It's December 2nd, 2025. You walk into the OpenAI
headquarters in San Francisco. Now, usually,
this is the time for holiday parties, right?
You're expecting champagne corks popping, maybe
some high fives that are the kings of the hill.
You would certainly think so, yeah. But instead
of a party, the mood is tense. It's frantic.
It is officially a code red. A code red. And
why? Because the era of the bodybuilder AI, you
know, those massive hulking models that rely
on pure size to solve problems, is officially
over and the era of the gymnast has begun. That's
a vivid way to put it, but honestly, it's not
far off. The raw strength we've all been obsessing
over, it's still there, sure. But suddenly, the
game isn't just about strength anymore. It's
about precision. It's about agility. And if these
leaks are real, the whole board has been reset.
Welcome to the Deep Dive. It's Thursday, January
22, 2026. Today, we're unpacking something that
feels like a genuine pivot point in AI history.
We're looking at the leaked details of OpenAI's
GPT -5 .3, which has the internal, and let's
be honest, slightly hilarious, codename, Garlic.
Garlic. It's definitely a choice. A lot earthier
than, you know, Orion or Gemini, all that celestial
stuff we usually get. It really is. But don't
let the name fool you. Our mission today is to
figure out... why this specific model caused
such a panic. This code red at OpenAI. We're
going to look at the engineering breakthroughs,
specifically around memory and this new self
-checking thing. And we'll see how it stacks
up against the current heavyweights, Google's
Gemini 3 and Anthropic's Claude Opus 4 .5. This
is going to be fun because the specs here aren't
just number go up. Right. It's not just we added
more zeros. This is a whole philosophical shift
in how we built this stuff. So let's start with
that context. We mentioned the code red. This
leak came from the information back in December.
Mark Chen, the chief research officer at OpenAI
reportedly shared these details. But help me
understand the panic. I mean, GBT 5 .2 was already
out. It was a good model. Why the fire drill?
It all comes down to momentum. In tech, if you
aren't leading, you're basically dying. And frankly,
OpenAI was losing ground. Losing ground to who?
To everyone. I mean, look at the last six months.
Google drops Gemini 3. And Gemini 3 just dominates
anything multimodal video, messy real world data
images. It was the king of stuff. Right. And
then on the other side, you had Anthropic with
Claude Opus 4 .5. And let's be honest, if you
were writing code in late 2025, you were probably
using Claude. I could vouch for that. I switched
to Claude for all my scripting. It just felt
less, I don't know, robotic. Exactly. So internally,
GPT 5 .2 was seen. as a Band -Aid. It kept them
in the conversation, but it wasn't bribing it.
They knew that shipping GPT -5 at a little bit
bigger just wasn't going to cut it. They needed
a response that fundamentally changed the metric
of success. And that response is garlic. You
used this analogy before we started the bodybuilder
versus the gymnast. I want to double click on
that because for. what, the last five years,
the headline has always been parameter count.
Yeah. Trillions of parameters. Fairer clusters,
more GPUs. Yeah. If you weren't building bigger,
you weren't trying. And that's the bodybuilder
approach. You solve problems by just adding more
muscle. The model doesn't get physics. Add a
trillion more parameters. Can't write a sonnet.
Add another trillion. Total brute force. But
garlic represents a shift toward density. It's
the gymnast. It's physically smaller architecturally.
more compact. But because of that, it can do
these complex reasoning maneuvers, these mental
backflips, if you will, that the massive, clumsy
bodybuilder just can't do. Okay, but I have to
play the skeptic here. Making it smaller but
smarter sounds like... Marketing fluff. It sounds
like the holy grail everyone promises, but nobody
delivers. Usually when you make a neural net
smaller, it gets dumber. How did they actually
do that? It wasn't a straight line. It comes
down to a merger of two different research tracks
inside OpenAI. They had Shallot Pete, which was
just their standard stability update track. Kind
of boring. But then they had this experimental
branch called Garlic. And the big breakthrough
is a technique called EPTE. BPTE. Enhanced Pre
-Training Efficiency. Okay, break that down for
me. No jargon. What is EPTE actually doing that's
different from GPT -4 or 5? Think of it like
a garden. Or, you know, better yet, the human
brain. When a baby is born, their brain just
has this explosion of connections, synapses everywhere.
It's a mess. Right. As we grow up, we get smarter
not by adding connections, but by pruning them.
We cut out the noise so the signal can travel
faster. So we get smarter by deleting parts of
our brain. In a way, yes. We delete the inefficiency.
Traditional AI training is like letting a forest
grow wild connections everywhere. Vines, weeds,
you name it. It's huge, but it's messy. EPCE
introduces a pruning phase during the training.
The model actively discards redundant neural
pathways. So it's Marie Kondo -ing its own brain.
This neuron does not spark joy. Basically. It's
cutting out the noise. So the result is you get
GPT -6 level reasoning capabilities. So higher
logic scores. Exactly. Higher logic scores. Because
you've pruned away the inefficiency, it runs
on a faster, smaller architecture. It's compressing
thought. That's fascinating. It really does challenge
that whole more is better assumption. But I have
to ask, is efficiency really as exciting as raw
power? As a user, do I care if the model is efficient?
I usually get excited about the next biggest
thing. Absolutely. And here's why. Latency and
cost. Think of it like the difference between
a muscle car and a Formula One racer. The muscle
car has raw power and makes a ton of noise, burns
a ton of gas. But the F1 car, it has precision
engineering. It turns on a dime. In this case,
precision means the model is cheaper to run and
faster to answer. And when intelligence gets
cheap and fast, you can use it in ways you never
could with the big, slow bodybuilder. So it's
shifting from raw horsepower to agility, and
that changes what it's useful for. Exactly. It
moves from a consultant you hire once to a worker
that lives in your computer. Let's move to the
specs, because while the philosophy is cool,
the actual numbers here are, well, they're staggering.
And I want to make sure we really get what they
mean day to day. Let's do it. So, memory first.
The context window. Garlic is reportedly shipping
with a 400 ,000 token context window. Now, just
to play devil advocate here, Gemini 3 has 2 million
tokens. So on paper, Garlic looks smaller. Why
should I be impressed by 400k? On paper, yeah,
Gemini is bigger, but this is where the nuance
really matters. If you've ever used Gemini's
huge context window, like you dump a whole novel
in there and ask about a character from chapter
three, you might have noticed middle of the context
loss. Right. I have seen this. It remembered
the very beginning and the very end of your prompt,
but it gets hazy on everything in the middle.
It's like it skimmed the book. Exactly. It's
the needle in a haystack problem. Yeah. Gemini
has a huge stomach, but imperfect digestion.
Garlic uses a new attention mechanism that reportedly
gives it perfect recall. cross the whole 400
,000 tokens. It doesn't just store the data.
It actually remembers it. So I could feed it
literally my entire company's documentation.
Every PDF, every Slack policy. Every messy confluence
page. And it wouldn't just have it. It would
know it. It would know it. You could ask, what
was that compliance rule we changed three years
ago from that one Tuesday memo? And it just pulls
it instantly. No skimming. That's the difference
between a hard drive and a brain. Precisely.
But here's the part that actually stopped me
in my tracks. It's not just how much it can take
in, it's how much it can put out. The output
limit. This is the big one. This is that wonder
moment. 128 ,000 tokens. In a single response.
It's huge. I mean, just for context, for everyone
listening right now, we're all used to the model
just stopping. You ask it to write code. It gets
halfway through a function and it just cuts off.
And you have to type continue. And then it repeats
the last line or it forgets the indentation.
It's so fragmented. It feels like pulling teeth
sometimes. It totally breaks your flow state.
But 128 ,000 tokens. That's not a response. That's
a novel. That's an entire software library. It
is. And I want you to really just pause and imagine
the experience of that. Imagine sitting at your
terminal. You ask for a complex legal brief or
maybe a full backend architecture for a new app,
not just the outline, the actual code. You hit
enter. And instead of a summary or a little piece
of it, you just watch the cursor move. And it
keeps moving. And it writes the files. It writes
the documentation. It writes the test suite.
And it just doesn't stop until the thought is
complete. One coherent stream of creation. That
is, wow. It's almost overwhelming to think about.
It feels like we're moving from just chatting
with a bot to manufacturing with a machine. That's
the shift. No more chunking. No more stitching
things together. You're not pasting snippets
into VS Code anymore. You're reviewing a finished
product. But does infinite memory actually change
how we work? Or does it just change how much
junk we dump into the chat box? I worry we'll
just get lazier. I think it fundamentally changes
the human's role. It stops us from being librarians
of data, you know, constantly fetching context,
pasting files, reminding the bot what we're talking
about, and lets us be architects of ideas. You
design the structure, the model pours all the
concrete. So the output limit frees us from being
data fetchers. To be designers instead. Spot
on. It shifts the cognitive load from memory
to strategy. That distinction architect versus
librarian, that really lands. Because the other
feature Garlick supposedly has leans heavily
into that architect role. We're talking about
native agents. Yes. This is another area where
that code red was necessary. Because right now,
everyone tries to make AI agents go do this task.
And, well, usually they fail. They get stuck
in loops. Or they hallucinate a file path that
doesn't exist and then crash. Right. But Garlic
isn't pretending to be an agent. The tool use
is native. It understands file systems. It can
run tests. It can debug like a developer. It
treats APIs not as some external thing it has
to awkwardly reach for, but as part of its own
cognitive process. So it's the difference between
me trying to speak French with a dictionary in
my hand, looking up every word. Versus actually
being fluent in French. Exactly. It thinks in
tools. It thinks in execution. If it writes code
that... fails a test it sees the error corrects
it and reruns it all before it even gets back
to you now speaking of thinking and correcting
itself yeah there's one feature here that i think
solves the single biggest anxiety i have when
i use these tools now i want to be a little vulnerable
for a second go for it i still struggle with
trusting the output You know, I'll spend 20 minutes
crafting this perfect prompt. I get an answer
that looks incredible, super confident, polished.
And then I spend 40 minutes fact checking it
because I've been burned by hallucinations before.
Oh, yeah. The universal experience. Confident
liar problem. Exactly. And it creates this weird
friction where I'm like, is this actually faster
if I have to babysit it? But garlic has a self
-checking mechanism. This is a game changer for
exactly that anxiety. Before the model answers
you, it enters a verification state. It just
pauses. It checks its own internal knowledge
graph to see, do I actually know this or am I
just statistically guessing? So it has a conscience
or at least a built -in BS detector. It has a
system two thinking process, to use the Daniel
Kahneman term. System one is fast, intuitive.
System two is slow, deliberate. Garlic uses system
two. If it isn't sure, it reassesses. The report
says this leads to drastically fewer hallucinations.
For lawyers, for developers, this is everything.
But I have to play devil's advocate again. If
the model is stopping to check its work, doesn't
that make it slower? We were just talking about
speed being the new king. It might pause briefly
before the first token appears, maybe a second
or two of thinking time. But think about the
time you just described, the 40 minutes you spend
fact -checking. It might be slower per second,
but it saves hours of human rework later. It's
that Navy SEAL mantra. Slow is smooth, and smooth
is fast. So the pause pays for itself by eliminating
all that cleanup time. Exactly. It trades milliseconds
of latency for hours of reliability. I'll take
that trade any day. Okay, I want to zoom out
a bit. We have the specs, the philosophy. But
OpenAI isn't operating in a vacuum. They're in
a war. We mentioned Google and Anthropic. How
does Garlic actually stack up? This is the battle
for the leaderboard. So let's look at Google
first. Gemini 3. The heavyweight. The heavyweight,
yeah. If you look at the leaked benchmarks, the
battle here is scale versus density. Gemini 3
wins on multimodal. If you have messy, real -world
data video, audio, weird images, Gemini is still
the king. It has that massive context and parameter
count for a reason. So if I'm analyzing a movie,
I use Gemini. Correct. But Garlic wins on pure
text, code, and complex reasoning. The benchmark
is something called GDP Val for reasoning. What's
that measuring? It's measuring logic puzzles,
multi -step reasoning where you can't just memorize
an answer. Garlic is scoring 70 .9%. Gemini is
at 53 .3%. Wow. That is not a small margin. That's
a generational gap. It's a blowout on reasoning.
So the verdict is, analyze a three -hour video.
Use Gemini. Build the back end of a banking app
where logic is everything. You use Garlic. Okay,
so that's Google. What about Antropic? Quad Opus
4 .5. I know a ton of developers who swear by
Claude. It feels warmer. It writes really readable
code. Yeah, this is the battle for the developer's
soul. Claude is known for that warmth and readability.
But Garlic is coming in with a ruthless value
proposition. It matches Claude's coding proficiency
94 .2 % on Human Evil plus R, which is the gold
standard. So it's just as good at the actual
coding. Okay, so it's a tie. Not quite, because
Garlic does it at two times the speed and half
the cost. Half the cost. Because of that pruning
we talked about. The model is physically smaller.
It burns less electricity. It costs OpenAI less
to run, so it costs you less to use. That's significant.
Yeah. But does price really trump everything?
I mean, if Claude feels more human to interact
with, won't people stick with it? For a casual
chat. Maybe. If you're brainstorming, you might
stick with Claude. But for running a business
at scale, if you're an API customer processing
millions of requests, half price is everything.
The unit economics alone will shift the market
overnight. It's the difference between a boutique
shop and like industrial scale. Exactly. If you're
building a startup and your API bill just got
cut in half, you're not going to care how warm
the model feels. You care that it works and it's
cheap. So garlic. competes on unit economics
and logic and sort of cedes the human touch to
Claude. For now, yeah. It's an industrial revelation,
not a dinner party. So we have the what and the
why. I want to take a brief pause here. And we're
back. We've looked at the code red, the tech,
and the competition. I want to wrap our heads
around the timeline and the big picture. Leaks
are great, but shipping is what matters. When
are we actually going to see this thing? Well,
looking at how the leaks and vendor updates are
converging, it feels imminent. We're expecting
a preview release. Probably the ChatGPT Pro users
and some partners in late January 2026. Late
January. That's basically this week. It's happening
now. Then the full API availability is slated
for February. And this is interesting. They're
expected to integrate a version of this into
the free tier by March. A counter to Gemini's
free access. Exactly. They have to capture that
user base. They can't let Google own the entry
-level market. Pulling all this together, the
pruning, the 128K output, the self -checking,
what's the big idea here? If I'm a listener trying
to make sense of all this, what is the core shift?
The core shift is that the definition of AI progress
has changed. For five years, progress meant bigger.
It meant more parameters. Now, progress means
cognitive density. It is about intelligence per
dollar and intelligence per watt. Cognitive density.
I like that. It sounds focused. It's about doing
more with less. And for you listening, the so
what is pretty direct. If you're a developer,
you can finally refactor entire code bases without
losing context. You don't have to choose which
files to upload. If you're a business, you can
build automation that actually works because
of the agentic capabilities. And if you're a
creator, you can generate long form content books,
scripts, courses without having to manually stitch
it together. It feels like the training wheels
are coming off. They are. The limitations we've
all learned to work around. Oh, I can only paste
half this file. Oh, I have to check its math.
Those limitations are just evaporating. So before
we sign off, let's give everyone listening something
to do. Because if this is dropping in weeks,
we shouldn't just be waiting around. How do we
prepare for the garlic era? Two things. First,
organize your data. If you want to use that 400
,000 token context window with perfect recall,
your data needs to be ready. Clean up your documentation,
merge your repositories, get it ready to go.
Don't feed the gymnast junk food. Exactly. If
you feed it garbage, you'll still get garbage.
Just perfectly recalled garbage. Good point.
And second, map your workflows. Start thinking
in terms of agentic workflows. Don't just think,
what question can I ask the bot? Think, what
multi -step process can I hand off entirely?
Check these invoices against these emails and
update the spreadsheet. If you map those out
now, when garlic drops, you can plug it in and
it will just work. That's great advice. Whether
it ends up being called garlic or GPT 5 .3 or
something else entirely, the message is clear.
The era of... giant, slow, forgetful AI is ending.
And the people who are ready to build with these
new efficient models are going to have a massive
advantage. A massive advantage indeed. Thank
you for walking us through the Code Red. Always
a pleasure. And to you listening, thank you for
diving deep with us. Go clean up your data, get
your workflows ready, and we will see you on
the next one. Take care.