OpenAI’s new GPT-5.4 is here, and on paper at least, it looks like one of their strongest all-rounder models so far.
What makes this model interesting is that OpenAI is not selling it as just a coding model, and not just a reasoning model either. They are basically pitching it as an all-round professional work model that combines strong reasoning, strong coding, better tool use, and much better performance on practical work like spreadsheets, presentations, etc.
makes sense, because it helps turn that mess into something models can actually work with.
And the specs are also pretty wild. GPT-5.4 supports a 1.05M token context window with 128K max output tokens, which is pretty good room to work with. All in all, this helps the model remember things better. Also, a thing to note is that the knowledge cutoff for this model is August 31, 2025.
Now, let's talk about the part we mostly care about.
On the official OpenAI benchmarks, GPT-5.4 scores 57.7% on SWE-Bench Pro (Public), which puts it basically side by side with GPT-5.3-Codex, a coding-focused model, at 56.8%. So yes, OpenAI says this general-purpose model is slightly better than GPT-5.3-Codex, a coding-focused model, which I personally have not had the best experience with compared to Claude models, and that is kind of wild to think about.
The standard GPT-5.4 model is listed at $2.50 per 1M input tokens, $0.25 cached input, and $15 per 1M output tokens. GPT-5.4 Pro is way more expensive at $30 input and $180 output per 1M tokens, and OpenAI says it can take several minutes on hard tasks, so that one is clearly for cases where you really want the best answer and are okay paying for it.
💁 The normal GPT-5.4 model is probably the one most people will actually care about day to day, and that's what I'd prefer.
And as always, benchmarks are benchmarks. But on paper at least, GPT-5.4 looks like one of the strongest all-rounder models OpenAI has shipped so far.
Quick Coding Test
As this is a general-purpose model instead of a coding-tuned model, comparing the model's ability solely on coding is just not fair. But as developers, we mostly care about how good the model is at coding anyway, so just to give you an idea of how this model performs, we will do a quick test.
Test: Figma Design Clone with MCP
In this test, we'll be comparing both models on a Figma design, a complex dashboard with so many things happening in the UI.
Here's the Figma design that I'll ask both models to clone:
Prompt:
Build a **pixel-accurate clone** of the attached Figma design frame using the **provided Next.js project** as the starting point. Do **not** create a new project. Instead, implement the UI inside the existing codebase.
https://www.figma.com/design/8quNKljV0spv67VAGsA75D/Dashboard-Design-Concept--Community---Copy-?node-id=69-123&t=Tvu2UB7UDMqkvPRb-4
Please match the design as closely as possible, with close attention to layout, spacing, alignment, typography, colors, borders, shadows, corner radius, and overall visual balance.
Requirements:
* use the existing **Next.js** setup
* keep the code clean and componentized
* make the page responsive without changing the intended design
* use semantic HTML where appropriate
* avoid adding your own design decisions unless necessary
* if any part of the design is unclear, make the most reasonable choice and stay visually consistent
Prioritize **design accuracy first**, then code quality.
GPT-5.4
GPT-5.4 pretty much one-shotted the entire implementation in one go, which was honestly nice to see. It did not need any follow-up prompt, no fixing, nothing. It just took the Figma frame through MCP and started building the whole thing right away.
The final result actually looked decent. I would not call it pixel-perfect by any means, but compared to Claude Sonnet 4.6, I’d say the implementation looked noticeably better overall. The whole thing feels more like a static picture of the design than an interface you can actually interact with.
Time-wise, it took roughly 5 minutes to get to a working to the working build.
Here’s the demo:
You can find the code it generated here:
After that, I gave it a quick follow-up prompt, and almost instantly, it fixed the issue and came back with a decent implementation.
As you’d expect, it did manage to clone the project structure and get the UI in place. And again, the same issue, there's just no functionality whatsoever. It just feels like a picture with no interactivity.
Here’s the demo:
You can find the code it generated here:
Token usage, based on Claude Code’s model stats, looked like this:
Input Token Usage: 84
Output Token Usage: 35.4K
And honestly, that’s the real takeaway here anyway.
These models keep getting better at a speed that is honestly hard to keep up with. So rather than getting too stuck on who won one benchmark, the better thing to do is probably to keep building, keep testing, and keep learning how to use these models better for your use case.
What do you think, is GPT-5.4 actually that good, or is Claude still your go-to? 👇
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