Imagine an AI that doesn't just, you know, assist
you, but actually builds things. You give it
a single prompt, and poof! Maybe a video game
appears. Well, that's not quite science fiction
anymore. Welcome to the Deep Dive. We're here
to untack the technologies really reshaping our
world. Today, we are going deep on OpenAI's GPT
-5, our mission, to figure out what makes this
feel less like just an update and more like a,
well, a genuine paradigm shift for automation
and maybe beyond. That's right. We're going to
explore what GPT -5 is. You can think of it almost
like a family of specialized AI brains. We'll
break down some, frankly, jaw -dropping benchmark
results. And uncover these surprising one -shot
creation abilities it seems to have. And then
we'll pivot. We'll look at the practical side,
what this means for automation, for real -world
applications. We want to offer you a kind of
strategic playbook for using this power efficiently.
And, like always, we'll wrap up by looking at
the future this tech is carving out. Our approach
today, calm, curious, focused on really understanding
this new frontier. Let's get into it. Okay, let's
unpack this then. The source material we looked
at calls GPT -5 a seismic event. When we hear
seismic event, I mean, what really makes this
more than just, say, GPT -4 .5? What's fundamentally
different here? Well, Sam Altman, OpenAI's CEO,
he put it pretty well, I think. He basically
described the leap from 4 to 5 as the difference
between talking to a really smart college student,
that was GPT -4, and talking to a PhD -level
expert, that's GPT -5. He said GPT -3 was maybe
like a high schooler. GPT -4, the college student.
But GPT -5, it acts like a true domain expert,
pretty much across any field you throw at it.
A PhD -level expert. Okay, that's quite a jump.
It really is. And for people actually building
things, you know, building automation, it boils
down to three critical improvements. First, just
better reasoning and problem solving. GPT -5
can work through these really complex multi -step
problems with an accuracy that we just haven't
seen before. Second, much better tool integration,
its ability to understand and use external tools,
APIs, which is vital for any real -world automation
that's dramatically improved. And third, this
is a big one, the cost efficiency is kind of
mind -bending. The input tokens for the main
GPT -5 model, they cost half of what GPT -4s
did. We're talking 1 .25 per million input tokens
for GPT -5 standard. That suddenly makes enterprise
-grade AI really accessible. So it isn't just
about raw power then, but more how it thinks
and how it connects things. Exactly. It's about
deeper understanding and real practical application.
Hey, here's where it gets, I think, really interesting.
The source mentions GPT -5 isn't just one single
model. It's a family of brains. What does that
actually mean for us? You know, the users, the
builders. How should we think about that? It
means you get choices and maybe more importantly,
optimization. GPT -5 isn't this one monolithic
thing. It's actually a suite of specialized brains.
Each one is tuned for different kinds of tasks,
different cost profiles. So you get GPT -5 standard.
That's your well -rounded genius. Good for most
general stuff. Then there's GPT -5 pro. That's
the real deep thinker for those super complex
multi -step problems that need maximum reasoning
power. Right. And what about for things that
are maybe simpler, but you do a lot of. High
volume tasks. Exactly. For that, you get GPT
-5 Mini. It's described as a hyper -efficient
workhorse. Great for high volume, maybe simpler
tasks where cost is a really big factor. And
finally, there's GPT -5 Nano. That's the most
economical one. Perfect for really basic high
frequency needs. And this new pricing structure,
alongside the family, it's kind of revolutionary.
It democratizes this advanced AI. Small businesses,
even solo entrepreneurs who might have found
GPT -4 a bit too pricey for heavy use. Now it's
potentially within reach. It feels like an economic
shift just as much as a tech shift. So you can
really pick the right brain for the specific
job you have in mind. Yes, tailoring the power
and the cost to your specific needs. Okay, let's
talk about the report card. The benchmarks for
GPT -5 show some pretty staggering performance
improvements. Let's dive into those numbers.
What do they tell us about what's actually possible
now? Yeah, the numbers are, they're pretty clear.
We've kind of stepped into a new era here. Take
SWE Bench. That uses real -world coding problems
pulled from GitHub. GPT -5 hit a 75 % accuracy
rate there. That's correctly solving nearly three
out of every four complex software engineering
challenges it was given. That's just a massive
leap for code generation. Wow, 75 % on real GitHub
issues. That's practically like having a pretty
dependable junior developer on call. It's certainly
getting there. And then there's its multi -language
ability. On a benchmark called Eater Polyglot,
this tests how well an AI understands. and edits
code across different languages, like Python,
JavaScript, SQL, you know, like a full stack
dev, GPT -5 scored 88%. That means it's getting
close to being a really dependable coworker for
development teams. Getting it right almost nine
times out of 10 on these tricky multi -language
tasks, that's huge. And then there's this thing
the source calls the miracle maker, single prompt
creations. That sounds, well, it sounds a bit
like magic. Yeah, this is where it gets genuinely
kind of mind bending, which is one single well
thought out. prompt, GPT -5 has apparently been
shown to create complete, professionally designed
landing pages. Or interactive, fully working
audio step sequencers. Little music tools. And
yeah, even fully playable, pretty complex spaceship
video games. And the key thing is, these weren't
tweaked over and over. They were supposedly generated
in a single shot. Whoa. I mean, imagine scaling
that kind of one -shot creation capability up
to, say, a billion users. It just fundamentally
changes things like rapid prototyping. You could
slash development cycles, accelerate getting
minimum viable products out there. Okay, stepping
back from the benchmarks for a sec, how do these
breakthroughs really translate into a builder's
dream, especially for automation? What specific
headaches does GPT -5 maybe solve? Right, for
automation builders, there are three things that
really stand out. First, that tool usage accuracy
we mentioned. GPT -5 is significantly better
at understanding and correctly using external
tools and APIs. That's just crucial for complex
workflows that interact with the real world.
Second, long context handling. It can keep track
of the conversation, the instructions, over much
longer multi -step tasks. Basically, it doesn't
forget what it was doing halfway through. That
makes it viable for much more sophisticated business.
And third, factual accuracy. There seems to be
a marked improvement, a much lower rate of hallucination.
You know, when the AI just makes stuff up confidently.
That obviously increases reliability quite a
bit. So it's not just smarter in theory, but
it's also more reliable in practice. And it actually
remembers what you told it earlier. Precisely.
It feels like a much more robust and trustworthy
AI partner. This really sounds like a perfect
match for automation platforms. The source actually
calls the combination of GPT -5 and NANN, that's
a popular automation tool, a perfect marriage.
Why is that synergy supposedly so powerful? What
makes them complement each other so well? It
really does feel like that. Think of it like
pairing, I don't know, the world's greatest architect
that's GPT -5's intelligence with the most efficient,
flexible construction crew that's N8N's platform.
GPT -5's huge jump in accuracy means you can
now automate more complex, even mission -critical
tasks with way more confidence. Fewer errors
mean more reliable business processes and probably
fewer late -night calls because something broke.
So it smooths out the rough edges, makes automation
less brittle. Yes, exactly. And beyond just accuracy,
GPT -5 is more of an all -in -one AI now. It
can handle text, images, code, complex reasoning,
all within a single model potentially. That simplifies
workflows a lot. Before, you might need to chain
together multiple different specialized AI services.
Now, GPT -5 might handle the whole chain right
inside your RN8N workflow. And maybe most importantly,
it democratizes this power. RN8N is visual. It's
low code or no code. Combine that ease of use
with GPT -5's incredible ability to follow complex
instructions. Well, suddenly, really advanced
automation becomes accessible to non -technical
users, maybe for the first time ever. So this
combination means more people can build much
more powerful things without needing to be expert
coders. Exactly. It's genuinely empowering for
builders of all kinds. Okay, for anyone listening
who's now itching to try this out. The guide
we read provides a step -by -step for hooking
up GPT -5 with NETAN. Can you give us the quick
rundown? How do people get started? Yeah, it's
pretty straightforward, actually. Yeah. First,
you need the key to the engine room, right? That's
your OpenAI API setup. Just go to platform .openai
.com, set up an account if you don't have one,
add a payment method because API usage isn't
free, and then create a new secret API key. Now,
critical step here. Copy that key immediately
and save it somewhere super secure, like a password
manager. Treat it like a password, maybe even
more carefully, because you won't be able to
see the full key again after you create it. Got
it. Guard that key. Then what's next inside NAN?
Right. Then inside your NAN workflow, you just
add an AI agent node. In its settings, you select
OpenAI Chat Model, paste in that secret API key
you just saved, and then, from the model drop
-down menu, you should now see options for GPT
-5 and its variants, like Mini or Pro. You just
pick the one you want to use. Now, a really crucial
note on billing, because this confuses a lot
of people starting out. Your... monthly ChatGPT
Plus subscription, if you have one, and the OpenAI
API. They're two totally separate products with
separate billing. ChatGPT Plus is kind of like
an all -you -can -eat buffet for the chat interface.
The API is a la carte. You pay specifically for
what your automation workflow uses based on tokens
processed. Ah, okay. So API usage is pay -as
-you -go, separate from the chatbot subscription.
That's important. So it's powerful, but you also
need to be smart about managing those costs.
Yes. Managing those API costs is absolutely key
to using AI. All right. Academic benchmarks are
one thing, but how does GPT -5 actually perform
in, let's say, a real -world cage match? against
the previous champ, GPT -4 .0. What happens when
the rubber really meets the road in an actual
automation workflow? Yeah, that's exactly what
we wanted to find out too. So the test wasn't
abstract. It was designed as a practical gauntlet.
We ran both GPT -4 .0 and GPT -5 through 10,
identical demanding test scenarios right inside
NE10. Things like generating complex emails based
on data from external tools or doing tricky database
lookups and summarizing the results. And importantly,
we used no custom system prompts, just raw out
of the box. the box intelligence from both models.
Okay. So head to head, what was the tail of the
tape? How did they stack up? Right. Round one,
accuracy. Here, GPT -5 landed a really heavy
blow. GPT -4 -0 scored a respectable 4 .2 out
of 5 .0 across the tests. But GPT -5, it hit
4 .7 out of 5 .0. It's a really significant jump.
It means far more reliable, much less error -prone
automation right out of the gate. Round two,
speed. Here, GPT -4 was consistently faster.
Now, this is likely temporary, maybe due to server
load from the GPT -5 launch, but for the moment,
the veteran GPT -4 definitely had quicker responses.
Interesting. And cost. That's always a huge factor
for anyone building automations at scale. Right.
Round three, cost. This was actually a bit of
a surprise. While GPT -5's input tokens are indeed
50 % cheaper, as OpenAI announced, it turns out
GPT -5 is often so much more thorough and detailed
in its responses that it frequently generates
a significantly higher volume of output tokens
compared to GPT -40 for the same task. So paradoxically,
this can sometimes lead to a slightly higher
overall cost per task with GPT -5, even though
the input is cheaper. It's kind of a you -get
-what -you -pay -for situation in terms of detail.
Honestly, I still wrestle with prompt drift myself
sometimes, you know. Finding that sweet spot
between getting a really thorough answer and
keeping the token count reasonable. Ah, that
makes sense. More detail costs more tokens. Okay,
so accuracy to GPT -5, speed to 4 .0, cost is
nuanced. Was there a knockout punch? What really
sealed the deal, if anything? Round four. Quality.
Yeah, this is where GPT -5 delivered the decisive
blow, I'd say. The sheer quality of the output
was just in a completely different league. The
responses felt dramatically more detailed, more
personal, more nuanced, just more human -like.
It didn't feel like just an incremental improvement
over GPT -4 .0. It really felt like a generational
leap in the quality of the interaction. And what
about the little guy, the mini version? Did the
featherweight contender make an impact in these
tests? Yeah, the featherweight. GPT -5 mini was
also put through the same 10 tests. It scored
3 .6 out of 5 .0 for accuracy, respectable, but
clearly below the bigger models. But here's the
kicker. Running all 10 evaluations cost approximately
$0 .03 in total API charges. Just $0 .03. So
the verdict there is pretty clear. For high volume,
relatively simple tasks where cost is absolutely
paramount, GPT -5 Mini looks like the undisputed
king of efficiency. So yeah, the overall judge's
decision from the cage match. GPT -5 is the clear
champion for accuracy and especially quality,
but there's currently a tradeoff in speed and
potentially slightly higher overall cost because
its output is so much richer. So it sounds like
for business -critical tasks, that boost in quality
and reliability probably outweighs the slightly
slower speed or the nuanced cost difference.
Absolutely. In the real world, quality and reliability
are usually what drive the most value. Okay,
moving beyond those structured benchmarks, how
does GPT -5 handle more complex reasoning or
even creative challenges? The source material
talks about testing it as an ultimate assistant
or even an AI art director. This starts to sound
like the JRVIS test, right? Like from Iron Man.
It absolutely does feel like that. We simulated
this ultimate assistance scenario. The mission
was intentionally high level. Achieve a business
goal that required coordinating multiple steps,
web research, looking things up in a database,
checking a calendar, drafting an email basically,
orchestrating several different tools seamlessly.
And what we saw was really a master class in
AI orchestration from GPT -5. What was the most
impressive part of that orchestration? What really
stood out? The most striking thing was what the
source called a self -healing workflow. During
the test, we intentionally sabotaged the API
key for the primary web search tool it was supposed
to use. Now, an older model probably would have
just failed, thrown an error, and stopped. But
GPT -5 correctly identified the specific error
it saw it was an authentication error. It automatically
retried the tool once, just in case it was a
temporary glitch. And when the error persisted,
it intelligently switched to its backup plan.
It used a different research tool, perplexity
in this case, to get the information it needed
and complete the mission successfully. That ability
to recognize a problem, try to fix, and then
execute a plan B. That's huge for building robust,
reliable automation that doesn't break easily.
Wow, okay. It can adapt to failures. What about
the creative side, the AI art director test?
How did that go? Right, so on the creative front.
We compared GPT -4 .0 and GPT -5 again. We gave
both models a really simple starting prompt,
something like a shark wearing a cowboy hat on
a classic car. And the task wasn't to generate
the image itself, but to generate a much more
detailed prompt that an image generation AI,
like Midjourney or Dell E3, could use to create
a really great picture. And the difference between
the two was, well, night and day. How so? What
made the GPT -5 prompt so much better? GPT -4
acted like a, you know, a competent technician.
It gave a solid literal description based on
the input. Serviceable. Yeah. But GPT -5, it
acted like a professional art director. Its generated
prompt was incredibly rich, detailed, and evocative.
It specified things like photorealistic cinematic
wide shot, weathered leather cowboy hat, tilted
at a jaunty angle. Mint condition, cherry red
1950s American classic convertible, sun bleached
desert highway, golden hour light, warm tones,
long shadows, low angle composition, shallow
depth of field, playful yet majestic mood, no
text or watermark. It was just packed with specific
visual and atmospheric details. That's, yeah,
that's amazing. It really paints a picture with
words. Exactly. And as you can imagine, the difference
in the final image you'd get from those two tromps
would be astronomical. One prompt gives you a
basic picture. The other gives you a potential
movie post or a whole story. This enhanced ability
to generate really creative, detailed, effective
prompts is a massive advantage for anyone who
needs to automate content creation, whether it's
for marketing, social media, anything visual.
So it can adapt to errors on the fly and it can
be incredibly creative. It's showing. deep intelligence
and artistic flair. Yes, exactly. It's a combination
we haven't really seen at this level before.
So let's bring it all together. What does this
actually mean for practical applications? With
this level of AI, what kinds of real -world problems
can we actually start solving now? Things that
maybe seemed impossible or just too impractical
before? Well, this new level of capability unlocks
a whole range of practical automation opportunities
that were maybe just out of reach. Think about
level two customer service agents. AI agents
that can handle genuinely complex, multi -step
customer inquiries, not just simple FAQs. Agents
that are fully integrated with your CRM that
can look up order histories, process returns,
troubleshoot issues, and importantly have a clear,
seamless escalation path to a human agent when
they hit their limit. Or imagine a content factory
workflow. automating your entire content production
pipeline from doing the initial research and
fact -checking to generating drafts in multiple
formats like blog posts, social media updates,
video scripts, all with built -in checks for
SEO and quality assurance. You could even build
sophisticated business intelligence engines,
systems that automatically pull data from various
sources, aggregate it, generate concise executive
summaries, maybe even create data visualizations
on the fly. Okay, that's a serious amount of
power. Which brings us back to strategy and cost.
How do we apply this power effectively? What's
the new playbook for building smart, cost -controlled
automations with GPT -5? Right, because with
great power comes potentially a great API bill
if you're not careful. So, smart cost management
is absolutely critical. The playbook has a few
key parts. First, strategic model selection,
like we talked about with the family of brains.
You need to pick the right engine for the job.
Use the powerful standard or pro models for tasks
needing deep reasoning or complex instruction
following. Use mini for high -volume, simpler
tasks where cost per task is key. And use nano
for the absolute simplest, most cost -critical
operations. Don't use the most expensive brain
for every single thought. Second, aggressive
token management. Think of the AI's context window,
the amount of text it can consider at once like
a briefcase. Every word you put in or get out
costs something. So optimize your prompts. Be
ruthless about cutting out fluff and unnecessary
words. Implement caching wherever possible if
you're asking the AI the same question repeatedly
or feeding it the same background info. Store
the answer and reuse it instead of hitting the
API every time. And consider hybrid approaches.
Maybe use a cheap model like Mini for an initial
pass, like filtering emails, and then only send
the really important ones to the more expensive
Pro model for detailed analysis. So being really
disciplined about what goes into that briefcase
and reusing information smartly is paramount
for cost. Exactly. And the third piece is batch
processing optimization. Think like an assembly
line. Making one single API call to process,
say, 10 customer reviews at once is far, far
more efficient and cheaper than making 10 separate
API calls, one for each review. So design your
workflows to group similar operations together.
Maybe implement intelligent queuing systems so
non -urgent tasks can collect and then be processed
in one large batch during off -peak hours. So
it's really about strategic deployment, choosing
the right tool, and being super smart and efficient
with tokens and requests. Absolutely. Efficiency
is what ensures profitability and allows you
to scale these powerful AI solutions. Okay, let's
zoom out one last time. If we connect all these
dots, look at the bigger picture. The release
of GPT -5 feels like more than just an update.
It feels like a signal, maybe, of some major
underlying trends in AI. From this vantage point,
what does the near future look like? Yeah, it
definitely feels like it's signaling at least
three big shifts on the horizon. First, what
the source calls the great leveling. This idea
that... Truly advanced AI automation, the kind
previously only available to huge corporations
with massive budgets, is now becoming accessible
to small businesses, even individual entrepreneurs.
This allows them to genuinely compete, maybe
even outmaneuver, much larger players by leveraging
AI smartly. It's a democratization of capability.
Second, we're getting closer to the JARVS build
-me -a -workflow future. GPT -5's strong performance
on coding benchmarks hints that we're rapidly
approaching a point where you might be able to
describe a complex automation you need in just
plain English. And the AI system could actually
generate a ready -to -run workflow for you, maybe
in a tool like N8n. Imagine just typing, build
me an N8n workflow that triggers every time I
get a new email with an invoice attached, extracts
the amount due and sender, adds it to my accounting
spreadsheet, and sends me a confirmation message,
and it just builds it. We're not there yet, but
GPT -5 suggests it's getting closer. Wow. AI
building AI workflows? And you mentioned a third
trend, teams of AIs. Right. That leads to the
third trend, the Avengers Assemble moment for
AI. This is the potential shift away from trying
to build one single monolithic AI that does everything,
like a solo Iron Man. Towards orchestrating teams
of more specialized AI agents, sometimes called
agent swarms, that work together, GPT -5's much
improved ability to reliably call and coordinate
external tools makes these kinds of multi -agent
systems far more feasible than they were before.
The future might not be about building the single
smartest AI, but about becoming like Nick Fury,
the director, who knows how to assemble the right
team of specialized Avengers, each with their
own unique superpower, to tackle a complex mission
collaboratively. So we're talking about AI building
AI and potentially teams of specialized AIs working
together on complex problems. Yes, it feels like
the next frontier is really about AI collaboration
and even self -assembly of solutions. Okay, so
the bottom line here for you, the listener, really
seems to be that GPT -5 isn't just another small
step forward. It feels more like discovering
a whole new continent for automation possibilities.
I think that's a good way to put it. It brings
dramatically improved reasoning, much better
tool integration, and crucially, a more accessible...
cost structure, especially with the family approach.
It's fundamentally more reliable for critical
tasks, and it enables more advanced, complex
systems that require less constant human hand
-holding. For platforms like NANN, this is massive.
For years, you could argue that the flexibility
of the automation platform itself was, in some
ways, ahead of the intelligence of the AI brains
we could easily plug into it. With GPT -5, it
feels like the brain has finally caught up to
the body. The potential is huge. So the race
has definitely started. It seems the question
is no longer if these tools will change pretty
much every business, but really who will be the
ones to actually build with them effectively
and lead the way. Absolutely. And my advice,
if you're listening and wondering where to start,
start simple, but start now. Pick one important,
tangible, real -world business problem you have.
Try building an automation to solve it using
these tools. Test it carefully. Refine it. Build
one strong, reliable system first. Get comfortable
with it. Then, slowly and confidently, grow your
AI -powered capabilities from there. Don't try
to boil the ocean on day one. That sounds like
solid advice. Thank you for joining us on this
deep dive into GPT -5. It's an exciting time.
This new era of genuinely powerful, increasingly
accessible AI is here. The time to start building
is definitely now. Out to your own music.