I built the same SaaS with GPT 5.4 and Claude Code. Here’s what actually happened after testing both on the same project.

Experimenting with AI: Building the Same SaaS Using GPT 5.4 and Claude Code – An In-Depth Comparative Analysis

In today’s rapidly evolving AI landscape, the choice of language model can significantly influence the efficiency, quality, and flexibility of your projects. As an AI developer and enthusiast, I recently undertook a hands-on experiment: creating an identical micro SaaS application using GPT 5.4 and Claude Code. This article outlines my process, observations, and insights gathered from this comparative venture.

Daily Operations with Claude Code

I maintain a production system powered by Claude Code, utilizing over 30 custom skills that automate a wide array of tasks—including lead outreach, email personalization, trend analysis, thumbnail generation, and PDF report dispatches via Telegram. My system’s memory is anchored in an Obsidian vault organized under the PARA methodology, serving as a persistent knowledge base. Each project activity, decision, and log entry is meticulously tracked, allowing agents to seamlessly pick up where they left off each day.

Transitioning to GPT 5.4

With the release of GPT 5.4, I was curious whether a similar setup could be replicated on a different engine. To test this, I duplicated my Claude Code skills directory by symlinking it into the GPT environment via the Codex app, effectively constructing an identical micro SaaS from scratch.

A Quick Overview of GPT 5.4

Before diving into the build process, it’s valuable to understand some of the key specifications of GPT 5.4:

Coding Proficiency: GPT 5.4 scores approximately 57.7% on SWE-Bench Pro, whereas Claude Opus 4.6 achieves around 80.8%. This indicates Claude’s dominance in raw code generation.
Computer Use: GPT 5.4 exceeds human expert performance with a 75% score on OSWorld, surpassing GPT 5.2 by 28 points.
Context Window: Supports up to 1 million tokens — a substantial increase enabling larger context handling.
Tool Search Efficiency: GPT 5.4 retrieves tool definitions on demand, reducing token consumption by nearly 47%.
Benchmarking (GDPval): Achieves 83%, matching industry professionals across 44 occupations, up from 71% with GPT 5.2.
Pricing: Approximately half the cost per million tokens compared to Claude — $2.50 vs. $5.00 in input tokens and $15 vs. $25 in output tokens.
Performance in Chatbot Arena: Claude remains somewhat ahead in both text and code challenges, with GPT 5.4 still under evaluation.

Overall, GPT 5.4 impresses with cost efficiency and large context capacity, but Claude still leads in raw coding benchmarks.

Building the Micro SaaS: A Practical Test

Project Overview: I aimed to develop an AI-powered Review Response Generator tailored for local businesses. Users search for their company, fetch Google reviews, and generate professional replies—an application I previously built with Claude Code.

Development Approach: Using the same project blueprint, I recreated the application with GPT 5.4 via the Codex platform. The core components included:

Landing page with Google Places API integration
Review fetching and rendering
AI-generated responses
User authentication via magic links
Backend management with Convex
Deployment on Cloudflare Pages

Build Conditions: Operating on the $20/month Codex plan, I was initially unsure whether GPT 5.4 could complete the build within resource limits, especially considering its advanced steerability feature, which allows mid-process corrections and refinements.

The Process and Key Observations

Planning and Execution

GPT 5.4 generated a comprehensive project plan, detailing:

Monorepo creation and architectural decisions
Scope definition covering MVP features
Logging every step before and after implementation
Technologies: Next.js, Vercel AI SDK, Convex database
Security with magic link authentication
SEO optimization and testing

Remarkably, the model also included writing unit tests unprompted—a detail seldom achieved with Claude Code without explicit instructions.

Deployment Speed

The entire deployment, from initial setup to live deployment, took approximately 45 minutes. Notably, GPT 5.4’s improved processing speed contrasted significantly with earlier Codex versions, which were considerably slower. During this process, I observed:

Efficient file creation and integration
Effective utilization of documentation and design skills loaded at appropriate times
Deployment on Cloudflare with minimal issues

Rate Limiting Insights

While working with Codex, I noticed its dynamic rate-limiting window, which shifted the remaining token quota rather than imposing a fixed reset period. This behavior, potentially a bug or an intentional design choice, allowed for more flexible usage during intensive building sessions.

The Power of Persistence

What truly stood out during this project was GPT 5.4’s persistence. When encountering obstacles—such as a misconfigured Convex setup or a failed login flow—it proactively tested and debugged itself, including internally verifying the magic link authentication. It even adapted to testing modes (like headed browser automation with Playwright), diagnosing issues, and fixing bugs without manual intervention.

In contrast, Claude Code demonstrated a good troubleshooting ability but tended to halt or report problems more readily. GPT 5.4’s continuous operation and adaptive troubleshooting resulted in a more resilient development process.

QA and Final Refinements

Using Playwright with the integrated interactive skill, the model performed end-to-end testing of the deployed app, including the login flow. It automatically simulated user interactions, identified issues, and patched errors—reducing the need for manual QA testing.

Following this rigorous testing and fix phase, the SaaS app achieved:

Real Google Business search results displayed
Fetching and presentation of authentic reviews
Functional AI-generated replies
Persistent business data and customizable brand profile settings
Additional features such as tone selection and branding options added spontaneously

Efficiency & Resource Usage

After roughly two hours of development, including build, testing, and troubleshooting, the system utilized about 85% of the weekly token quota and 50% of the five-hour limit on the $20 plan. This demonstrates that with efficient skill management and process optimization, substantial projects can be completed economically.

Comparative Summary

While Claude Code still surpasses GPT 5.4 in raw coding benchmarks and complex nuanced instruction following, GPT’s resilience and persistence are game changers.

Final Insights

This experiment underscored a fundamental truth: the choice of AI model is less critical than the surrounding system and skills architecture. My core assets—project files, organizational workflows, and skill configurations—remained consistent, enabling me to switch engines swiftly and efficiently.

Developing well-structured skills and workflows first, then selecting the best available engine, is a scalable strategy. The recent advancements in GPT 5.4—especially its persistence, speed, and cost-effectiveness—make it a formidable contender, even if Claude still holds advantage in certain benchmarks.

Closing Thoughts

The AI development landscape continues to evolve rapidly. Staying adaptable, emphasizing system design, and building modular, reusable skills will serve developers well. As models improve, the synergy between human expertise and AI capabilities will unlock unprecedented productivity.

What are your thoughts on leveraging these advancements? Are you ready to experiment with similar setups in your projects? Share your experiences and insights in the comments.

Published by [Your Name], AI & Software Development Enthusiast

Holidays in Europe