendation for most serious coding work, especially in larger projects, but Opus remains valuable for its speed, user experience, and front-end capabilities. The video concludes by encouraging developers to leverage both tools as needed, noting that switching between them is easy and that using multiple models can help catch issues that one alone might miss. The rapid evolution of these models means that staying flexible and open to new tools is essential for modern software development.