Why GPT 5.4 Failed My Production Test: A Deep Dive Into Claude Opus 4.7 And OpenSpec SDD For Professional Coding

The world of generative AI moves at a pace that often leaves developers feeling like they are constantly playing catch up. Just when we get comfortable with a specific workflow, a new model drops or a token limit forces us to pivot. Recently, I found myself in exactly this position. Having relied heavily on Claude Opus 4.7 for several months, I hit a wall. Due to heavy token utilization, I was locked out for a week, which prompted me to return to the OpenAI ecosystem to test Codex with the newly released GPT 5.4.

My primary workflow revolves around Spec Driven Development, also known as SDD, utilizing the OpenSpec framework. If you are not familiar with SDD, it is essentially a methodology that treats the specification as the primary source of truth, making the development process LLM agnostic. Whether you are working on a Greenfield project, starting from scratch, or a Brownfield project, managing a massive existing codebase, SDD provides a structured way to ensure the AI understands the architectural intent before a single line of code is written.

Getting Started With OpenSpec SDD

Before diving into the comparison, it is worth explaining why OpenSpec has become my go-to tool. It simplifies the bridge between high level requirements and production grade code. To get started, you can visit the Fission AI GitHub repository for OpenSpec, but the actual implementation is remarkably straightforward.

First, you need to install the package globally on your machine using the following command in your terminal:

npm install -g @fission-ai/openspec@latest

Once the installation is complete, navigate to any project folder where you want to begin development. In the terminal, simply run:

openspec init

This command triggers a setup wizard. You will be prompted to select your preferred models and IDEs. This is a critical step because OpenSpec adapts its "skills" based on the environment you use. After you make your selections and press enter, there is one non-negotiable step: you must restart your IDE. This ensures that the newly injected skills and environment variables are properly recognized.

Once you are back in your editor, you can access the full suite of supported tools by typing /opsx in your terminal or chat interface. This opens up a library of slash commands designed to handle everything from architectural proposals to granular task execution.

The Shift From Claude To GPT 5.4

When my Claude tokens ran out, I transitioned my existing OpenSpec specs over to Codex powered by GPT 5.4. One of the major selling points of OpenSpec is that it can maintain continuity across different models. If an LLM stops mid-spec due to a token limit or a crash, another model should, in theory, be able to pick up the artifacts like the proposal or task list and continue the work.

I pointed GPT 5.4 to my existing spec name. It began the analysis phase, looking at the previous progress and the artifacts generated by Claude. However, this is where the experience began to sour. While Claude Opus 4.7 has consistently provided a smooth experience, following complex instructions with surgical precision, GPT 5.4 felt like it was struggling to keep its head above water.

In a production environment, "close enough" is not good enough. Claude has a reputation for generating production grade code that respects the established architecture. In contrast, my experience with GPT 5.4 was a series of many misses compared to very few hits. The most frustrating aspect was its inability to follow instructions that were clearly defined at both the local spec level and the global spec level.

The Reactive Bug Fixing Problem

One of the hallmarks of a high quality coding assistant is its ability to recognize patterns. If I point out a bug in one component, a sophisticated model should logically conclude that similar patterns in other files also need fixing. This is what I refer to as reactive bug fixing.

Claude excels at this. It understands the context of the entire project. If I fix a state management issue in one view, Claude often suggests or automatically applies the same fix to related views. GPT 5.4, however, lacked this intuition. It would fix the specific line I pointed to but leave identical bugs scattered throughout the rest of the codebase. This forced me into a tedious cycle of manual pointing, which defeats the purpose of using an advanced SDD framework like OpenSpec.

The Styling Test: React Native Size Matters

To truly test the reasoning capabilities of GPT 5.4, I gave it a very specific set of rules for a mobile project I was developing using Expo. I was using the react-native-size-matters library, which is essential for creating responsive designs across different screen sizes. I provided the following strict rules for the LLM to follow:

Use ms (moderate scale) for containers with the same width and height.
Use ms for all font sizes.
Use ms for all border radius values.
Use ms for padding and margin.
For individual style attributes: if it relates to top, height, or bottom, use mvs (moderate vertical scale). If it relates to left, width, or right, use ms.
Use ms for paddingHorizontal and marginHorizontal. Use mvs for paddingVertical and marginVertical.
Always use mvs for line height.

These rules are not particularly complex for a human, but they require consistent attention to detail across dozens of files. Claude Opus 4.7 handles this with roughly 99 percent accuracy. It understands the spatial logic of why a vertical margin requires a vertical scale while a horizontal one does not.

When I handed these same rules to GPT 5.4, the results were disappointing. Despite taking a significant amount of time to "think" and process the request, the output was riddled with inconsistencies. It would use ms for height in one file and mvs in the next. It ignored the line height rule entirely in several components. Even when I toggled the model to High or Max Reasoning modes, the improvement was negligible. It simply consumed more tokens while delivering the same flawed results.

Why Production Grade Matters

When we talk about production grade coding, we are talking about reliability. As a developer, I do not want to spend my time double checking whether the AI remembered to use the correct scaling function for a border radius. I want to focus on the business logic and the user experience.

The failure of GPT 5.4 in this context highlights a growing gap in the LLM market. There is a difference between a model that can write a clever Python script and a model that can act as a reliable co-engineer in a complex, multi-file architecture. Claude Opus 4.7 feels like the latter. It respects the boundaries set by the OpenSpec SDD approach. It treats the spec as a set of laws, not suggestions.

GPT 5.4 felt more like a distracted intern. It had the knowledge, but it lacked the discipline to apply that knowledge consistently across a large task. The fact that it struggled with pattern recognition and rule adherence even with Max Reasoning enabled suggests that the underlying architecture might still be lagging behind Anthropic's flagship model when it comes to long form, structured coding tasks.

Market Sentiment And Technical Outlook

It is important to note that the AI landscape is incredibly fluid. The performance of these models can change with a single update or a tweak to their system prompts. Furthermore, expected release dates for model improvements and specialized coding branches are always subject to change. What is true today might be disrupted by a new version tomorrow.

However, based on the current state of these tools in mid 2026, the choice for serious developers is clear. While OpenAI has made strides in speed, they have seemingly sacrificed the deep, logical consistency required for professional grade software engineering.

Final Verdict

After a week of struggling with Codex and GPT 5.4, I can safely say that I will be sticking with Claude Opus 4.7 for all my OpenSpec SDD projects. While Claude might require a bit of oversight to optimize certain functions, its ability to follow complex, multi-layered instructions is unparalleled. It creates code that is not just functional, but maintainable and architecturally sound.

If you are a developer looking to integrate AI into your production workflow, do not be swayed by the hype of higher reasoning modes or brand names. Test the models against a strict set of rules and see which one actually lightens your workload rather than adding a new layer of "AI proofreading" to your daily routine. In the battle for the best code generation model, Claude is currently the undisputed winner.