Just yesterday, GPT -4 changed our world. Pete,
today it's already history. It really is moving
that fast. Welcome to this deep dive. I am deeply
grateful you are here with us today. Yeah, thanks
for tuning in. Our mission today is highly specific
and honestly deeply important. We are unpacking
a massive, comprehensive, early -2026 review.
We're looking at the three new AI titans currently
dominating the landscape. The big three, GPT
-5 .4, Claude Opus 4 .6, and Gemini 3 .1 Pro.
And we are not just looking at dry, boring numbers
today. No, absolutely not. We put these titans
through an absolute gauntlet. A real torture
test. Exactly. We ran them through five hardcore
real -world tests. Testing things you actually
do every single day. Right. We had them detect
fake financial data. We checked if they can write
human -like apologies. Which is surprisingly
difficult for a machine. It really is. And we
even made them code complex JavaScript games.
The technological landscape has shifted so dramatically
lately. It's truly mind -blowing. These new models
can think incredibly deeply right now. They can
remember thousands of pages in just seconds.
The sheer memory capacity is staggering. Let's
start by looking at GPT 5 .4 from OpenAI. Think
of this model as your ambitious top student.
The quantitative analyst. Exactly. It boasts
a massive 1 million token memory limit. Which
is essentially like memorizing a very thick textbook
instantly. Right. It costs $2 .50 per million
tokens. Pretty reasonable for the power. It is.
It's incredibly fast and it deeply excels at
complex logic. You can easily find it on platforms
like OpenRouter right now. Yeah, it's highly
accessible. Then we have Claude Opus 4 .6 from
Anthropic. I like to think of Claude as your
dedicated expert in writing. The Director of
Communications. Right. And it features something
called Agent Teams right out of the box. It spawns
many workers to handle separate parts of a task.
Which is a massive plus for complex workflows.
It delegates beautifully. It does cost a bit
more, at $5 per million input tokens. Yeah, it's
pricier. It has a 200 ,000 standard memory limit.
Though there is a 1 million token beta right
now. True. But it is highly safe and feels remarkably
natural. Finally, we have Gemini 3 .1 Pro from
Google DeepMind. This model is the absolute beast
of raw performance. The ultimate value workhorse
today. It's the cheapest at just $2 per million
tokens. And it scored an incredible 94 .3 % on
the GPQA science test. That science score is
honestly staggering. It really is. And it is
natively multimodal right from the start. Meaning
it understands audio and video directly without
any text conversion. It skips that translation
step entirely. Exactly. Which of these specs
actually changes the day -to -day workflow for
a user? It really comes down to that massive
memory size. When an AI can hold a mill - tokens,
everything shifts. You stop breaking your projects
into tiny frustrating pieces. You just feed the
model everything all at once. The friction is
just gone. So bigger memory means fewer bottlenecks
for heavy daily tasks. Absolutely. It fundamentally
changes how you work. Let's move to our first
major evaluation. The hallucination test. Right.
We need to talk about creating trustworthy financial
reports. This is a massive headache for busy
professionals today. Hallucination is just when
the AI confidently makes things up. And it ruins
trust instantly. You ask the model to write a
serious report. It gives you strong numbers and
detailed links. But then you actually click on
those links. And the link is broken or the number
is totally fake. To test this, we used a raw
PDF file. dense data. It contained real Southeast
Asia stock market data. This was data from 2025.
We asked them to write a 1 ,500 word report.
And they had to use real Bloomberg or Reuters
links. We put their data processing through an
absolute grinder here. Let's start with Gemini
3 .1 Pro. It had perfect accuracy down to the
decimal. It nailed the exact GDP numbers for
Vietnam and Thailand. Yeah, it has really great
Google search integration built in. But we did
notice it was a bit lazy with the layout. It
basically just dumped all the links at the very
end. Claude Opus 4 .6 took a different approach.
It was incredibly smooth and highly professional.
It read like it was written by a real financial
expert. But here is the most crucial detail of
this entire test. We planted a deliberate fake
data trap in that PDF file. Yeah, we actively
tried to trick it. We swapped some key export
numbers around. Claude actually caught the trap.
It immediately warmed the user. It stated it
would use correct real -world data instead. It
earns a massive high score for careful self -checking.
That is exactly what you want in an analyst.
Then we evaluated the report from GPT 5 .4. It
was highly detailed. It initially looked very
impressive. But it hallucinated several extra
filler parts entirely from scratch. It just invented
things to make the report look longer. Why does
GPT feel the need to invent filler content? It
seems wired to provide the most exhaustive answer
possible. It tries to draw extra connections
to look more thorough. It prioritizes creating
a long response over strict factual accuracy.
It just wants to impress you with sheer volume.
It prioritizes looking comprehensive over sticking
strictly to the facts. It's a classic case of
trying way too hard to please. Let's transition
to our next fascinating evaluation. The human
touch. Being accurate is important, but sounding
human is a totally different challenge. Oh, absolutely.
We asked the models to write an apology letter.
It was for a late package sent to a very frustrated
customer. We explicitly wanted to avoid classic
robot speak here. We all know those phrases.
We hate phrases like, in today's fast -paced
world. Or the dreaded, not only, but also structure.
Those are massive red flags for AI text. They
instantly break the illusion of empathy. Claude
was the absolute undeniable winner in this category.
A perfect 10 out of 10 for human style. It used
natural pauses and beautifully short sentences.
It genuinely sounded like a truly sorry friend
talking to you. GPT 5 .4 felt completely different
in its emotional approach. It sounded like a
legal department trying to avoid a lawsuit. The
sentences were much too long. Overly professional.
It completely lacked warmth. We gave it a 7 out
of 10. Gemini found a somewhat awkward middle
ground here. Yeah, an 8 out of 10 for human style.
It was easy to understand throughout the main
paragraphs. But the final ending felt like a
canned corporate template. Is it harder for AI
to mimic empathy than to do math? Math just follows
a strict set of logical, unbreakable rules. Empathy
is subtle. It's full of strange human contradictions.
AI struggles heavily when there is no objective
right answer. What feels warm to you might feel
deeply condescending to me. Yeah. Math has strict
rules, but human empathy is incredibly messy.
Messy, subjective, and highly dependent on cultural
context. We're going to pause for just a brief
moment. Be right back. This edition of The Deep
Dive is brought to you by our premium sponsors.
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you these in -depth analytical reviews of the
latest technology. Check the show notes for exclusive
listener discounts. And we are back. Let's look
at analyzing deep data insights. The big Excel
tests. We wanted to see how they handle heavy
unstructured information. So we uploaded a massive
50 ,000 row Excel spreadsheet. It contained raw
sales data from a local retail shop. We asked
them to find strange hidden shopping patterns.
We wanted insights that a human analyst might
never notice. Each model looks at raw numbers
in its own special way. It's literally like having
three different experts in your office. Let's
discuss Gemini 3 .1 Pro first. It leveraged that
massive 1 million token memory perfectly. Whoa.
Imagine scaling to a billion queries. It read
50 ,000 rows in exactly one second. The sheer
processing speed is genuinely hard to comprehend.
And it actually found a completely hidden shelf
placement trend. Yeah, it noticed people bought
umbrellas next to sunscreen. Because they were
prepping for extreme weather shifts. A human
would rarely cross -reference those two random
items. Clytopis 4 .6 took a very different analytical
path. It acts much more like a trained consumer
psychologist. It focuses heavily on the feelings
behind the raw numbers. Right. It looks for the
reasons behind the customer trends. It explains
the why instead of just listing cold percentages.
However, its smaller standard memory is a real
liability here. It actually crashes when processing
these massive, messy files. It just can't hold
all that context at once. GPT 5 .4 firmly establishes
itself as the ultimate math expert. It writes
Python code directly inside the chat window.
It builds beautiful, customizable charts in real
time for you. It's incredible for data visualization.
Does Claude's psychological approach make up
for its smaller memory? It absolutely does if
you have a dedicated marketing team. Understanding
the emotional drivers behind a purchase is deeply
valuable. Marketers need to connect with human
feelings, not just raw numbers. You just have
to feed it smaller chunks of data. Smaller data
sets get deeper emotional analysis, which marketers
desperately need. Quality of insight often beats
sheer volume of data. Let's shift our focus to
coding complex software games. Building a game
from scratch is a massive technical challenge.
It requires intricate logic and deep structural
understanding. We asked them to build a JavaScript
roguelite game. Some prompts asked for a cyberpunk
snake game variant. It required dividing the
code into very specific functional parts. We
needed a game logic section, a UI section, and
an input handler. This tests how well the AI
organizes a multi -file project. Claude Opus
4 .6 was simply a beast in this arena. It executed
the complex coding task flawlessly on the first
try. It even explained exactly why it organized
the files that way. It handles big apps well
due to a massive output limit. It prints the
whole game without stopping halfway through.
GPT 5 .4 was also a very helpful coding assistant.
It provided the full code. And suggested really
cool sound effects. But it used an outdated save
score function in the code. Yeah, that function
actually breaks in newer web browsers today.
You had to check its code very carefully for
deprecations. Gemini 3 .1 Pro was easily the
fastest coder of the group. It generated the
game code much quicker than the others. But we
noticed the enemy logic was honestly pretty stupid.
The enemies just kept walking straight into blank
walls. They couldn't figure out basic pathfinding.
Why does Gemini struggle with game logic if it's
so smart at science? Science often involves processing
known facts and established formulas. Game logic
requires understanding fluid, dynamic spatial
relationships. The AI has to predict how moving
parts interact constantly. Gemini prioritized
speed over thinking through those complex spatial
interactions. Speed sometimes sacrifices the
intricate spatial logic a game demands. Right,
and you end up with enemies stuck in corners.
Let's move to our final rigorous evaluation today.
The super prompt test. This evaluates following
strict rules without getting confused or lost.
Look, I have a vulnerable admission to make right
here. Go ahead. I still wrestle with prompt drift
myself when I give the AI too many rules. Well,
prompt drift is when the AI slowly forgets your
original instructions. It happens to all of us
constantly. You give it five rules and it completely
ignores the last one. To test this fairly, we
created a brilliantly difficult super prompt.
We asked the three models to write a movie review.
But we gave them four very strict formatting
rules to follow. First, they could not use the
word great anywhere. Second, Paragraphs had to
start with the letters C -I -N -E -M -A. Third,
they had to mention the director exactly three
times. Not two, not four. Finally, they had to
include a three -movie comparison table. Which
requires planning the entire response before
typing a single word. Claude Opus 4 .6 was 100
% obedient here. It followed every single rule
with perfect, careful execution. It planned the
acronym paragraphs flawlessly from the very start.
GPT 5 .4 struggled significantly with this complex
constraint list. It forgot the acronym rule by
the fourth paragraph entirely. It got way too
focused on the narrative story it was telling.
It sacrificed formatting rules to write a more
compelling review. Jim and I followed the basic
story rules reasonably well overall. But the
final comparison table was incredibly lazy. It
was extremely simple and lacked any deep comparative
information. What's the best way to avoid prompt
drift entirely? You really need to stop sending
massive walls of text. It overwhelms the model's
attention mechanism. You should logically break
your instructions down into individual, sequential
steps. Guide it through the process, one clear
rule at a time. Just break long instructions
into smaller, bite -sized steps. That is the
most reliable way to guarantee consistent performance.
We've covered a massive amount of ground today.
That's a lot to process. Let's synthesize this
into a big idea recap for you. We need to understand
what this all means for your workflow. If you
are a writer or a busy programmer needing perfection,
choose Claude. It provides that essential human
touch and flawless, careful logic. It's definitely
worth spending the extra money for that reliability.
If you're a student or a small business... Look
at Gemini. It is the ultimate value workhorse
of early 2026. You can process huge files and
videos incredibly cheaply. It handles massive
context windows faster than anything else available.
And if you're doing complex math or creating
charts, GPT 5 .4 remains your classic, highly
reliable go -to tool. Its Python integration
is still wonderfully smooth and deeply technical.
It's the analytical engine you want for heavy
data lifting. I highly recommend you go out and
try these models yourself. You can test small
versions for free on their respective websites.
Or you can use Open Radar to test the premium
versions side by side. AI changes daily, and
you desperately need hands -on experience. Reading
about these models is simply never enough. You
have to feel how they respond to your specific,
unique workflows. You need to see where they
shine and where they break. I want to leave you
with a final provocative thought. Beat. Think
deeply about the technological trajectory we're
on right now. If Claude is already writing apologies
that feel more sincere than a human's. And GPT
is creating complex charts faster than a trained
analyst. At what point do we stop using AI to
assist our thinking and accidentally start letting
it replace our empathy? Two secs silence. Thank
you for joining us on this deep dive. Take care.