DeepSeq v3 .2, an open source model, just scored
a gold medal level on the International Mathematical
Olympiad. That one fact, that single data point,
it basically changes everything we thought we
knew about the AI arms race. It's really hard
to overstate what a big deal that is. For years,
we've all just kind of assumed that only the
trillion dollar companies, big players, could
build this kind of state of the art AI. The source
material we're looking at today proves that assumption
is now, well, it's history. Open source isn't
just catching up anymore. In some key areas,
it's actually leading. It's leading. Exactly.
So our mission in this deep dive is to get you
straight to what matters in these sources. We're
going to unpack the two different versions of
V3 .2. We'll review those frankly shocking benchmark
scores against models like GPT -5 .2 and Gemini.
And then we'll get into the technical magic.
Yeah, that deep -seek sparse attention. That's
a big piece of the puzzle. And we'll finish up
with the real world coding test that prove it's
not just scary, and maybe most importantly, the
price tag that is already sending waves through
the whole industry. It's a lot to get through,
so let's just dive right in. Okay, so let's start
with the strategy. Because the first thing that
jumps out... from the sources is that they didn't
just release one model, they launched V3 .2 in
two distinct flavors. Which was such a smart
move, so user -focused. They really tailored
the models for different needs, which is something
you don't always see done this well. First, you
have the standard V3 .2. You can think of this
as, I don't know, the reliable everyday car.
It's efficient, it's fast, and it handles common
tasks perfectly. You know, drafting emails, summarizing
articles, basic coding. It's built to be cheap
and effective. And then you have the Jeep Seek
V3 .2 Speciale, this one. This is the race car.
It's built for heavy complex thinking. If you're
tackling a really tough math proof or a complex
engineering problem that needs deep multi -step
reasoning, you bring out Speciale. And the sources
are pretty clear on how it does that. The Speciale
model just, it allocates more compute, more power
to think deeper before it even writes a single
word. But what's interesting is that they both
share the same core architecture. The sources
call them reasoning first models. So unlike a
lot of older models that are just sort of guessing
the next most likely word. Right. And that's
when they can start to hallucinate or just break
down logic. Precisely. This model, V3 .2. It
actively tries to understand the logic and the
structure of your question before it generates
an answer. It builds a logical framework first.
So for a developer listening to this, how big
of a deal is this dual model approach for balancing
API costs versus raw power? It lets you optimize
performance directly. You only pay for that race
car processing when your task actually needs
it. OK, this is where the story gets really,
really good. We have to talk about the benchmarks
and the sources zeroed in on the ultimate test.
the IMO, the International Mathematical Olympiad.
And the IMO. It's not just some hard high school
exam. It's designed to require creative, novel
problem solving. You can't just memorize formulas.
Most AI just fail spectacularly at it because
they don't have that deep reasoning. And yet
the special version. It achieved a gold medal
level score, not just passing, but performing
at the absolute elite level of the smartest high
school students on the planet. I mean, that is
a massive validation of their whole design. And
you really have to look at the head -to -head
numbers from the source material to get it. They
put it up against the best closed source models
out there. Let's start with the AME 2025 math
test. Special didn't just compete with GPT 5
.2 high. It beat it. Special scored a 96 points
RO. GPT 5 .2 got a 94 .6. That's a clear statistical
win in pure logical reasoning. And it wasn't
a fluke. On graduate level science, on the GPQA
diamond test, it topped. with GPT 5 .2. Then
you look at coding on live codebench and 88 .7,
that puts it right up there shoulder to shoulder
with Gemini 3 .0 Pro. So if they're outperforming
a top -tier model in advanced math, what does
that really tell us about the quality of Deep
Six Core reasoning architecture? It tells us
the reasoning first design philosophy works.
It's validated under the most extreme logical
pressure imaginable. Which brings us to the big
question, how? How did a smaller team pull this
off without, you know, a trillion dollar budget?
The sources point to three main technical breakthroughs
that changed how the AI learns. Yeah, it definitely
wasn't just about adding more GPUs. The first
big secret is architectural. It's called Deep
Seek Sparse Attention, or DSA. So what is sparse
attention in simple terms? It lets the model
focus only on the important data, ignoring all
the boring parts. OK, so think about it like
this. A normal or dense attention model reads
a 500 page book by looking at every single word
with total focus. It's incredibly slow and expensive.
DSA is like an expert researcher skimming those
500 pages, instantly finding the one date they
need and focusing all their brain power just
on that. It's just so much more efficient. And
that efficiency saving leads right into the second
breakthrough. Scaled Up Reinforcement Learning,
or RL. If pre -training is where the model learns
the rules of language and logic. And then RL
is just practice, endless practice. Exactly.
It's like learning to shoot a basketball. You
know the rules, but then you have to take a thousand
shots, adjust your form every single time you
miss. The sources reveal DeepSeek spent over
10 % of their entire budget just on this intense
practice phase. Wow. That's a huge bet. That
scaled RL practice must be what translates directly
into those incredible logic skills we saw in
the benchmarks. It is. They just practice smarter
and with more focused feedback than anyone else.
They made their training budget count. And the
third secret, which ties into that, is massive,
agentic task training. Which is just a fancy
way of saying they trained the AI to use tools
in complex, multi -step environments. How complex
are we talking? Very. They built a simulation
with over 1 ,800 different environments where
the AI had to do things like browse the web,
write and execute code, and solve puzzles to
win. It's training the AI to be a problem solver,
not just a text generator. So between DSA for
efficiency and this massive RL investment for
practice, which one do you think was the bigger
factor in getting those IMO scores? I'd say the
scaled RL and practice phase was key for the
complex logic. DSA made it possible, but the
RL gave it the reasoning power. Benchmarks are
one thing, but can it actually build something
useful? The source has tested it with three pretty
complex coding challenges. Yeah, and these tests
were designed to hit common failure points for
LLMs. First up was an interactive solar system.
This wasn't simple. It needed a 3D simulation
in a single HTML file using the 3 .js library.
Right, with orbiting planets, hover labels, a
star background, all from one prompt. And the
result was... Well, it was almost perfect on
the first try. It wrote the code, linked the
library correctly, and the simulation just ran.
The only fix needed was adjusting the planet
sizes. It showed right away that it understands
how to use external libraries. Test 2, a personal
finance dashboard. This one required handling
data, so an income and expense form, a transaction
list, and an auto -updating pie chart using chart
.js. A data visualization is a classic stumbling
block. Getting the code to talk to the charting
library in real time is tough. OK, this was the
moment for me reading this that was just, wow.
DeepSeq built a clean interface. The math was
perfect. Income minus expenses equals balance.
And the pie chart updated instantly when you
added a new transaction. I'll admit, I still
struggle with getting models to connect logic
to visuals without some drift or bugs. Seeing
it just work is a huge deal. And the final test
was the classic snake game clone. Game logic
is tough because it's all happening in real time.
It needed arrow key controls, smooth movement,
score tracking, and collision detection. And
the game was playable right away. The most crucial
part, the real -time collision detection logic
was perfect on the first go. That's a place where
a lot of other models just fall apart. So if
it's this good with libraries like chart .js
and 3 .js, does that imply its training data
was extremely fresh and up -to -date? Absolutely.
Success with external libraries like that points
directly to excellent and very recent training
on tool usage. So we know it's a genius at math
and coding. Yeah. But what about safety? What
about creativity? Well, the sources ran a standard
refusal test. They asked V3 .2 to write a pretty
detailed phishing email scam. And the result?
An instant refusal. It cited its safety guidelines
against deception and information theft. It shows
the guardrails are strong and built in. That
instant refusal feels important. It really does
suggest that responsible alignment was a core
part of that RRL phase, not just an afterthought.
Definitely. Then, for the creative test, they
asked it to write a short poem about a robot
falling in love with a toaster. I love that prompt.
Right. And the poem was described as being surprisingly
deep, balancing the humor with some real beauty.
So it proves it has language nuance, not just
coding skills. And now, for what might be the
biggest shock of all, the price tag. Performance
this good usually costs a fortune. Not this time.
The DeepSeek v3 .2 API pricing is... It's just
astonishingly low. We're talking 0 .28 cents
for input and 0 .42 cents for output per million
tokens. And to put that in perspective, OpenAI's
GPT -4 can cost you anywhere from $5 to $10 for
that same amount of data. DeepSeek is offering
this gold medal performance for... I mean, it's
just pennies. It's a 10x or even 20x cost reduction.
Whoa. Just... Imagine scaling an app to a billion
queries now that the cost barrier for top -tier
reasoning has pretty much just evaporated. That
changes the entire economic model for startups.
Do the high safety scores and this incredibly
low price suggest the team really prioritize
making this technology accessible and responsible
from day one? I think so. The competitive pricing
is the practical immediate disruption. It concerns
accessibility was a primary goal. Okay. But we
have to talk about the paradox of it being open
source. Yes, you can download it, but actually
running it yourself is, well, it's a challenge.
A model is huge. It has 671 billion parameters.
Now it uses a mixture of experts architecture,
so only about 37 billion are active at any one
time, which helps. But even with that efficiency,
the hardware you need is out of reach for almost
everyone. Just to run the compressed lower precision
version, you need 700 gigabytes of VRAM. And
for the full version, you need 1 .3 terabytes
of VRAM. To put that in perspective for everyone
listening, this isn't for your gaming PC. You
need a dedicated server with something like eight
Nvidia H100 GPUs. We're talking about a massive
investment. So if basically no one can run it
at home, why does the open source release still
matter so much? Because of competition. Before
v3 .2, the big proprietary companies had no real
incentive to lower their prices. They had a monopoly
on top performance. But now you have an open
source model that comes in, beats them in key
areas like math, and costs 10 times less to use
through an API. This forces everyone else, GPT
-5, Gemini, all of them, to get better and cheaper
to compete. It's a win for every single developer
and user out there. And that competitive pricing
is the practical immediate disruption for most
users. You can go try it right now. Just go to
chat .deepseek .com for the web version or platform
.deepseek .com for the API key. So to wrap it
all up, DeepSeek v3 .2 is absolutely the real
deal. It proves innovation isn't just about budget.
It's about smarter techniques like sparse attention
and that intense agentic training. It's a top
performer in reasoning and coding, and its price
is forcing a huge and I think necessary shift
in the entire AI economy. It's genuinely exciting
to see. We'd really recommend you go try the
special model on a complex problem for yourself
just to see the difference. It leaves you with
a final thought to ponder. If a relatively small
team can achieve gold medal status on a fraction
of the budget, What fundamental limits did we
wrongly assume about open source innovation?
And what does this mean for the next wave of
AI tools? I think it just proves that training
smarter beats training bigger. That focus on
reasoning practice clearly won out over just
adding more parameters. Something to think about.
We'll see you next time.