GPT 5.4 Tested: The Best AI Coding Model Right Now?

GPT 5.4 Tested: The Best AI Coding Model Right Now?

Three days ago I loaded an entire Laravel monolith — 847 files, roughly 120,000 lines of code — into a single GPT 5.4 context window. No chunking. No summaries. No "here's the relevant file" workarounds I'd been doing for years. Just the whole codebase, raw, dropped into one session.

Then I asked it to find a race condition in my queue worker that had been haunting production for two weeks.

It found the bug in ninety seconds. Not a guess. Not a "this might be the issue." It traced the execution path across four services, identified the exact moment two workers could grab the same job, and wrote a fix with a database-level advisory lock that I'd been too stubborn to consider. I stared at my screen for a full minute before I even tested the patch.

That moment — that specific, visceral "wait, what just happened" moment — is why I'm writing this post. GPT 5.4 isn't an incremental update. It's the first AI coding model that made me genuinely rethink how I structure my entire development workflow. But it's not perfect, and some of the hype around it is already getting out of hand. I need to separate what's real from what's marketing noise, because if you're about to reorganize your toolchain around this model, you deserve an honest assessment from someone who's actually stress-tested it.

The Million-Token Context Window Changes Everything About How You Work

I've been complaining about context window limitations since GPT-4's original 8K token cap back in 2023. Every AI coding tool I've used has required some version of the same dance: carefully select which files to include, write detailed summaries of the parts you can't fit, and pray the model doesn't hallucinate connections between code it can't actually see. With Claude Code and Opus 4.6, the context management got smarter — the tooling handles a lot of it automatically — but the fundamental constraint was always there. You were always working with a partial picture.

GPT 5.4 supports up to one million tokens of context. That's not a theoretical maximum buried in documentation. That's a usable, practical context window that I've filled with real codebases on multiple occasions over the past week.

Here's what changes when context stops being a bottleneck.

First, you stop thinking about what to include. I used to spend five to ten minutes at the start of every complex debugging session deciding which files were relevant. Sometimes I'd get it wrong, realize midway through that I needed a config file or a middleware I hadn't included, and have to restart the conversation. With GPT 5.4, I just load everything. The model figures out what's relevant. That decision fatigue disappears completely.

Second, cross-cutting concerns become trivial to analyze. Want to understand how authentication flows through your entire application? How a single environment variable propagates through configs, services, and deployment scripts? Questions that used to require me to manually trace paths across dozens of files — GPT 5.4 just answers them. It has the full picture. It can see the database migration, the model, the controller, the API route, the frontend call, and the test, all simultaneously.

Third — and this surprised me — the model's code generation quality improves dramatically when it can see more context. I ran an informal experiment: I asked GPT 5.4 to add a new feature to a project twice. Once with just the relevant files (about 15 files, maybe 3,000 tokens of code). Once with the entire codebase loaded. The second attempt produced code that matched my existing patterns, used my custom helper functions instead of reinventing them, and followed the naming conventions I'd established months ago. The first attempt produced generic, correct-but-foreign code. Same model. Same prompt. The only difference was context.

I should mention the practical constraint here: loading a million tokens isn't free. The API costs scale with input tokens, and a full codebase load on every interaction would burn through credits fast. For exploratory sessions and complex debugging, it's worth every penny. For routine "write this function" tasks, you're overpaying. I've settled into a pattern where I load the full codebase for architecture-level work and use smaller, targeted contexts for day-to-day coding. That balance feels right — but you'll need to find your own.

Computer Use: When Your AI Starts Testing Its Own Code

This is the feature that made me sit up straight. GPT 5.4 can interact with applications the way a human would — clicking buttons, reading screens, navigating interfaces. OpenAI calls it "computer use," and while the name sounds like marketing fluff, the capability is genuinely new territory for a coding model.

I tested this on a React dashboard I've been building. I asked GPT 5.4 to add a data export feature, and instead of just writing the code and handing it back to me, it wrote the code, opened the app in a browser, navigated to the dashboard, clicked the export button it had just created, verified that the CSV downloaded correctly, noticed the date formatting was wrong in one column, went back and fixed the code, then tested again. All without me touching anything.

Let me be real about the current state of this: it's impressive but not production-ready for complex testing workflows. The model handles straightforward UI interactions well — form fills, button clicks, navigation. But it struggles with applications that rely heavily on hover states, drag-and-drop, or complex animation timing. I had it try to test a Kanban board feature and it couldn't reliably drag cards between columns. It knew what it wanted to do. The motor skills weren't there yet.

Where computer use already shines is in the feedback loop it creates. Traditionally, the AI coding workflow goes: write code → manually test → report issues back to the AI → iterate. That middle step — the manual testing — is where most of the time goes. Not because testing is hard, but because translating visual results back into text descriptions for the AI is slow and lossy. "The button is there but it's positioned wrong" loses information compared to the model actually seeing the button.

GPT 5.4's computer use collapses that loop. The model writes, tests, sees the result, and iterates — all within a single interaction. For straightforward features, this cuts the development cycle from four steps to one. I built and tested a complete contact form — validation, error states, success message, email sending — in a single prompt. The model went through three iterations on its own before presenting the final version. Each iteration fixed real issues it discovered by actually using the form.

I expect this capability to improve rapidly. The underlying vision model is strong. The interaction layer just needs polish. Six months from now, I suspect computer use will be table stakes for AI coding models. GPT 5.4 got there first, and even in its current form, it's changed how I think about the testing phase of development.

The Reasoning Modes That Actually Matter

GPT 5.4 ships with selectable reasoning modes — "high" and "ultra-high" — and my initial reaction was skepticism. Reasoning modes felt like a marketing checkbox. "Our model thinks harder if you ask nicely." But after using both modes extensively, I've changed my mind. The difference is real, measurable, and worth understanding.

High reasoning mode is what you want for 80% of coding tasks. Writing functions, refactoring code, generating tests, explaining complex logic. It's fast, accurate, and reliable. The response times are comparable to what I've experienced with other top-tier models — you're not waiting around.

Ultra-high reasoning mode is for the problems that make you close your laptop and go for a walk. Architectural decisions with cascading implications. Debugging race conditions in concurrent systems. Analyzing security vulnerabilities across an entire codebase. Performance optimization where the bottleneck isn't obvious. These are problems where "thinking harder" actually produces different — and better — answers.

I ran a direct comparison on a real problem: optimizing a database query that was taking 14 seconds on a table with 2.3 million rows. In high mode, GPT 5.4 suggested adding an index and rewriting a subquery as a JOIN. Standard optimization advice. Correct, but I'd already tried both. In ultra-high mode, the same model analyzed the query execution plan, identified that the optimizer was choosing a hash join when a merge join would be faster given the data distribution, suggested a query hint to force the merge join, recommended a partial index on the filtered column, and pointed out that my WHERE clause was preventing index usage because of an implicit type cast I hadn't noticed. The ultra-high response took about twelve seconds longer. It saved me approximately three hours of manual query analysis.

The lesson I've taken from this: don't default to ultra-high. Start with high. Escalate when you're stuck or when the problem genuinely requires deeper analysis. Ultra-high mode uses more tokens and takes more time — it's a tool, not a setting to leave permanently enabled. But when you need it, the difference between "good suggestion" and "that's exactly the insight I was missing" is worth the wait.

GPT 5.4 vs Opus 4.6: The Honest Comparison Nobody Wants to Make

I use both. Daily. I'm not going to pretend one is categorically better than the other, because that would be dishonest and unhelpful. Here's what I've found after weeks of running both models on the same projects.

GPT 5.4 wins on backend complexity. For large codebases, complex debugging, multi-service architectures, and tasks that require holding vast amounts of context simultaneously, GPT 5.4 is currently the stronger model. The million-token context window is the obvious advantage, but it's not just about capacity. The model's ability to reason across distant parts of a codebase — connecting a database schema change to its ripple effects through an API layer, a frontend state management system, and a test suite — feels qualitatively different from what I get with other models.

Opus 4.6 wins on frontend and UI work. This surprised me, honestly. I expected GPT 5.4 to dominate across the board. But when I'm building React components, designing layouts, working with CSS animations, or iterating on visual design, Opus 4.6 consistently produces better results. The components look more polished. The CSS is cleaner. The design sensibility — and I realize I'm attributing aesthetic judgment to an AI model — feels more refined. I've tested this multiple times, switching between models on the same UI task, and the pattern holds.

Both models handle standard development work equally well. For the bread-and-butter tasks — CRUD endpoints, database migrations, unit tests, documentation, code reviews — I genuinely can't tell a meaningful difference. Pick whichever model fits your existing workflow.

Here's my current setup, in case it helps: I use GPT 5.4 through Cursor for backend work and complex debugging sessions. I stay in Claude Code with Opus 4.6 for frontend development, agent-based workflows, and anything that benefits from Claude Code's excellent project context management. When I'm starting a new feature that spans both frontend and backend, I typically begin with GPT 5.4 for the architecture and API layer, then switch to Opus 4.6 for the UI implementation.

Is this optimal? Probably not. Am I going to consolidate to a single model eventually? Maybe. But right now, the strengths of each model are different enough that using both produces better work than committing to either one exclusively.

Matt Shumer — CEO of HyperWrite and someone whose technical assessments I generally trust — called GPT 5.4 "essentially flawless" and said coding is "solved." I think that's roughly 70% correct. The model is astonishingly capable. But "solved" implies there's nothing left to improve, and I can point to at least a dozen interactions from this week where the model produced subtly wrong code, misunderstood a constraint, or needed multiple iterations to get something right. It's the best AI coding model I've used. It's not perfect. The gap between "best available" and "flawless" is still real, and it matters when you're shipping production code.

Getting Started Without Wasting Your First Two Hours

I wasted my first session with GPT 5.4 because I didn't understand the tooling landscape. Let me save you that time.

Step 1: Choose your interface. You have three primary options right now.

The Codex app is OpenAI's dedicated coding interface. Despite the name, the app no longer uses a separate "Codex" model — you select GPT 5.4 from a dropdown menu within the app. This confused me initially because I kept looking for a dedicated Codex model option that doesn't exist anymore. The app connects directly to your GitHub repositories, which means you can pull in your codebase without manual downloads. For project-level work — architecture reviews, major refactors, feature planning — the Codex app is probably your best starting point.

Cursor is where I do most of my GPT 5.4 work. If you're already a Cursor user, the integration is seamless — GPT 5.4 appears as a model option in the model selector. Cursor's strength is inline coding assistance: you're writing code, you highlight a block, you ask a question, you get an answer in context. GPT 5.4's speed and accuracy make this flow feel particularly smooth. If you're coming from VS Code with Copilot, Cursor with GPT 5.4 is a meaningful upgrade.

The API directly is the third option, and it's what I use for automated workflows. I have scripts that run GPT 5.4 for code review on pull requests, automated test generation for new files, and documentation updates when APIs change. The API gives you full control over context management, system prompts, and output formatting. It's more work to set up, but the flexibility is unmatched.

Step 2: Get your codebase ready. If your code lives on GitHub, both the Codex app and Cursor can connect directly. For Cursor, you just open your local project folder. For the Codex app, you connect your GitHub account and select the repository. If you're not using GitHub, download your codebase as a ZIP, extract it locally, and open it in Cursor. I recommend having a clean, up-to-date local copy regardless of which tool you use.

Step 3: Start with high reasoning mode. Don't jump straight to ultra-high. High mode handles 80% of coding tasks efficiently, and you'll get faster responses. Reserve ultra-high for when you're genuinely stuck on a complex problem — you'll know when you need it because high mode's suggestions won't be solving your actual problem.

Step 4: Test with a real task, not a toy example. The worst way to evaluate GPT 5.4 is to ask it to write a todo app or reverse a string. Those tasks don't exercise its actual strengths. Instead, take a real bug you've been putting off, or a feature that requires changes across multiple files, or a performance problem you haven't had time to investigate. That's where GPT 5.4 earns its reputation.

Pro tip: If you're migrating from another AI coding tool, don't try to replicate your exact workflow. GPT 5.4's context window is so much larger than what you're used to that your old habits — carefully curating which files to include, writing detailed summaries of your architecture — are unnecessary overhead. Let the model see everything. Let it figure out what's relevant. This feels uncomfortable at first, like letting go of the steering wheel. But the model navigates better with the full map than you do with a highlighted route.

The Honest Limitations Nobody's Talking About

I've been enthusiastic about GPT 5.4 throughout this post. Intentionally so, because the model deserves it. But I'd be doing you a disservice if I didn't talk about what's still broken, limited, or frustrating.

Front-end design quality isn't there yet. I mentioned this in the Opus comparison, but it bears repeating because so many people are going all-in on GPT 5.4 for everything. If you're building user interfaces — especially anything that needs to look polished, feel responsive, and handle edge cases in layout gracefully — GPT 5.4 will give you functional but visually mediocre results. The CSS it generates works. The components render correctly. But the design choices are safe, generic, and uninspired. Opus 4.6 is noticeably better here, and dedicated UI tools like Figma-to-code workflows still produce superior visual results.

The million-token window has a cost problem. I alluded to this earlier, but let me be explicit: loading 500K+ tokens of context on every interaction is expensive. On one particularly intensive debugging session, I burned through what I estimate was $15-20 in API credits over about three hours. That's fine for me — the bug I fixed would have taken me a full day without the model. But if you're a solo developer watching costs, you need a strategy for when to use full context and when to be selective.

Hallucinations haven't disappeared. They're less frequent with GPT 5.4 than any model I've previously used, but they still happen. Last Tuesday, the model confidently told me that Laravel 12 had introduced a Model::withoutTimestamps() method for bulk operations. It hadn't. The method doesn't exist. The suggestion was plausible enough that I spent fifteen minutes looking for it in the documentation before realizing the model had invented it. Always verify. Trust but verify. The model is smarter than any previous version, but it's still capable of creative fiction when it encounters gaps in its training data.

Long-running tasks can drift. In extended sessions — think two hours of continuous back-and-forth on a complex feature — I've noticed the model occasionally losing track of decisions made earlier in the conversation. It'll suggest an approach that contradicts something we'd already agreed was wrong. The million-token window helps because it can technically "see" the earlier discussion, but attention over very long contexts isn't uniform. Important decisions early in a long session can get less weight than recent messages. My workaround: for critical architectural decisions, I state them explicitly as constraints at the start of each major phase of work. "We've decided X. We're not revisiting that. Now let's work on Y."

No Anti-Gravity integration yet. If you're in the Anthropic ecosystem and use Anti-Gravity for orchestrating multi-model workflows, GPT 5.4 isn't available there yet. This might change — the AI tooling landscape moves fast — but as of today, using GPT 5.4 means stepping outside the Anthropic toolchain for those specific tasks. For me, this means maintaining two separate workflows, which adds friction.

I'm listing these limitations not to undermine the model but because I've seen too many "GPT 5.4 is perfect, coding is solved" takes online. It's the best AI coding model available right now. It's also a tool with real constraints that you need to understand before you build your workflow around it.

What This Means for the Next Six Months

I've been building with AI coding tools since the first GPT-4 API preview. Every few months, there's a model that forces me to update my mental model of what's possible. GPT 5.4 is one of those moments.

The combination of a million-token context window, computer use capabilities, and genuinely improved reasoning creates something that feels qualitatively different from what came before. It's not just a better autocomplete. It's closer to a junior developer who can read your entire codebase, test their own work, and explain their reasoning when you ask.

Here's my prediction, and I'll revisit this in six months: the next wave of AI coding tools won't compete on model quality alone. GPT 5.4 and Opus 4.6 have proven that multiple organizations can produce world-class coding models. The differentiator will be tooling — how the model integrates into your workflow, how it manages context, how it handles multi-step tasks, and how it recovers from mistakes. The model is the engine. The developer experience is the car. Right now, we have great engines in mediocre cars.

The developers who'll benefit most from GPT 5.4 aren't the ones who use it as a faster autocomplete. They're the ones who restructure their workflows to take advantage of its actual strengths: full-codebase reasoning, autonomous testing, and deep analysis of complex problems. That requires changing habits. It requires letting the model do things you're used to doing manually. It requires a level of trust that takes time to build — and should take time to build, because blind trust in any tool is a recipe for shipping bugs.

If you've been on the fence about AI coding tools, GPT 5.4 is the model that should push you off it. Not because it's perfect. Because it's good enough that not using it puts you at a measurable disadvantage against developers who do.

And that race condition in my queue worker? The one I spent two weeks debugging manually? GPT 5.4's fix has been running in production for five days now. Zero incidents. Zero failed jobs. The advisory lock approach was cleaner than anything I would have written myself, and it took the model ninety seconds to find a solution that had eluded me for fourteen days.

I'm not sure if that makes me feel brilliant for using the right tool, or humbled that the tool is better at my job than I am. Probably both. Definitely both.

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