OpenAI has just rolled out ChatGPT 5.4.
There's actually a couple of cool features in here that I'm really excited about.
That I've been wishing ChatGPT has been able to do in the past.
And they finally launched it.
And, of course, if you look at all of their marketing, it's going to just basically be them saying this is our most capable model yet.
And of course, it's the most capable model.
If it wasn't, I mean, what would they even be making an update for?
So I'm just going to get past all of the hype and all of the buzz from what they said in their launch.
And I'm going to tell you some really interesting use cases and some ways that I actually think this is useful.
GPT 5.4.
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All right, let's get into what's going on.
The first thing I want to mention here is that this is called GPT 5.4 Thinking.
They have a higher performance variant that is known as GPT 5.4 Pro.
But both of these together are designed to kind of handle everything from some complex analysis.
They do a lot of coding, a lot of long running workflows across a lot of different professional software tools.
And they're kind of dubbing this as like their professional work tool.
They're trying to get into, you know, into the hands of more working professionals.
And this is coming right on the backs of them signing a whole bunch of deals with a bunch of different consulting firms that are going to allegedly get ChatGPT into more businesses and kind of the professional environment.
And at the same time they're having kind of this.
You know they're locked in a battle with even Google's in this right now, but really with Anthropic for Anthropic's Cloud Code, their Codex tool.
They're really trying to push forward in kind of how software is using AI models and how computer use is going on.
So this is where they're really focusing.
One of the most, one of the biggest changes basically about this is the scale.
So in the API, GPT 54 has a context window of up to a million tokens, which basically lets them work with huge documents, really big conversations, big data sets.
And really I mean, if you think about this, a huge benefit is going to be coding where you can look at bigger code.
You know code bases to actually work with.
So this was something that Anthropic was really crushing at, and then OpenAI is trying to get into this.
OpenAI also says that their model is specifically more what they're saying is token efficient, which this is actually one thing that I'm excited about.
Basically can solve the same problems using a lot less tokens than GPT-5.2.
So your costs are going to come down.
It's actually kind of cool if you already had 52 running in a software which, even if you don't a lot of the software you use will.
The costs come down a lot for that, and it also gets a lot faster.
So the costs come down and the speed goes up.
And so, yeah, for me, this is something I'm actually excited about.
So as far as how the benchmarks look, I know you know I'm not trying to like sit here and nitpick the benchmark percentages, but I did want to talk about some interesting use cases and reasons why these are why they're good.
And specifically, it's kind of leading on a bunch of the better known benchmarks.
One of those is for coding.
Of course, we know why that's important right now, but also computer use.
And this is something I'm excited about right now.
I feel like Anthropic is really crushing it with computer use.
Basically you know it can look at everything on your computer and go click on stuff and get stuff done for you.
This is a use case that I've been using a lot with Claude's Anthropic browser, the Claude Chrome browser extension.
Basically, it's a button you click.
It opens a sidebar. chat bar.
I go to really complex UI or complex websites.
I'm not a developer, but if I'm going into like, for example, recently I had to do some stuff on Google Cloud to set up a tool that i was vibe building on lovable and i needed to beef up my back end so it could uh, you know, do some extra fancy stuff.
I didn't really understand anything that lovable was telling me i needed to be able to do, so i opened up the clod sidebar, told it look, i'm on my you know my google cloud account.
Go and here's the instructions from lovable, and it clicked around and set up some stuff for me.
Now should i have a real developer?
Look over this?
I mean, we're going to throw caution to the wind for the time being, and i hear all the developers screaming into their headphones right now.
But at the end of the day it got it done and my software is now functioning and I have.
I did not have to watch a whole bunch of long YouTube tutorials on how to set up some complex
I mean, for me, complex because I have no idea how to code.
Google Cloud stuff.
So this is a really incredible use case for a lot of reasons.
And I think OpenAI beefing up their capabilities and computer use is really exciting because they're going to start competing more directly with OpenAI.
I mean, this is not like error with Anthropic.
It's not like Anthropic, is kind of like the only one working on this opening has been doing this for a long time with agents, but it feels like it's getting a lot better okay.
The other one that i'm excited for um is they're getting a lot better at knowledge work, and so i mean these are kind of things that i think everybody uses it for.
So this is something we're just going to see some incremental improvements on um.
On openai's gdp val benchmark, which basically checks tasks, it has up like 44 different occupations, so it's kind of like showing you how you can use this for different professionals.
It is exceeding industry professionals in 83 of comparison.
So they're like look, these are all the tasks that people in all of these different professional industries are doing.
It is better than 83 or it's.
You know it's beating what an industry professional might give you in 83 of these cases specifically, I think for knowledge work.
And it has a really big jump from achieving about 71% that GPT 5.2 is getting.
So upgrading this to now GPT 5.4, we're getting from 71% to 83%.
It just basically is going to be a lot better for knowledge work.
I mean, and by a lot better.
I mean we're seeing you know a 10 jump here or you know 12 jump here, which is pretty significant.
On some of the coding benchmarks.
So SWE Bench Pro.
This is a software engineering bench pro.
The model is getting slightly better than the last version.
So I mean, this is good, but beyond just getting slightly better, it is actually quite a bit faster.
So Um, if anybody has used a lot of these software tools is specifically, we use cloud code AI box.
My developer sends me screenshots of like because of these really long, elaborate tasks that it's doing on our, our backend, our code base.
And he I swear it's like a goal for him to see how long he can get cloud code to run continuously without stopping on project.
He gives it.
It's funny because I'm, you know, vibe coding stuff on lovable and I usually get a lovable response back to me in, like you know, a minute or two.
He has a go for like three and a half hours doing a task.
So when this model gets faster, I'm excited because hopefully that three and a half hours gets cut down on.
You know some of the stuff that we're working on.
I think one of the things that it's also very good at is for real computer interaction.
There's an OS World Verified.
It basically evaluates how well an AI can operate a desktop environment.
It pretty much just takes a screenshot and then it uses the keyboard and mouse commands to go and click stuff.
Right now, it has about a 75% success rate.
I've used ChatGPT Agents.
It's not perfect.
It's actually not my go-to.
I don't use it that much.
I wish I could use it more.
I think Anthropic is doing better in this, but...
75% success rate, like they are improving.
Their success rate is up a bit.
It's better than GPT 5.2.
I still don't think it's the best.
There's a major focus on kind of how it is being used professionally.
OpenAI says their model right now is significantly better at basically giving the kind of deliverables that people use in real work.
So things like spreadsheets, presentations, financial models, legal analysis.
All of those.
They've done a bunch of different tasks and they had one performed by a junior investment banker analyst.
It got 87 compared to 68 that GPT 52 got.
Some human evaluators also preferred it about 68% of the time.
They said it had better visuals and better structure.
So there's some cool stuff.
Okay, cool features that you might actually use today.
This is the one I'm very excited about.
It has what they're calling steerability, but basically, when you're talking to ChatGPT, it's available in the API too, which is, I think crazy, but it's on ChatGPT.
If you're talking to ChatGPT, And you can kind of see it's reasoning, right?
Like it's thinking through some stuff and it puts a couple steps down and you realize it's going in the wrong direction.
You know, maybe you're like, hey, I'm trying to visit like the best beach for surfing.
And it's like, OK, looking at beaches in Kauai and you're like, oh, crap, like I'm in California.
I don't want to see Kauai.
And you're like, then you can type a message like specifically in California and mid Kauai, like prompt, mid response.
It actually takes into account what you just said and is, you know, steerability.
It's going to go and incorporate that into its, into what it's looking at and into its reasoning, and give you an updated response.
So basically you can do mid response prompts and it's going to take that into account and change its prompt and give you better prompt mid response.
So It's kind of interesting because I think they did a couple of clever things here.
But one of them is like when you ask a question, you have to wait for it to think.
You have to wait for it to reply.
You sit there and you wait.
We all hate waiting.
And so if, in the middle of waiting, we're reading its line of reasoning and we're giving it more input and more feedback, it feels like we did a lot less waiting.
We're really just kind of reading and trying to throw in something and it can get it done faster and better, rather than having to wait for it to spit out the whole thing.
And you'd be like, OK, this is wrong and here's why it's wrong.
And here's what you should do instead.
You could do that in the middle of the chat conversation response, which is really cool in my opinion.
Something else I've kind of focused on right now is online research.
Apparently, it can search across like a greater number of sources on the web.
So it can kind of instead of just like, OK, we're looking at this website, getting some data.
Now we're going to look at this website.
It's going to go in and search just like a ton at the same time across the web.
And then it's going to follow leads across different pages.
So it might get an idea from something to read on one article.
It's going to go follow that to another article, bounce around a lot more.
So it's kind of doing like I know we have had deep research for a while, but it's doing deeper research, if that's the thing.
And it's going to combine all the information that it gets into one coherent answer.
So basically, this is going to be more useful for some of the more complex questions where the information is kind of scattered across a lot of different sites instead of sitting in one place.
Now, not every question you ask is going to be relevant.
But sometimes when you have a complex answer question, it's going to be able to go get you a more coherent answer quicker.
So This is great.
They have all this like kind of I don't know fluff in their launch about how it hallucinates less and it has less.
You know it has more, it has less factual errors and all this kind of stuff.
I don't think that's super important.
One thing that we also heard about it is that it is going to it's going to turn you down less.
So like if you ask a question and they're like, hey.
Um, you know, I don't know.
You ask a question, it's going to be.
It's less likely, allegedly according to Sam Allman, to like not answer.
However, our good friend Connor Grennan, who hosts the AI applied podcast with myself.
He was testing.
I saw a post he made on LinkedIn where he asked it is it true that air bubble inside of an IV can cause me or, can you know, could kill me?
And it said, you know, apparently it typed out the whole response to him, kind of.
And just like we saw with like Deep Seek and the Chinese censored model, if you ask anything about Tiananmen Square to Deep Seek, it like types it out and then it disappears.
And it's like, sorry, can't answer this.
Apparently Chai Chibiti said the exact same thing.
Also, this is kind of a tricky moment because we're seeing New York right now is trying to pass some legislation where they they're saying hey, we don't like.
They're basically trying to pass legislation saying AI models can't ask, answer any questions about medical help legal, like they have all of these different areas.
I think even hairstylists they're trying to put in there.
It's basically all of the all of the different industries with regulatory capture.
They just don't want people to be able to get the answers for free.
So pretty I don't know kind of bummed about that legislation and people like seriously considering that.
However, so it doesn't seem like it's that much better, but maybe it's moving in a good direction.
I'm not 100% sure.
It still feels like there's other models that are more of the adult in the room, but you also get pros and cons with those models.
Grok famously is going to answer any question you have about basically any of those topics.
But, you know, there's a lot of different there might be some other cons with Grok.
So pros and cons to all of the models.
Thank you so much for tuning into the podcast today, guys.
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I'll catch you guys all in the next episode.
