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Language: en
You're probably using chat GPT right now
and you've definitely hit that
frustrating moment where it just forgets
what you told it 20 minutes ago.
Maybe you're feeding it a long document
and it can only process part of it. Or
your conversation gets cut off just when
things are getting useful.
Well, I dug into the Gemini 3.5 leaks
and what I found is kind of insane.
We're talking about 2.1 million tokens
versus GPT5's 128,000. That's like
comparing a sticky note to an entire
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So, in this video, I'm breaking down
everything we know about Gemini 3.5.
From the leaked capabilities to how it
stacks up against GPT52 and its own
predecessor, Gemini 3. I'll show you
what these rumored features actually
mean for you in practical terms. First
up, let's talk about what makes this
model so different. Starting with
something called context length that's
absolutely mind-blowing.
The context revolution. Why 2.1 million
tokens changes everything.
Here's where things get interesting.
You know how current AI models kind of
forget what you told them after a long
conversation?
Well, Gemini 3.5 is rumored to handle
around 2.1 million tokens in a single
context window. Now, I know that sounds
like tech jargon, but let me put this in
perspective for you. Gemini 3 already
pushed boundaries with about 2 million
tokens. That's impressive. Sure.
But wait until you see this. GPT 5.2,
the latest from OpenAI, maxes out at
around 32,000 to 128,000 tokens,
depending on the configuration.
We're talking about a difference of
roughly 16 to 65 times more capacity.
Think about that for a second. What does
this actually mean for you? Imagine
you're working on a novel.
With current models, you might feed it a
few chapters and ask for feedback.
With Gemini 3.5's rumored capacity, you
could potentially feed it your entire
manuscript, every single chapter, every
character arc, every subplot, and ask it
to analyze consistency across the whole
thing.
Or picture this, you're a developer
working on a massive code base.
Instead of breaking your project into
tiny chunks, you could feed the AI your
entire repository and ask it to identify
bugs or suggest optimizations across all
your files at once.
This isn't just about bigger numbers.
This is about maintaining coherent,
intelligent conversations that span the
length of entire books. It's about an AI
that actually remembers what you
discussed days ago, not just minutes
ago. Beyond text, the multimodal
powerhouse.
But here's where it gets even more
fascinating. Gemini 3.5 isn't just
playing with words. This model is being
designed to truly understand and work
with text, images, audio, and video all
at the same time. And I'm not talking
about those chat bots that can sort of
look at a picture and describe it. This
is different. Gemini 3's Flash variant
already demonstrated something
remarkable. It can process hundreds of
images or hours of audio and video in a
single request. We're talking about 900
images, 8.4 hours of audio, or 45
minutes of video all at once.
Now, early reports suggest Gemini 3.5
will push these limits even further. The
implications here are staggering.
Here's a real world scenario that
clicked for me.
Think about a content creator who wants
to analyze their last month of YouTube
videos. With Gemini 3.5, you could
theoretically feed it all your raw
footage, your published videos, your
thumbnails, and your scripts. Then ask
it to identify patterns in what performs
best. Or imagine you're a teacher
creating educational materials.
You could provide the AI with textbook
pages, student work samples, video
lectures, and audio discussions. then
ask it to create a comprehensive study
guide that references all these
different formats. Naturally, the model
achieved 85% accuracy on mm bench.
That's a vision and language benchmark,
up from 78% in Gemini 2.
That might not sound like a massive
jump, but in the AI world, every
percentage point at this level
represents a significant leap in
understanding.
And this next part will surprise you.
Some leaks even hint at support for 3D
graphics and interactive outputs.
Speed that actually matters.
Now, you might be thinking, "Okay, but
if it's processing all this data, won't
it be slow?"
And that's exactly what I thought, too.
But wait until you hear this. Gemini 3
Flash runs approximately three times
faster than the previous 2.5 Pro model.
Three times. And it does this while
being dramatically cheaper to operate.
The leaks about Gemini 3.5 suggest this
speed trend will continue with code
names like Fierce Falcon. And yes, that
name alone tells you something about
their priorities.
Some reports claim response times could
drop below 200 milliseconds on high-end
hardware. To put that in perspective,
that's faster than the average human
reaction time.
We're approaching the point where the
AI's response feels instantaneous, like
talking to another person. This isn't
just about convenience. This
fundamentally changes what's possible
with AI powered applications.
Think about real-time translation during
video calls. Or imagine an AI coding
assistant that responds to your
questions as fast as you can type them,
making the programming experience feel
like pair programming with an expert
sitting right next to you. The Falcon
Twins, Fierce and Ghost. Here's
something that leaked recently that got
me genuinely excited. Google is
apparently testing two specialized
variants of Gemini 3.5 internally, and
they've given them some pretty
interesting code names.
Fierce Falcon and Ghost Falcon.
From what we've learned, Fierce Falcon
is optimized for speed and precision.
This appears to be the workhorse
variant. Think coding, data analysis,
factual research, anything where
accuracy and quick turnaround matter
most.
But Ghost Falcon, that's where things
get creative.
Ghost Falcon is reportedly designed for
creative design tasks, UI layouts,
graphics generation, game design
elements. The leaks suggest it can
generate scalable vector graphics,
create interactive game prototypes, and
even build simulated coding
environments.
Now, the reports also mention it needs
more tuning for consistency, which makes
sense. Creative tasks are inherently
more subjective and harder to nail down.
Both variants are being tested on
Google's internal Lamarina platform,
also called LM Arena, where they're
running simulations for game design, UI
mockups, and complex coding scenarios.
This tells us something important.
Google isn't just building a
generalpurpose model.
They're thinking about specialized tools
for specific professional workflows.
Gemini 3.5 versus the competition.
Let me address the elephant in the room.
How does this compare to GPT 5.2, which
is probably what you're using right now.
Gemini 3 already topped the LM Arena
leaderboard and outperformed its
predecessor on every major AI benchmark.
If Gemini 3.5 follows this trajectory,
and the leaks suggest it will, we're
looking at a model that could surpass
both Gemini 3 and GPT 4.2 on reasoning
and vision tasks. But here's the thing
about GPT 5.2, and this is important.
OpenAI's latest update is more
incremental. It's a refinement, not a
revolution. GPT 5.2 2 Turbo offers solid
performance with that 32,000 to 128,000
token context window I mentioned
earlier. It maintains excellent
reasoning capabilities and strong coding
ability. GPT5 always ranked high on
benchmarks like MMLU and GPQA.
The difference is this. GPT5.2 is like
upgrading from a really good car to a
slightly better version of that same
car. Gemini 3.5 based on these rumors is
more like switching from a car to a
spaceship. The approach is fundamentally
different. Where GPT 5.2 excels as a
powerful generalist with broad world
knowledge, Gemini 3.5 seems positioned
to dominate in large context reasoning,
multimodal tasks, and specialized
workflows.
Think about it this way. If you need a
model to write emails, answer general
questions, or help with typical everyday
tasks, GPT 5.2 is excellent and will
remain so.
But if you're working on something that
requires processing massive amounts of
information across different formats.
Maybe you're analyzing a company's
entire documentation set or building
complex applications that need visual
and textual understanding
simultaneously.
That's where Gemini 3.5's rumored
capabilities start to shine. The
developer advantage.
For those of you who build applications
or work in tech, this section is going
to matter a lot.
Claude 4, another competitive model,
recently scored 78% on swbench verified.
That's a coding benchmark.
Gemini 3 flash is already showing
promising results in similar areas. The
expectation is that Gemini 3.5 will push
these numbers even higher. What this
means practically,
imagine debugging or writing an entire
application in one continuous session.
The extended context means the AI can
track all your files, understand the
relationships between different parts of
your code, and make suggestions that
account for your entire architecture,
not just the snippet you're currently
working on.
And here's something that really caught
my attention. The leaked information
about interactive features. Reports
suggest Gemini 3.5 might support
browser-based operating systems and
interactive 3D applications, picture
weather simulations that respond to your
queries in real time, or mechanical
design tools where you can describe what
you want and see it rendered in 3D
immediately.
This points to something bigger than
just a chatbot upgrade. We're talking
about AI that powers entire interactive
environments.
What this means for regular users?
Let's bring this back to Earth for a
moment. If you're not a developer or a
researcher, you might be wondering,
okay, but what does this actually do for
me?
Here's the thing. These technical
improvements translate into real world
benefits that you'll notice immediately.
that extended context I mentioned. It
means you could have ongoing
conversations with an AI assistant that
genuinely remembers your preferences,
your projects, your goals over days or
even weeks. Not just in theory, actually
remembering and building on previous
interactions in meaningful ways. Content
creators could use Gemini 3.5 to
generate video story boards, game
levels, or interactive 3D scenes just by
describing them in natural language. The
leaked ability to create games and user
interfaces suggests powerful new tools
for indie game developers or UX
designers who might not have extensive
coding backgrounds.
Students and educators might see AI
tutors that can process entire
textbooks, video lectures, and practice
problems, then create personalized study
materials that adapt to individual
learning styles. With the massive
context window, the AI could track your
learning journey over an entire
semester, identifying patterns in what
you struggle with and what you grasp
quickly.
And here's something that often gets
overlooked in AI discussions. Cost.
If Gemini 3.5 follows the flash trend,
it could be substantially cheaper per
token than previous models.
Gemini 3 Flash was announced at only 50
cents per million input tokens. That's
about 75% less expensive than comparable
models. This isn't just about saving
money for big companies. Cheaper AI
means more apps can embed these
capabilities without passing huge costs
to users. It means advanced AI features
become accessible to smaller businesses,
independent developers, and eventually
average consumers. The bigger picture.
What we're really looking at here is a
shift in how AI integrates into our
daily lives. Current AI assistants are
impressive, but they often feel like
you're talking to someone with really
bad short-term memory. They're helpful
for individual tasks, but they don't
truly assist you over time. Gemini 3.5's
rumored capabilities suggest we're
moving toward AI that functions more
like a persistent digital colleague. One
that can maintain awareness of complex
ongoing projects. one that understands
context across different types of
information, your documents, your
images, your voice notes, your videos,
and connects those dots in useful ways.
Think about what this enables.
An AI that helps you plan an event could
remember every detail you've discussed
over multiple conversations. It could
research venues, coordinate schedules,
track budgets, and execute subtasks
without you having to repeatedly explain
what you're trying to accomplish.
This blurs the line between a static
chatbot and a true personal AI
assistant.
The timeline question.
Now, I know what you're asking. When can
I actually use this? And honestly,
that's where things get fuzzy. The leaks
and rumors don't provide a definitive
release date, though speculation
suggests something in late 2025 or early
2026.
Some industry watchers view these models
as Google's response to OpenAI's recent
advances, which would make sense
strategically.
What we do know is that Google is
actively testing these models
internally.
The Lamarina platform testing is
happening right now. The Fierce Falcon
and Ghost Falcon variants are real
projects being evaluated. Whether they
launch as Gemini 3.5 or under different
branding, the technology is in active
development. The practical reality
check. Before we get too carried away,
let's address something important.
Everything I've shared is based on
leaks, rumors, and analysis of patterns
from previous releases. Until Google
officially announces Gemini 3.5 with
concrete specifications, we're working
with informed speculation. Some of these
capabilities might be exaggerated. Some
might be scaled back before public
release. The context window might be
smaller than rumored.
The speed improvements might not be as
dramatic. The specialized variants might
launch later or differently than
expected.
But here's why I still think this
information matters.
Even if Gemini 3.5 delivers only half of
what these leaks suggest, it would still
represent a significant leap forward.
The direction is clear. Google is
pushing toward larger context windows,
better multimodal understanding, faster
inference, and more specialized models
for different use cases. The competition
between Google, Open AI, and other AI
labs benefits all of us. It drives
innovation, pushes costs down, and
expands what's possible.
Whether you end up using Gemini 3.5, GPT
4.2, Claude 4 or another model entirely,
the overall quality of AI tools
available to everyone is improving
rapidly.
What you should do now?
So, what should you actually do with
this information?
First, if you're currently using AI
tools in your work, start paying
attention to your context limitations.
Notice when your AI assistant seems to
forget earlier parts of your
conversation.
Think about how extended context would
change your workflow. This will help you
evaluate new models when they launch.
Second, if you're a developer or
business owner, consider how multimodal
capabilities might enhance your products
or services. The ability to process
text, images, audio, and video together
opens up entirely new categories of
applications. Start imagining what
becomes possible. Third, stay informed,
but stay critical. AI development moves
fast and not every announcement or leak
pans out as expected. Follow official
channels from Google, read analysis from
credible sources, and wait for verified
benchmarks before making major decisions
about which tools to adopt.
And finally, experiment. When Gemini 3.5
does launch, try it yourself.
See how it performs on your actual use
cases, not just on synthetic benchmarks.
The best AI model is the one that works
best for what you specifically need to
accomplish.
Final thoughts. The AI landscape is
evolving faster than any of us can keep
up with.
What seemed cutting edge 6 months ago is
standard today. What we're calling
rumors about Gemini 3.5 might be
outdated by the time it actually
launches.
But the trajectory is fascinating. We're
moving from AI tools that feel like
clever tricks to AI systems that feel
like genuinely useful collaborators.
We're moving from models that handle one
type of input to models that seamlessly
work across text, images, audio, and
video.
We're moving from systems with goldfish
memory to systems that can maintain
context across massive amounts of
information.
Gemini 3.5 represents where this is all
heading. More capable, faster, cheaper,
and more specialized for actual human
workflows.
Whether it lives up to every rumor or
not, the progress is undeniable.
The question isn't whether AI will
continue improving. It will.
The question is how you'll adapt your
work, your creativity, and your problem
solving to take advantage of these new
capabilities as they emerge.
Outro.
If you found this breakdown helpful, let
me know in the comments what you're most
excited about with Gemini 3.5.
Are you looking forward to the extended
context, the multimodal capabilities,
the speed improvements, or are you
skeptical about whether these rumors
will pan out? I'd love to hear your
thoughts. And if you want to stay
updated on AI developments without
drowning in hype, subscribe to the
channel.
I sift through the noise to bring you
what actually matters. Thanks for
watching and I'll see you in the next
one.