GPT 5.4 First Test Results

Today on the AI Daily Brief, GBT 5.4 is here, and these are both the first impressions

from the broader world as well as my first test results.

The AI Daily Brief is a daily podcast and video about the most important news and discussions

in AI.

Alright, friends, quick announcements before we dive in.

First of all, thank you to today's sponsors, KPMG, AIUC, Blitzy, and Prompt QL.

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Lastly, as often happens, when we have a big new model release, no headlines today, we

are just going to spend all of our time on this exciting new model.

So without any further ado, let's dive in.

After a couple of weeks where mostly we've been talking about big macro issues like the

Pentagon and Anthropic and all of that sort of thing, we finally have the cool fresh

breeze of a new exciting model to test.

And this one, indeed, is pretty exciting.

Ethan Molyc tweeted, I think we've been through enough release cycles for models at this

point to say that the latest model from OpenAI or Anthropic or Google is generally going

to be the best model in the world upon release, with some jagged edges until the next release

by one of the big three.

Now with that background, a different way to look at where we've been is that it's

simply been OpenAI's turn.

However, the expectations coming into GPT 5.4 were a little bit higher than they might

have been for some of OpenAI's more recent releases.

Ever since the release of GPT 5, all the big model providers got the memo that trying

to promise too much in each update rather than just being very incremental was a pretty

scary proposition.

That's what got us the 5.1, 5.2, 5.3, now 5.4 kind of paradigm.

But of course, it's not just OpenAI doing that, Google and Anthropic are both on that

same plan as well.

And yet, even with that, 5.4 has had a little bit more hype and anticipation around it than

some of the previous iterative models that we've gotten more recently.

This was theoretically supposed to be the big outcome of OpenAI's code red which was launched

back in December.

And what's more, the buzz for the last week or week and a half or so has been that this

one was really meaningful.

Enough so that it almost felt to me like some of the more recent leaks to publications

like the information were almost trying to tamp down on expectations.

To take one example, rumors had been flying that there was a 2 million token context window,

whereas the information's reporting from the last couple of days suggested it was just

1 million.

Seemed to me to be a little bit of expectation setting through leaks.

In any case, on Thursday afternoon we actually got the model.

And the initial buzz was strong.

Ben Hylac wrote, I've been using GPT 5.4 for the past few weeks.

In a sea of endless model drops and benchmark maxing, this model is the first and a long

time to be worth your time to try.

Honestly, didn't expect to open AI to pull this off.

So let's talk first about how OpenAI frames things, look at some of the early reactions

in the community, and then we'll walk through a more comprehensive case study with a project

that I recently did to put the new capabilities through the ringer.

Now one of the interesting things about this week is that this was not the first new OpenAI

model we got.

Just a couple of days ago we got GPT 5.3 instant, although OpenAI started promising almost immediately

that 5.4 was coming.

5.3 instant was as we've talked about a speed and personality play.

The announcement tweet called it more accurate, less cringe.

This actually was part of the inspiration for our episode about what's going to actually

matter in consumer AI, as this was so clearly aimed at that default sort of experience

that the average chat GPT user is going to have because they're not optimizing the model

selector for what they think they want.

GPT 5.4, although still having a lot of offshoots, does feel like they're trying to bring

together their models under a coherent banner.

On the right, GPT 5.4 brings together the best of our recent advances in reasoning, coding

and agent workflows into a single frontier model.

It incorporates industry leading coding capabilities of GPT 5.3 codex, while improving how the

model works across tools, software environments and professional tasks, involving spreadsheets,

presentations and documents.

The result is a model that gets complex real work done accurately, effectively and efficiently,

delivering what you ask for with less back and forth.

In other words, if 5.3 instant was for the personal use cases, GPT 5.4 was, as the subheader

says, designed for professional work.

As the information had it in that leak, we do indeed get a 1 million token context window,

which among other things, improves 5.4's ability on tasks that require longer thinking.

The quotes from early testers that they included were ravishing, even more than the ones that

they usually include.

Brendan Fudy, the CEO at Mirkoire Rights, GPT 5.4 is the best model we've ever tried.

It's now top of the leaderboard on our Apex agents benchmark, which measures model performance

for professional services work.

It excels at creating long horizon deliverables such as slide decks, financial models and legal

analysis, delivering top performance while running faster and at a lower cost than competitive

frontier models.

Indeed, these professional tasks are some of the big focus for open AI's announcement blog.

They compare GPT 5.4's outputs to GPT 5.2's on spreadsheets, documents and presentations,

all the significant upgrades.

And if overall knowledge work is what open AI chose to focus on, there was also a big

emphasis on computer use capabilities.

We are, of course, living in an open-claw world, and so it makes sense that this is becoming

increasingly important.

Computer users actually listed above coding, perhaps because the jump from 5.3 codecs

to 5.4 isn't all that big.

Indeed, they basically said that 5.4 is the integration of 5.3 codecs with other improved

aspects of the model, meaning it seems to me that their latest round of big coding innovations

was embedded in 5.3 codecs itself.

There's a sub-theme running throughout the announcement about efficiency, and the

first part of the announcement posts they write that GPT 5.4 is our most token efficient

reasoning model, using significantly fewer tokens to solve problems when compared to

GPT 5.2, translating to reduce token usage and faster speeds.

In the coding section, they also talk about fast mode in codecs, which they say delivers

up to 1.5x faster token velocity.

They write, it's the same model and same intelligence just faster.

That means users can move through coding tasks, iteration, and debugging while staying

in flow.

The new efficiency gains also show up in tool search.

They write, previously, when a model was given tools, all tool definitions were included

in the prompt up front.

For systems with many tools, this could add thousands or even tens of thousands of tokens

to every request, increasing cost, slowing responses, and crowding the context with the

information the model might never use.

With tool search, GPT 5.4 instead receives a lightweight list of available tools, along

with a tool search capability.

When the model needs to use a tool, it can look up that tool's definition and append

it to the conversation at that moment.

This approach, they say, dramatically reduces the number of tokens required.

They evaluated 250 tasks from scales, MCP atlas, and found that this new configuration had

the same accuracy but reduced total token usage by 47%.

That combined with improved accuracy on tool calling obviously makes it really appealing

for agent use cases.

There's a few other parts that the announcement, including improved web search and improved

durability, but those are the big hits.

And as people dug in, there were a few things that stood out.

First of all, that efficiency is showing up in people's tests.

Greg Kamrat, the president of ARC Prize, said that on ArcAGI 2, they were seeing a consistent

20% point lift versus 5.2 at the same price.

Still the three benchmarks that people were most discussing were around coding, computer

use, and GDPVAL.

On coding people flagged that GPT 5.4 was only nominally better than 5.3 codecs on benchmarks

like SWEBench Pro, but most people understood that that wasn't the core value proposition

of this updated model.

The computer use improvement got more of a discussion.

This is maybe the most concerned I've ever seen people with computer use, and I think

that's the difference between the pre and post open-claw world.

Now that people have got all these Mac minis running around with their open-claw agents

having pretty much unfettered access to them, how good the models are at using the computer

becomes much more relevant in a day-to-day way, not just a theoretical way.

Rahul Agarol writes, GPT 5.4 is here and it can use a computer better than a human.

In AI, ship GPT 5.4 on March 5th.

The headline isn't the reasoning improvements, it's that this is their first general-purpose

model with native state-of-the-art computer use.

It can operate websites and software autonomously, issue keyboard and mouse commands, write and

execute code, and navigate full desktop environments.

On OSworld verified it hits 75%, which is above human level performance at 72.4%, and

a massive jump from GPT 5.2's 47.3%.

That's not incremental, that's a step change.

When agents can reliably navigate desktops, the bottleneck on automation shifts from

can the model do it, to do you trusted enough to let it.

That's the question nobody has a good answer to yet.

Jimmy Cuff from PACE wrote an ex article about this specifically.

He called it, we stress tested GPT 5.4 on the hardest UI on the internet, and writes,

people don't realize how good AI computer use is actually gotten, until they see it tackle

the hardest UIs in existence, legacy insurance portals.

At PACE, we build AI agents for insurance workflows like submission and taken first

notice of loss.

Because we operate in this space, we use legacy enterprise software as our ultimate benchmark.

If AI can reliably navigate a 20-year-old hyperdense insurance portal without hallucinating

a click, it can navigate anything.

For a long time, the technology just wasn't there, but after spending the last few months

working closely with OpenAI to stress test their new GPT 5.4 model inside these environments,

it became clear the paradigm has shifted.

All things they point to as much better, the first is click accuracy.

Historically, Jamie writes, the biggest failure point was simply clicking the right thing,

enterprise software is incredibly dense, layouts are cluttered, buttons are tiny, and the

systems were designed decades ago.

Earlier models would frequently miss targets.

GPT 5.4 is vastly better at grounding itself visually, clicking precisely where it needs

to, even on a crowded screen.

They also point out improvements in long trajectory reasoning, speed and time to iteration, as

well as memory.

Now there was also a lot of chatter around GDPVAL, basically around the measure of the

model's performance on professional work tasks.

GDPVAL remember tests against knowledge were expanding 44 occupations from the top nine

industries that contribute to US GDP.

The win rate versus industry professionals was 49.8% for GPT 5.2, 60% for 5.2 pro, and

between 69.2% and 70.8% for the GPT 5.4 family.

That's just wins when it comes to ties, that number rises to 82 to 83%.

Ethan Mollock pointed out what this means in terms of time savings.

He wrote, given the GDPVAL benchmark for GPT 5.4, the new model ties or beats humans is

judged by other experts at professional tasks 82% of the time.

If you give a seven hour task to AI, even with failure rates in the need to check results,

you'd save four hours and 38 minutes on average.

And in addition to the general performance increases across the GDPVAL set, it's very

clear that open AI is aggressively going after certain industries even more.

COO Brad Lightcap, for example, tweeted, the team worked extremely hard to make GPT 5.4

great for finance.

It's much improved for financial modeling and analysis, integrates directly into Excel,

and connects to Fectivia, Dilupa, S&P Global, and many more.

It does feel like a codex moment is coming here.

But what about the overall impressions outside of the individual benchmarks?

Layton Space wrote, we've learned to take for granted that open AI is the smartest

kid in the room, always reporting state-of-the-art eVALs, but this set of updates feels much more

substantial and confident than any open AI launch in recent history.

The every vibe check summed it up, open AI is back.

Three months ago writes the team at every, open AI was losing the agent to coding race.

Cloud code had captured developer's hearts, and Opus 4.5 was shipping at a level other

models couldn't touch.

Meanwhile, open AI's coding agent codex felt like it was built for an older era of coding

with AI.

It was precise but rigid, powerful but personalityless, and not good with tools are able to run

for long periods of time autonomously.

Open AI's latest model released GPT 5.4 along with their other recent releases GPT 5.3

codex, GPT 5.3 codex Spark, and the codex desktop app shifts the competitive balance

back towards open AI on the coding front.

The new model produces plans that are thorough and technically precise, and have a user-focused

and human feel that has been missing from open AI's previous coding models.

In our testing GPT 5.4 reviews code with more depth than GPT 5.3 codex, and has a noticeably

more conversational voice.

With a few tweaks, it became our preferred model to use in our open clause, especially

given that it is half the price of Opus 4.6.

Even Kieran Klassen, our diehard Cloud Code devotee, is now reaching for GPT 5.4 daily

since we started testing it a week ago.

As ever they say, there are trade-offs.

GPT 5.4 has a tendency to expand the task well beyond what you asked for, and to call

tasks done before they're finished.

It sometimes completed tasks in obviously wrong ways, then lied about it.

The bigger story here is Open AI's trajectory.

From the codex desktop app to GPT 5.3 codex and to GPT 5.4, the company is iterating fast,

and many members of the team now uses tools and models daily for coding, a significant

change from a few months ago.

A couple of things that they said they liked about it, it did proactive research without

being asked, it had a more human voice than previous codexes, and it was roughly twice

as fast as Opus.

What they didn't like included scope creep on multi-step conversations, misreading completion

reports in OpenClaw, and over-engineering.

One of their team members called it too eager, adding things that aren't wrong, but aren't

necessary.

Ultimately, they summed it up with their title, Open AI is back.

Other people who reported their own experience also had positive things to say.

Dr. Daria Anute-Maz wrote, I asked GPT 5.4 pro to write a 10,000 word literary article

on my beloved T-cells, as I read through it, I am both mesmerized and deeply emotional.

It's just so beautifully written.

When I was tweeting about what I found to be 5.4's over-verbosity, click Health, Simon

Smith wrote, counterpoint, it's the best writing model from Open AI I've seen, and probably

better than the best clawed models now at writing, and only needs a bit of nudging to write

extraordinarily well.

I've done a ton of writing tests with 5.4 now, and it's capable of writing every imaginable

way with great empathy, creativity, wit, and decision, and it has personality again.

Now IAI's Claire Voe wrote, what I love speaks more like a human than 5.2 and 5.3, a million

token context window, tool use is Chef's Kiss, first model where I've experienced the

true go-investigate-and-fix experience that feels robust.

What needs work, basic frontend and UX taste, still loves a bullet point, latency, and stability.

The next day she updated it and said she's, quote, starting to have loving feelings towards

the model.

Basically, it did some really difficult tech and data work really well without a lot

of support.

Matt Schumer, who you might remember from something big is happening, calls GPT 5.4,

in short, the best model in the world by far.

On coding, he writes, coding capabilities are ridiculous.

It's essentially flawless.

Inside codex, it's insanely reliable.

Coding is essentially solved.

There's not much more to say on this.

It's just that good.

Now, he did find weaknesses, including frontend taste, which is something that I'm going

to get into in just a moment as well.

Mark Tenon holds from perplexity also pointed out that while the model itself is great,

the updates to the actual codex CLI experience are really good as well.

He called them the real hero.

So much less friction than the previous approval system, he says.

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So as we round the corner, let's actually shift into the test that I did.

I wanted to do something that was actually kind of difficult.

And that was also real.

I've been building a bunch of agents recently with Cloud Code, and the specific thing that

I was interested in building with Codex and 5.4 was some type of experience to help people

show off their agent building and agent orchestration skills.

Right now, you know that we have a couple different self-directed experiences for people

to learn how to use tools like OpenClaw.

And one of the things that I've been recognizing around that is that this new skill set of

agent building and agent orchestration is going to be more and more in demand from all sorts

of different types of organizations.

However, it is really this new types of skill set.

It's not easy to describe necessarily.

It's not easy to show necessarily.

And the people who are going to be hiring or contracting for it aren't necessarily going

to be really easily able to describe exactly even what they're looking for.

They're just going to want people who can help them agentify things.

And so I wanted to build some type of experience that could help that gap, that could help

builders show off what they built in ways that were understandable and accessible to other

people outside of them, and that could help people who were potentially interested in

working with builders actually engage in a way where they weren't just totally blind

in terms of finding the right people to help them with whatever agentification needs

they had.

So this is not an easy feat, and it also seemed to be a great way to test both 5.4 as

a model inside Chatchee BT as well as the updated Codex.

Now before we get into how it did, I will say that I also tested 5.3 instant as a partner

in helping me get Codex set up on a new machine.

Overall I think 5.3 instant is a big upgrade.

It's less annoying than 5.2, which I could get into more, but honestly just 5.2 was really

annoying.

It wouldn't answer things and then it would be cloying in its responses.

It's just a very unpleasant model to interact with.

5.3 is way better, it's much faster, and much easier to use for these not so intellectually

heavyweight types of tasks.

Two things that I have noticed with 5.3 instant however, one is that it puts too much into

each response.

When I said I need help setting up Codex and figuring out how to get the most out of it,

it wrote this like 18 page tom with all sorts of steps basically giving me a full guide

to Codex instead of working our way step by step.

In fact one of the things that I would recommend if you are using it in this way is to ask

it to go step by step.

This over verbosity as you will see is one thing that I think is consistent with this new

set of GPT models.

I also noticed 5.3 instant doing this very weird clickbaity thing.

Where when it has something to add at the end of a conversation, instead of just saying

it, it gives you this weird almost clickbait type final sentence.

For example, if you want I can also show you the 5 Codex workflows that technical founders

are using to build products insanely fast right now.

Later on it says, once that works I'll show you something important.

The 32nd Codex workflow that makes it feel like you suddenly have a junior engineer

working for you.

It's like they studied at the school of clickbait linked in and X article headlines and

brought that into chat GPT in a way that is just really really weird.

Still over all those complaints aside, 5.3 instant is a big upgrade in a lot of ways especially

for more simple types of needs.

Now moving over into the planning stage, I used 5.4 thinking admittedly just on the standard

setting and perhaps the results would have been very different if I had used more extended

thinking or even pro.

Even though this isn't the instant model, it is extremely fast compared to previous

thinking iterations.

In fact, it's so fast that it actually took some harnessing to slow it down and have

it be actually useful.

I described the problem that we were trying to solve in the same way that I described

it to you and it immediately dove in trying to spec the thing out before we even discussed

it.

The problem was that this wasn't just over eager, it was that it fell into some default

patterns based on its training data rather than really considering what I was trying

to do differently.

Specifically, in this case, it jumped right into assuming that if we were talking about

matchmaking between a company and an agent orchestrator, we really needed to focus on

technical skills.

Even though, of course, the whole point is that we're dealing with a world where technical

skills are being brought by the tools and so it's a different type of skill set that

we're actually trying to understand.

In addition to that, almost immediately I started to notice another issue, which is

that 5.4 thinking was extremely oververbose.

There's a point of repeating itself.

Even when it's not required, like when it's agreeing with you about something that you

already said, it uses a million lists, bullets, letter lists, numbered lists, all in the

same response.

And maybe this sounds like I'm complaining about it doing too much, which is maybe a better

problem to have, but the way that it operates honestly puts a huge cognitive burden on

the promsir.

Now, like I said to be fair, I could have changed it from thinking standard to thinking extended

to see if that slowed it down a little bit and improved things, but this was a set of

issues that I was dealing with.

And as I asked it to focus on our back-and-forth conversation, it was still trying to update its

build spec at the same time.

When it came to those biases that it was bringing into this conversation, that speed made

it harder to undo.

I didn't get it to slow down until I said, kind of annoyed, did I at any point ask you

to immediately start building a spec?

Finally, we got it to engage in the way that I wanted, and at that point I started running

into a new set of challenges.

One, it was still going incredibly long, and I found myself skipping over huge amounts

of words that it was saying because there was just so much to read.

The next thing that I noticed was that it was really eager to stay in planning mode,

whereas way earlier in the conversation, Claude would have started building artifacts.

GPT-5-4 just wanted to go deeper and deeper and deeper in planning in ways that I think

were wildly over-optimizing, relative to just starting to build the thing.

After one single line response where I told it what direction I wanted it to go, it agreed

and then wrote hundreds of lines with multiple bulleted lists, just confirming what we had

already talked about.

Now, I will say that once we honed in on the core idea, it was pretty smart and was able

to stay focused on the things that made what we were trying to build different, but I was

still having real problems trying to get it to go into do the actual work mode.

Even when I tried to shift it into visualization mode, it kept trying to plan the visualization

rather than just visualize it.

Again, I finally had to say, why aren't you just designing it?

Claude would have showed me five versions by now.

It responded, fair hit, I stayed too long an abstraction when I should have started showing.

And then you would think that at that point it would start doing the showing, but instead

it said, here are five concrete interface directions for builder rep, all built around

blah, blah, blah, blah, blah, blah.

I had to literally stop it and say, no, I'm not saying describe it, I'm saying go build

the clickable prototype.

Which it finally did, but then had its own problem, it was just awful visually.

And this is something that lots of other folks pointed out.

Ben Davis who works with Theo on his YouTube channel about AI said, it is hilariously bad

at UI stuff.

At Schumer said, front end taste is far behind Opus 46 and Gemini 3.1 Pro.

Why is this so hard to fix?

I'm not trying to rip on it too much here, but it is just honestly staggering.

How bad and tasteless the UI design is in my little experience.

I had to bring it back over to Claude and do the front end design there.

Claude pulled no punches in critiquing what we had gotten from five four.

The card backgrounds are muddy gradient blobs.

The colors are dull and washed out.

The typography has no hierarchy.

The tags look cheap.

The cards have no breathing room.

The whole thing looks like a dark mode template from 2023.

Brutal, but all very true.

Still after a while, we use Claude to get the design under control, and we're off to

the races inside the Codex CLI.

And this is where things start to turn around, and where you start to see why many folks

are so excited about this new model.

There were some not perfect things about the experience with Codex.

It had a couple weird foibles like it pushed me to use GPT 4.1, but I found similar weirdness

like that with Claude code as well.

I confirmed that it wasn't just five four in chat GPT, but also in Codex that was not

good at design, but there were some parts of the experience that were great, including

compared to Claude code.

The thing that Mark Tenenholz was talking about with much less friction in the approval

system was absolutely true.

There are so much fewer confirmations with Codex right now than Claude code in ways that

make the experience just massively, massively better.

What's more, Codex did a much better job of letting you know what was going on as it

was building.

It actually has not just reasoning traces, but almost interstitial updates around what

it's doing when it's doing a long running task.

This means that when it's doing something that takes five or six or eight or ten minutes,

it's not just a total black box.

You actually have a sense of where it is in its process as it's happening.

Now Claude code doesn't do none of that, but the way that Codex uses full sentences to

describe where it is I think is really appealing.

So the big thing was when we got to the point where we were actually ready to deploy

this thing, there were zero errors.

It just worked.

It worked immediately, it worked right out of the box, in a way that basically nothing

I've ever built with Claude code has.

Where that leaves me is in a place that's pretty similar to what every found, where I'm

not going to abandon one of these models for the other, but where I can absolutely see

and probably will find five, four in Codex deeply integrated into my process going forward.

I also think it's important to point out now that I've ripped on a bunch of things that

I didn't like that this was my first set of tests, and I have not yet taken the time

to go try to change, for example, the core instructions that make GPT 5.4 Overleaver

Bose.

It's entirely possible that a lot of my critiques are actually relatively superficial and

fixable and just about dealing with the out of the box settings.

Overall, based on the average of what people are experiencing, you would be doing yourself

a disservice if you didn't go try GPT 5.4.

I will certainly get more reps in with it, probably in some other contexts like OpenClaw

as well, and I will report an update when I do.

For now though, that is going to do it for today's AI Daily Brief, I appreciate you listening

or watching, as always, have fun playing with 5.4, and until next time, peace!