A fully finished 23 second video intro beat generated
from a single slash goal prompt. Wow. There was
no step by step babysitting involved at all.
It took one hour and 15 minutes to complete.
Yeah, that is wild. And it didn't even max out
the one million context window. Right. And it
used three hundred fifty seven thousand tokens
to do it. This completely shifts how we have
to look at open source capabilities today. Welcome
to the deep dive. We are very glad you are here
with us. Absolutely. Today, our mission is to
unpack a really rigorous hands -on guide. We
are testing the open source GLM 5 .2 model, and
we're doing this directly inside Cloud Code.
We are going to walk through some actual real
world tests here. We pit GLM 5 .2 against the
heavyweight OPUS 4 .8. Right. We will outline
a fascinating 80 -20 workflow split for your
daily tasks. We will also break down the surprisingly
simple non -technical setup. Finally, we will
reveal why this specific shift makes open source
AI impossible to ignore. Beat. But to really
understand why this matters, we have to look
at the data. Yeah, what actually happened when
these two models went head -to -head in the wild?
That is the most important question. There were
several highly practical tests run here. Right.
We wanted to see if GLM 5 .2 could genuinely
compete on real work. Or did it just feel good
because it was the cheaper option? Well, the
first test was a one -shot web design task. The
exact same prompt was used for both models. They
both had to build a fully functional landing
page. GLM finished the job in 3 minutes and 59
seconds. Wow. And Opus took 14 minutes and 59
seconds to finish. That is an absolutely massive
difference in time. And GLM was around five times
cheaper to run. Yeah. But the really fascinating
part to me is the actual quality. Exactly. These
were not clunky early 2000s wireframes. Right.
Both results were highly polished and visually
impressive. The pages had movement built into
the design. They had properly structured sections
and very clear calls to action. This was not
some flimsy budget result you would have to rewrite
anyway. No. It was a completely viable starting
point for a real project. Looking at them side
by side, you would have a hard time justifying
that massive price tag for Opus. When the final
result is that close, the price starts to matter
heavily. It really does. But we didn't stop at
visual design. Next up was hard coding. So GLM
crushes basic web layout, which is great. But
visual spacing is one thing. Logical reasoning
is another entirely. Absolutely. That naturally
makes you wonder what happened. Less forgiving.
It was a complex coding assignment was evaluated
using codecs. Using codecs kept the evaluation
entirely neutral and fair. Exactly. And GLM 5
.2 actually did very well here. It handled the
bulk of the task perfectly. But. Opus caught
one very subtle edge case that GLM missed entirely.
tricky database values, it caught the subtle
difference between true and one, or one and one
point now. Oh, wow. If you have ever stared at
a broken database at two in the morning, you
know exactly why that matters. Yeah, bugs always
live in those tiny details. It feels like a very
natural division of labor. GLM is kind of like
the fast junior developer. It writes the bulk
of the code very quickly. And Opus is the senior
architect catching the tricky database bugs before
they launch. That is a perfect analogy for how
this works. GLN is fantastic for fast implementation
and initial scaffolding. Right. And OPUS is necessary
for careful reasoning and tricky edge case handling.
Exactly. I have to be honest here. I still wrestle
with prompt drift myself. beat. You know, you
give an AI a long list of instructions. And by
step three, it completely forgets step one. So
watching an open source model hold its focus
over complex instructions is deeply impressive
to me. It really is an incredible leap forward.
Right. But there's a specific speed quirk we
definitely should mention. Right. GLM is not
always the faster model. Yeah. On a highly creative
HTML task, GLM took 35 minutes. Opus finished
that exact same task in only 11 minutes. The
rule here seems to revolve entirely around reasoning.
Exactly. The more reasoning required by the prompt,
the slower GLM feels in practice. Execution -heavy
tasks are incredibly fast. Planning and creative
taste just take much longer. They both succeeded
on the creative task, though. GLM built an interactive
page called the Anatomy of Attention. Right.
It featured moving background elements and token
visuals. and Opus built the life of a Death Star.
It was a beautifully structured timeline -style
page. Both were excellent one -shot results.
The final test here was deep research. This utilized
a storm -style workflow. For anyone unfamiliar,
that just means multiple sub -agents work together
using different personas. Right. They compiled
a very rich, highly detailed HTML report. It
combined different expert lenses and actively
challenged its own assumptions. And GLM managed
this complex agent workflow beautifully. The
significantly lower cost changes the math entirely
for us here. Running multiple autonomous agents
suddenly becomes justifiable for daily work.
Yeah, you can test 10 different angles without
burning through your cash. But wait, if GLM struggles
with heavy reasoning, doesn't that make it too
risky for professional work? Well, not necessarily.
It is really about matching the model to the
risk profile. You have to match the specific
tool to the specific task. Right. Scaffolding
a basic website layout carries very low risk.
Exactly. But migrating a production database
carries extremely high risk. You always use the
heavy reasoning model for the high risk work.
So you don't compare final answers, you compare
your acceptable risk for the task. Precisely.
Which leads us to a much broader mindset shift
we need to discuss. Yeah. The real skill today
is knowing exactly when to use GLM 5 .2. AI work
is not a simple contest anymore. It is not about
the best model wins mentality. No, it is a multi
-step, highly iterative process. Most work involves
researching, drafting, and continuous testing.
Then you edit, make decisions, and finally ship
the product. Each of those steps demands a completely
different level of intelligence. Exactly. Let's
break down the 80 % work first. This is the natural
domain of GLM 5 .2. It easily handles first drafts
and gathering initial research. It does basic
web design and cleans up your messy notes. Right.
It generates your initial options for a project.
Cost matters immensely here because you are doing
constant iterative testing. You are exploring
different ideas. You don't want maximum pricing
when you are just sketching a rough draft. Exactly.
Then we have the remaining 10 to 20 percent work.
This is the domain of OPUS 4 .8. This is the
heavy thinking. Final reasoning, edge case review,
and high -risk coding tasks. The system context
is crucial here, too. The harness really matters.
Right. For clarity, a harness is just a digital
workspace where the AI can use tools. Cloud Code
provides that powerful harness for us. It lets
the model read local files and actively run terminal
commands. So GLM is not just a cheap OPUS replacement.
It is a cheaper worker operating inside the exact
same system. Mm -hmm. Beat. But aren't we just
complicating things by juggling multiple workers
for a single project? It might seem that way
at first glance. But using one expensive model
for basic sorting tasks burns cash needlessly.
Right. Splitting the work optimizes both your
budget and the applied brain power. You get significantly
better efficiency across the board. Let the cheap
model gather the lumber. Let the expensive model
build the house. Exactly. It is about working
much smarter within the environment you already
use every day. Placeholder for sponsor reads
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sponsor copy. Hearing about those massive price
differences makes me want to try this immediately.
Yeah. But whenever we talk about open source
routing, it usually involves spinning up servers
or Docker containers. Right. Is this actually
feasible for a normal user to set up? It absolutely
is. You are not learning a brand new tool here.
You are just routing the model call to a different
location. You simply route it to z .ai instead
of Anthropix server. Exactly. Step one is going
to the z .ai API console online. You can actually
test the model out right there first. They have
3D generation tools and small mini games available.
It gives you a great feel for how the model responds.
Yeah. Then you choose your preferred billing
method. You can pay per token or choose a set
monthly plan. Those monthly plans run roughly
$16, $64, or $144. There's some really great
practical advice on this point. Keep your Claude
plan active and just add z .ai on the side. Yeah,
you don't have to choose just one platform. You
use both of them together. Next, you generate
a secure API key inside the console. This brings
us to editing a specific local file. It is called
settings .local .json. You find this file sitting
inside the .clod folder on your machine. What
is brilliant here is that you aren't installing
heavy new software. Clod code is already looking
for a brain at a specific web address. Right.
All you were doing in that settings file is changing
the address book. You pointed away from Anthropic
servers. You route the base URL directly to z
.ai instead. You leave the Anthropic API key
completely blank, and you put your new z .ai
key in as the auth token. Finally, you set the
specific model name you want to use. It is surprisingly
simple. But there is a genius trick we need to
highlight here. You create two entirely separate
folders on your local machine. Yeah, this is
incredibly clever. One folder is named slash
gm. The other folder is named slash opus. You
put the custom routing configuration file only
inside the slash glm folder. Wait, so just by
changing directories in your terminal, you instantly
switch the brain powering your workspace, zero
friction. That is brilliant. You open clod code
in the glm folder for your rough drafts. You
open it in the opus folder for your final code
reviews. Exactly. But I have to ask a security
question here. If I put my API key in a local
JSON file, isn't there a risk I accidentally
share it? Oh, there is absolutely a major risk
there. That is why you must be extremely careful
with this file. Right. Treat that API key exactly
like a banking password. Keep it completely out
of public repos or shared team screenshots. Treat
the key like a password. Never push your local
settings to the public. Exactly. Keep it secure
and the entire workflow remains brilliant and
safe. Now, we really need to look at the bigger
picture here. Why does GLM 5 .2 make open source
AI models impossible to ignore right now? Well,
open source is finally practical for the daily
professional workday. It is no longer just a
theoretical weekend project for developers. The
massive scale of this model is truly staggering
to think about. GLM 5 .2 operates with around
753 to 756 billion parameters. Yeah. Let's pause
on that number for a second. That is far too
massive to run locally on a normal computer.
You would need serious, incredibly expensive
server hardware in your house. That is exactly
why API renting through providers is necessary.
It is the pragmatic middle ground for users right
now. You get all the massive power without the
massive infrastructure headache. This brings
us to the underlying cost of tokens. Tokens are
just tiny puzzle pieces of text. It is kind of
like stacking logo blocks of data. Exactly. The
AI processes these tiny pieces to understand
and generate language. When you look closely
at the token pricing, the math is undeniable.
Right. Opus costs $5 in and $25 out per 1 million
tokens. GLM sits at roughly $1 .40 in and $4
.40 out. Two secs silence. Whoa. I mean, imagine
scaling to a billion queries without bankrupt.
yourself. That five times price difference changes
everything about how we build software. It is
especially critical for complex agent workflows.
These autonomous models constantly read folders,
revise their own work, and call subagents. They
burn tokens invisibly in the background while
you drink your coffee. Exactly. Significantly
lower prices encourage you to experiment freely
without watching the meter. There are also performance
benchmarks to consider. GLM actively beats GPT
5 .5 and Opus 4 .8 on some specific software
benchmarks. Right, but there is a very crucial
point about those numbers. Benchmarks are just
a signal. They simply tell you a model is worth
testing yourself. Daily usefulness on your own
specific files is the only real test. Yeah, how
does it handle your unique code and your messy
personal notes? There is also a major strategic
advantage we should point out here. It acts as
a necessary hedge. Closed platforms change their
rules and their pricing structures all the time.
A vital feature can suddenly move behind an expensive
paywall tomorrow. Learning how to route open
source models protects your daily workflow from
those sudden shifts. But think about this dependency
for a moment. If open source models are this
huge, won't we always be dependent on cloud providers
to run them anyway? Yes. For now, that is the
reality. Yes. API rental is the necessary middle
ground today. Right. But it still fundamentally
breaks the monopoly. You aren't relying entirely
on one single closed ecosystems rules anymore.
It's about having a backup plan when the closed
platforms change their rules. Exactly. You maintain
your professional options and your creative freedom.
So let's bring all of these different pieces
together for you. We are looking at a fundamental
shift in how we approach knowledge work. The
era of treating open source models as clunky
budget alternatives is officially over. The true
modern skill isn't finding the one perfect AI
to do everything. You are essentially becoming
an orchestrator of multiple minds. Right. You
have to know exactly when to deploy a fast, cheap
worker. You use GLM 5 .2 for the heavy, repetitive
lifting and the rough drafting. And you must
know exactly when to bring in the expensive protectionist.
You call on Opus 4 .8 to close the deal and ensure
total precision. It is a beautiful synergy when
you set it up correctly. We want you to look
at your own daily tasks today. Identify the 80
% work you are currently doing manually. Where
are you overpaying for AI compute right now?
Where could a fast, highly capable open source
model handle the drafting and sorting for you?
Finding that exact balance will completely change
your daily productivity. Thank you for joining
us on this deep dive today. We always appreciate
you spending your valuable time with us. It has
been a truly fantastic exploration today. We
want to leave you with one final provocative
thought to mull over. If an open source model
is already handling 80 % of the workflow today
at a fraction of the cost, what happens to the
value of closed models when open source naturally
creeps up to cover 90 or 95 %? Does intelligence
become essentially free?