Opus 4.6 vs GPT-5.3 Codex: Best AI Model for App Builds & UI Rebuilds

Comparison of AI Models: Opus 4.6 vs. GPT 5.3 Codeex

Overview of the Tests Conducted

• Anthropic released Opus 4.6, while OpenAI launched GPT 5.3 Codeex; both models are available in Cursor.

• Two identical tasks were assigned to each model: building an app from a detailed PRD and visual comprehension using screenshots.

Test 1: App Development from PRD

• The app named "Quakewatch" is designed as a real-time earthquake dashboard utilizing live data from the USGS API.

• Key features include an interactive map with clustered markers, filterable event feed, detail panel, and three chart types—all requiring complex state management and performance targets.

Build Time Comparison

• Codeex completed the build in 7 minutes and 3 seconds, while Opus took 22.5 minutes, making it over three times slower.

• Opus's longer time was attributed to its repeated visual testing cycles, identifying bugs through browser testing.

Bug Identification and Handling

• Codeex identified one issue (leaflet clustering incompatibility), pivoting quickly without extensive testing.

• In contrast, Opus encountered four or more runtime bugs but managed to fix them due to its thorough visual testing approach.

Accessibility and Performance Metrics

• Opus implemented accessibility features like area labels and keyboard navigation; Codeex did not address these requirements.

• The final bundle size for Opus met the target of under 500 kilobytes, whereas Codeex did not measure this aspect.

Test Results Summary

• In summary, Codeex built faster with fewer bugs but lacked thoroughness in testing compared to Opus's comprehensive approach that ensured functionality at the cost of speed.

Dev Mode Evaluation: Analyzing Both Builds

Issues Found in the Opus Build

• Upon loading in dev mode, the map on the Opus build broke when entering full-screen mode, displaying only a gray box.

Functionality Check for Other Features

• Despite issues with the map, other components like tabs and statistics worked correctly within the Opus build.

Success of GPT 5.3 Codeex Build

• The Codeex build functioned properly even in full-screen mode without breaking; all filters operated effectively.

Comparison of User Interface Elements

• Unlike Opus, which had an empty gray box issue on its map display, Codeex maintained a clean interface with scrollable information sections for earthquake data.

Conclusion on Model Performance

• Overall observations indicate that despite slower performance and more extensive bug identification processes by Opus, GPT 5.3 Codeex delivered a cleaner functional output quicker without any visual testing interventions.

Opus vs. Codeex: A Comparative Analysis of Build Performance

Overview of the Issue with Opus

• The lack of a scroll bar in Opus led to excessive empty space on the map side, causing layout issues. This was due to information being extended downwards rather than being confined to a specific area.

Performance Comparison: Codeex vs. Opus

• Codeex demonstrated superior performance by completing builds three times faster than Opus, which is significant for users building complex applications using text prompts. This efficiency is highlighted as a major advantage for Codeex.

Visual Test Setup

• A visual test was conducted where both models were tasked with recreating a landing page based on screenshots of the Stripe homepage, transitioning from written prompts to visual prompts. This test aimed to evaluate their ability to interpret and replicate design elements accurately.

Details of the Stripe Homepage

• The Stripe homepage contains various components including:

• Hero section with animated gradients.

• Solutions grid featuring product mockups.

• Dark stats block and sections for enterprise/startup showcases.

• Developer integration area and a multi-column footer at the bottom.

This complexity makes it an ideal candidate for testing model capabilities in visual reconstruction.

Execution of Visual Prompt Task

• Both models were instructed via minimal text prompts to study attached screenshots carefully and recreate the entire page while matching design elements such as layout, colors, typography, spacing, and interactive components identified from the images provided in their respective directories.

Results: Codeex Build Evaluation

• Codeex completed its build in approximately 3.5 minutes:

• The structure included all key sections but had larger font sizes compared to the original site, affecting overall aesthetics.

• While it captured titles and gradient backgrounds correctly, it failed to include detailed product UI mockups within solution cards; they remained empty despite having correct titles and styles.

Results: Opus Build Evaluation

• In contrast, Opus took about 8.5 minutes but produced more accurate results:

• Typography closely matched that of the original Stripe page; logos were represented correctly instead of just large text placeholders.

• It successfully created detailed product mock-ups within solution cards (e.g., phone payment terminals), showcasing greater attention to detail compared to Codeex's output.

Overall accuracy and detail were significantly better in this build when compared directly against Codeex's results.

Comparison of Opus 4.6 and Codeex in Building Web Pages

Overview of Model Performance

• The footer design in Opus 4.6 closely resembles the Stripe homepage, showcasing significant improvement over the previous Codeex version.

• Opus 4.6 is recommended for users who frequently utilize images and mockups, as it effectively translates screenshots into accurate working code.

Test Results: Quake Watch vs. Stripe Homepage

• In the Quake Watch test, Codeex outperformed Opus 4.6 by completing the build in 7 minutes compared to Opus's 22 minutes; Codeex also produced a functional map and scrollable earthquake list.

• The Opus build faced issues with full-screen map rendering and improper list containment, highlighting its performance limitations in this task.

Visual Comprehension Test Outcomes

• During the Stripe homepage rebuild, both models successfully identified all sections; however, Opus delivered superior visual fidelity with accurate typography and complete product mock-ups.

• Although Opus took two and a half times longer than Codeex, the additional time contributed to higher quality output.

Key Takeaways on Model Selection

• The results indicate that while Codeex is faster and effective for functional builds from written specifications, Opus excels in tasks requiring visual precision despite being slower.

• Both models have distinct advantages depending on project requirements—Codeex for speed and functionality versus Opus for detail-oriented UI tasks.