Just when you think the AI landscape is set in
stone, dominated by these huge corporate players,
an open source model just drops. It's called
DeepSeq v3 .2. And the claims are pretty staggering.
It's saying it can outperform GPT -5 and Gemini
3 in really critical areas like reasoning and
math. This isn't just an update. No, this feels
like a real shift. Welcome to the deep dive.
The pace of AI right now can feel well. overwhelming.
Yeah, it's a lot to keep up with. Our job is
to give you the shortcut. We've spent our time
digging through the sources on DeepSeq v3 .2.
And our mission today is pretty straightforward.
We're going to demystify the core tech here.
We're talking mixture of experts and sparse attention.
We need to unpack the three different versions
of this model. And most importantly, figure out
if this thing really delivers on that promise.
Yeah. Saving you money without, you know, sacrificing
actual intelligence. OK, let's start with that.
The core architecture. Why is this such a game
changer? For years, the models we've used, the
standard GPTs, they were all dense models. And
that's the key problem right there. Dense just
means inefficient. Oh, so. A dense model is like
a single massive brain. Every single neuron,
every part of that network has to fire up for
every single query. Even for a simple question.
Yeah, you're using the whole brain just to answer
what's the weather like. It's just incredibly
wasteful. So if that's the old, inefficient way,
what's DeepSeek's big fix? The fix is Moe. Mixture
of experts. Okay. Instead of one giant brain,
think of it like a team of specialized AI experts.
Moe just activates the specific network parts
it needs for efficiency. So it's like having
a team, not one overworked assistant. Exactly.
Imagine a librarian who has to read every single
book in the library for every question. That's
the old way. Right. Moe is like having a team
of experts, a math genius, a coding wizard, a
history buff. And when a question comes in, only
the relevant expert wakes up to answer it. And
the scale of this is pretty massive, right? It's
huge. The total brain power is 685 billion parameters.
But for any given word it generates, it only
uses a tiny fraction of that. And that's the
key. That solves the two biggest problems in
AI right now. Speed and cost. You get answers
faster and you need less computing power to do
it. Why is MoE of fundamentally better architecture
right now? Well, it's faster and cheaper because
it uses less computing power for every query.
That's the core of it. OK, so it's easy to get
a bit overwhelmed when a new model family drops.
And DeepSeq isn't just one bot. No, it's a family
of three. And it's really crucial to know which
one to use for what task. So let's break them
down. First up, you've got the main one, DeepSeq
v3 .2. This is your daily driver. Generalist.
Yeah, your go -to. It's balanced. It's fast.
It's smart. It handles all the standard stuff,
coding, writing. And it can connect to the internet,
use external tools. Yep. It could check stock
prices, pull weather data, browse the web. I
was using it to plan a trip and debug some simple
Python, and it just felt snappier. OK, so that's
the Daily Driver. What's the second one, the
one all the researchers are talking about? That's
DeepSeq v3 .2, especially. The genius. The genius.
This one, it locks the door to focus. It does
not look at the Internet. It doesn't use any
tools. So it's just relying on its own internal
knowledge. How does that give it an edge? It
generates something they call thinking tokens.
It's like it talks silently to itself, checking
its own logic step by step before it gives you
an answer. So it's best for things that require
pure structured reasoning. Exactly. Hard math
problems, complex logic riddles, physics. That's
its domain. And the third one, the X. DeepSeq
v3 .2 -x6. That one is really just for researchers
at the moment. It's testing the raw tech. You
should probably stick to the first two. Which
of these versions should our listener use 90
% of the time? The Balanced Main V3 .2 is the
general purpose daily driver for daily tasks.
Got it. OK, this brings us to the benchmarks.
And this is where it gets kind of shocking. Can
an open, free model really beat the giants? The
numbers. I mean, they speak for themselves. In
math, on the AM 2025 test, which is incredibly
difficult, Deepsea scored a 93 .1 % pass at one.
Let's define that. Pass it one means what exactly?
It means it gets the right answer on the very
first try. So no guessing, just immediate high
confidence accuracy. Exactly. And that 93 .1
% that beats GPT -5 high, which was around 91%.
Wow. Then in reasoning, think math, Olympiad
level, it's hitting gold medal performance. It's
matching Gemini 3 .0 Pro. Matching the best in
the world. And it's open source. And for coding,
on the SWE bench, it solved over 2 ,500 issues,
which beats Claude 4 .5 Sonnet. So it's not just
a one -trick pony. And that amazing performance
on logic, puzzles, and math, that really comes
back to the special version's thinking advantage,
right? It really does. Most bots just rush to
an answer. Special pauses to verify. We see that
with that classic riddle, right? If one wet shirt
takes an hour to dry. Yeah, how long for three
shirts? A normal bot might just multiply and
say three hours. But special... Special reasons
it through, understands they dry in parallel,
and correctly says one hour. Whoa. Imagine applying
that verification ability to scale enterprise
-level coding projects. That's the thing. It's
not just about riddles. It's about catching bugs
before they happen. How exactly does the special
version achieve such reliable results on hard
math? It pauses to verify its own logic, checking
each step quietly before giving the final answer.
OK, so that efficiency brings us right to the
money part. The part everyone's waiting for.
For anyone paying API costs, this is huge. Let's
talk about DSA, or Deep Seek Sparse Attention.
DSA is really the secret sauce that makes all
this cost saving possible. It's all about how
the model reads the information you give it.
So with the old way, if I gave a model, say,
a 1 ,000 -page document... It had to read all
1 ,000 pages every single time. That's dense
attention. Exactly. Just massive wasted effort.
DSA is like giving that 1 ,000 -page book a really
smart index. So instead of reading the whole
book, it just jumps to the relevant pages. It
only reads what it needs. And the cost difference
is... It's just staggering. Let's get into the
specifics. OK. So the cash miss, that's the first
time you read a document, is about $0 .28 per
million tokens. Which is already pretty cheap.
It is. But the cash hit. When you ask a question
about that same document again, the price drops
to about $0 .28 per million tokens. That's a
90 % drop. In a lot of cases, it's 10 to 30 times
cheaper than models that don't have this. So
if you're building an app that queries the same
legal documents or internal wiki over and over,
your bill just plummets. It changes the entire
economic model for those kinds of apps. I still
wrestle with how quickly API costs can spiral
when you're querying large knowledge bases. This
cash hit model is the cost break we needed. For
a developer reading the same long documents,
how significant is the cash and price difference?
The cash hit feature can effectively drop the
API bill by 90%, making large -scale processing
affordable. It's cheap, it's fast, it's smart,
so what's the catch? Let's talk reality. Can
you actually run this thing yourself? The MIT
license is great for privacy, your data stays
in -house, but there's a serious hardware reality
check. The full model weights are nearly 700
gigabytes. So you can't run this on a MacBook
Pro. No, not even close. You need a dedicated
server with massive VRAM. We're talking something
like 8 NVIDIA H100 or A100 GPUs. Which is tens
of thousands of dollars. At least. Just for the
hardware. So for an individual developer or small
startup, what's the realistic path to actually
using this? You've got a couple options. The
easiest by far is just to use their API. It's
cheap and they handle all the hardware complexity.
Okay, what's the second option? You wait. The
open source community will almost certainly create
smaller, distilled versions. You know, like a
7 billion parameter version that captures most
of the intelligence but can actually run on high
-end consumer hardware. And for the bigger companies
that do have that kind of server farm? They need
to use specialized software. Tools like VLLM
or SGLANG are built specifically to run these
sparse, moey models efficiently. What's the most
realistic path for an individual to use this
powerful model today? The easiest way is using
their cheap API or waiting for smaller, diskilled
versions to release. Makes sense. Okay, let's
pivot to using it. If we have these two distinct
versions, the daily driver and the logic engine,
we can't talk to them the same way, can we? No,
absolutely not. The way you prompt them has to
be different. So for the main v3 .2, the daily
driver, what's the strategy? You use it for quick,
standard tasks where you need clean output fast,
ask it for a React component for a pricing table,
specify the styling, tailwind CSS, three columns.
And it just spits out the code? Clean code immediately.
It's built for that. And for the special version,
the reasoning expert? There, you have to be precise.
Define the rules clearly. It's more like giving
it a formal problem statement. Use it for a complex
geometry proof where accuracy is everything.
So the switch is... Quick email summary, use
the main, debugging a really stubborn logic error
in your code. You use special. You match the
tool to the task. For the special version, do
I still need to write, think step by step? No,
special does that automatically, but you must
define the rules and constraints clearly. So
let's wrap this all up. What is the single biggest
idea you should walk away with? I think the big
idea is that open source AI is officially no
longer second best. DeepSeek proves that with
moey and sparse attention, open models can match
or even beat the performance of the biggest closed
platforms. And the final takeaway for me is twofold.
The special version gives a reasoning power that's
on par with GPT -5 or Gemini 3, while the main
version gives developers a cost structure that
could honestly revolutionize how we build AI
apps. The gap between open and closed has basically
vanished. The performance ceiling got raised,
but the cost floor just dropped through the basement.
That's the tectonic shift. We really recommend
you go and test that thinking mode on the special
model yourself. Give it a tough logic puzzle.
See how it works. And here's a thought to leave
you with. Yeah. If an open source model under
a free MIT license can do all this today, what
happens to the economics of those expensive closed
source AI platforms next? What does the future
of competition look like now? A great question
to think on. It is. Thank you for sharing your
sources with us and letting us take this deep
dive. We'll catch you next time.