You've probably typed something into
Gemini, got an answer, and closed the
tab, the same way you'd use any other
chatbot. Here's the thing, that's maybe
10% of what it actually does. Use it
right, and it can quietly take over
almost half the busy work you're still
doing by hand. I spent hours mapping
every model, every mode, and every
product Gemini is quietly wired into,
and the number that stopped me was this.
Gemini's app alone has over 650 million
monthly users, and that's before you
count everyone using it inside search.
Most of them are using maybe 20% of it,
with no idea the rest even exists. Look
at this data from the Census Bureau.
Only about one in five US businesses
actually use AI in their operations.
So, if you run a business, and you're
even thinking about this, you're ahead
of most of your competition.
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below.
Click it, fill out a short form about
your business, and we'll get in touch to
set up a call. So, in this video, I'm
breaking down exactly what Gemini is as
of mid-2026,
every current model, every mode, and
everywhere Google has quietly built it
in. By the end, you'll know exactly
which Gemini tool to reach for depending
on what you're actually trying to do,
instead of just typing into whichever
box is in front of you. First, let's
clear up the biggest misconception.
Gemini isn't one product at all. What
Gemini actually is, here's the mental
model you need before any of this makes
sense. Gemini isn't a single AI, it's
Google's umbrella name for a whole
platform, a family of models underneath,
and a set of products on top that let
you actually talk to them. Think of it
in two layers. The bottom layer is the
models themselves, things like Gemini
3.6 Flash or Gemini 3.1 Pro. These are
the engines tuned for different jobs.
Some built for speed, some for heavy
reasoning, some for images or audio. You
never see these names unless you go
looking. The top layer is everything you
actually click on. The Gemini app, AI
mode inside Google search, Gemini inside
Gmail and Docs, the voice assistant on
your phone. All of those are just
different doors into the same underlying
models. That's the whole point of this
video. Google isn't trying to build one
great chatbot. It's trying to put the
same AI brain behind every product you
already use. So, let's start with the
brains, the actual models, because once
you know what each one is built for,
everything else clicks into place. The
current model lineup. This is a demo
checklist, so we're going model by
model. What it is, what it's actually
good for, and where you can get it.
Gemini 3.7 flash.
Launched on August 13th, 2026, this is
Google's newest flash model and its most
capable workhorse yet. It's built
primarily for coding and AI agents with
major improvements in software
engineering, web development, and
complex multi-step workflows. Google has
already made it generally available
through the Gemini API, positioning 3.7
flash as the new go-to model when you
want strong intelligence without giving
up the speed and efficiency the flash
lineup is known for. Gemini 3.6 flash.
This is Google's current flagship,
announced in a company blog post on July
21st, 2026. It's built as a workhorse,
strong at coding, knowledge work, and
multimodal tasks. And according to
Google's own numbers, it does the job
using about 17% fewer tokens on average
than its predecessor. Fewer tokens means
faster answers and a lower bill if
you're paying for it through the API.
You can reach it through the Gemini API,
through AI Studio, or simply by using
the Gemini app and searches AI mode. No
extra setup required. If you only
remember one model name from this video,
make it this one because it's what most
of Gemini is quietly running on right
now. Gemini 3.5 Flash. This one launched
back in May 2026
and it's the model that was actually
powering AI mode in search before 3.6
Flash took over. Google described it as
frontier-level intelligence at
exceptional speed and by its own
benchmarks, it pushed output throughput
to roughly four times faster than other
top models at the time. It's still very
much in active use across the Gemini
app, Google's anti-gravity platform, and
enterprise tools. It's slightly behind
3.6 now, but it's the model quietly
sitting behind a huge share of what
shipped this year. Gemini 3.5
Flashlight. Same July announcement,
different job entirely. This is the
stripped-down, high-throughput sibling.
Google sites roughly 350 tokens per
second, which is built for volume, not
depth. You wouldn't use this for a hard
reasoning problem. You'd use it for
background tasks and agent pipelines
that need to move fast and cheap at
scale. Gemini 3.1 Pro. Released in
February 2026, this is the reasoning
specialist. Google's benchmarks claim
roughly double the logic performance of
the earlier Gemini 3 Pro. It's mostly
gated behind preview access through the
API, anti-gravity, Vertex AI, and Pro or
Ultra subscriptions in the consumer app.
If 3.6 Flash is built for speed, 3.1 Pro
is built for depth. The model you'd want
on a genuinely hard problem, not a quick
one. And if you're actually building on
top of these through the API, price is
where the real-world decision gets made.
According to Google's own published
rates, 3.6 Flash runs about $1.50 per
million input tokens and $7.50 per
million output tokens. For context,
that's noticeably cheaper on the output
side than GPT 5.6 Luna's roughly $6 per
million. That gap is exactly why so many
developers default to flash tier models
for anything running at volume. Now, a
handful of specialty models worth
knowing by name, even if we don't dwell
on each one. Nano Banana 2 is Gemini's
current image generation and editing
model, replacing the older Imagen line
entirely. And that's not a small detail
because Imagen is actually shutting down
on August 17th, 2026. If you've had
workflows built on Imagen, that clock is
already running. There's also a Nano
Banana 2 light variant built purely for
speed, trading a small amount of quality
for much faster, cheaper output at high
volume. VIO 3.1 is Google's video
generation model, still in beta, built
to turn a text prompt into a short clip
with matching audio. Gemini audio 3.5
live translate handles real-time
speech-to-speech translation across more
than 70 languages, already built into
Google Meet and Android. And if you're
curious about the more niche end of the
lineup, there's a security-focused
variant called 3.5 flash cyber built to
coordinate with vulnerability scanning
tools. And Lyra 3.5, Google's music
model, which can now generate tracks up
to 3 minutes long from a text prompt.
Here's the honest limitation worth
naming. Google ships a lot of these
models fast, and the naming gets
confusing on purpose or not. 3.5, 3.6,
3.1 pro, flashlight, flash cyber. If
you're not building on top of the API
professionally, you genuinely don't need
to memorize this list. You just need to
know the shape of it. Fast and cheap,
deep reasoning, and multimodal. That's
really three categories wearing a lot of
different name tags. The modes you
actually interact with, models are the
engine. Modes are the steering wheel.
Here's where things get useful for
anyone who isn't a developer. AI mode
inside Google Search turns your search
bar into a conversation. As of IO 2026,
it's globally powered by Gemini 3.5
flash and instead of 10 blue links, you
get a written answer with follow-up
questions and sometimes an interactive
widget built on the fly. Anyone with
search can use it. No subscription
required. Ask something like, "What's a
quick dinner with what's in my fridge?"
and it answers in full sentences, not a
list of recipe blogs. Deep Think is the
extra effort version of the Gemini app.
It spends more compute per answer to
reason through harder problems
step-by-step. Google gates this one
behind Google AI Ultra and it's built
for genuinely difficult science or
engineering questions, not everyday
chat. Now, Deep Research is where this
stops being a chatbot and starts being
an assistant. You give it a topic and
instead of one reply, it plans a
research strategy, opens web pages,
reads them and if you allow it, pulls
from your own Gmail and Drive, too. What
comes back isn't a paragraph. It's a
full multi-page report inside Gemini's
canvas. This is the part of Gemini that
actually earns the word agent and we're
coming back to why that matters in a few
minutes. Gemini Live is the voice and
camera mode. Say, "Hey Google, let's
chat" and you're talking to it
hands-free with the option to point your
camera at something and ask what it's
looking at live. And Canvas is the
workspace mode. Type, "Create a quiz app
about planets" and it writes the code,
the interface and the content in one
pass, right there for you to edit. One
more worth a mention briefly because
it's still early. Gemini Spark, a
personal agent announced at IO 2026,
meant to run continuously in the
background handling things like
scheduling. Right now, it's limited to
early Ultra testers. So, treat this one
as coming, not here. Quick gut check
before we move on. If all of that sounds
like a lot of separate tools, that's
fair. But, notice the pattern. Every
single one of these modes is just Gemini
3.5 or 3.6 flash wearing a different job
title. You're not learning six different
AIs, you're learning six different ways
to ask the same brain for help.
Multimodal in practice. Let's talk about
what multimodal actually means
day-to-day, because it's more than a
buzzword on a slide. Gemini reads and
writes text and code. Obviously, that's
the baseline. But drop a photo into a
chat and ask it to caption or edit it,
and Nano Banana handles that. Ask it to
speak an answer out loud, and Gemini's
audio models generate that voice on the
spot with actual control over tone and
pacing. Ask for a short video and VO
builds one from scratch. Ask it to edit
an existing clip, swap the sky, change
the style, and that's a separate tool
called Gemini Omni doing frame-by-frame
editing by voice command. Inside Google
Docs and Sheets, the same underlying
models can draft a document from your
meeting notes or build a spreadsheet out
of a pile of invoices, complete with
formulas and charts. Not just raw
numbers dumped into cells. In Slides,
hand it a list of bullet points and it
can lay out an actual deck, not just
text on blank slides. And through Gemini
Live's camera mode, you can point your
phone at a menu in a language you don't
speak and get a live translation
overlaid on what you're looking at. Or
ask it to identify an object it's
looking at through the lens. No typing
involved. Here's the part worth
remembering. You never pick the model.
You just say what you want. Make this an
infographic. Translate this. Write this
in Python. And Gemini quietly roots the
request to whichever model actually does
that job. That's the design philosophy
in one sentence. One platform, and it
decides the plumbing so you don't have
to. Where Gemini actually lives. This is
the part that's easy to underestimate.
Gemini isn't confined to one app. It's
spread across nearly everything Google
ships. In Search, it's AI mode, already
covered. In Gmail, it's behind Smart
Compose and auto-reply suggestions. In
Docs, Sheets and Slides, Ultra and Pro
Pro
get Gemini drafting text, building
formulas, and designing slide layouts,
pulling context from your own files when
you let it. In Drive, it can find and
summarize documents for you. In Google
Meet, it's doing live caption
translation. On Android, especially
Pixel devices, it's baked straight into
the voice assistant. And there's a
Chrome extension that lets the browser
send page content straight to Gemini, so
you can ask questions about whatever tab
you're on. And for developers, all of it
is exposed through Google AI Studio and
the Gemini API, plus a newer platform
called antigravity for building
multi-agent workflows on top of it. The
strategic point here isn't subtle.
Google isn't trying to win the best
standalone chatbot argument. It's trying
to make sure you're never more than one
product away from Gemini, no matter what
you're doing on a Google device or in a
Google app. Agents, the part that
actually matters. Now, here's the shift
I promised earlier, the one that
actually changes what this platform is
for. Everything so far has been ask a
question, get an answer. Agents are
Google trying to move Gemini past that
entirely. Deep research is the clearest
example already live, plan, browse,
synthesize, write, without you
babysitting every step. Spark is the
early, still limited attempt at a
persistent personal agent running
continuously in the background. And on
the developer side, antigravity lets
companies build coordinated teams of
sub-agents. Google's own blog post gave
an example of businesses running
parallel agents to analyze data at
scale, rather than one model doing
everything sequentially. Picture the
difference in practice. The old way, you
ask Gemini, "What should I know before a
trip to Japan?" and it gives you a
paragraph. The agent way, you say, "Plan
my trip to Japan." and it checks
flights, compares hotel options, and
comes back with an actual itinerary,
pausing to confirm with you before it
books anything. That's the same
underlying model, just given permission
to take more than one step before
handing control back to you. None of
this is science fiction anymore, and
none of it is fully finished, either.
That's the honest read. Deep Research
genuinely works today. Spark is still in
early testing, but the direction is
unmistakable. Google wants Gemini to
eventually take a task, break it into
steps, and execute most of them without
you typing a follow-up for every single
one. What actually makes Gemini
different? So, how does this stack up
against everyone else building the same
kind of thing? Let's be balanced here,
because Google's advantages are real,
but so are its weak spots. The clearest
edge is data. Gemini can pull from live
search results, Maps, Gmail, and Drive
in ways that a closed sandbox chatbot
simply can't match without plugins
bolted on. The second edge is reach.
Every Android phone is a potential
Gemini client, and every Workspace
business account already has it
available. No competitor has that kind
of built-in distribution. And on raw
benchmarks, Gemini 3 Pro topped the LM
Arena leaderboard, which, regardless of
how much weight you put on any single
leaderboard, says Google's
infrastructure and DeepMind's research
are producing real, top-tier results,
not just hype. But, and this matters for
credibility, Google is genuinely more
conservative about rollout than some
competitors. Deep Think and Spark are
still gated behind ultra subscriptions
or limited testing, while some rivals
ship new capabilities to everyone at
once. And the tier structure itself,
free, pro, ultra, API pricing, can be
genuinely confusing next to a simpler
flat subscription from a competitor. If
you've ever opened the Gemini pricing
page and closed it 5 minutes later still
unsure which plan you need, that's not
just you. Where this is actually headed,
a few things are confirmed and a few are
still rumor, and it's worth keeping
those separate. Confirmed, Gemini 3.5
Pro is currently in partner testing with
a public release expected soon, and
Google has already started training on
Gemini 4, according to its own July 2026
announcement, though there's no public
timeline for that yet. Workspace AI
rollout continues expanding, and Gemini
Live's regional language support keeps
growing. Speculative and worth labeling
clearly as such, there's talk of a
dedicated on-device AI chip for future
Pixel phones, and some experimental
DeepMind research around 3D avatars and
world simulation that hasn't shipped as
a product. Treat both of those as
possible, not coming. Nothing official
has confirmed either one. The verdict.
So, where does that leave things? Gemini
in 2026 isn't a chatbot you occasionally
open. It's an AI layer Google has
threaded through search, Gmail, your
documents, and increasingly your phone
itself. The models handle the thinking,
the modes handle how you ask, and agents
like Deep Research are the clearest sign
of where all of it is actually heading.
If there's one thing worth trying this
week, it's Deep Research on something
you'd normally spend an evening looking
into yourself, and actually watching it
work instead of just reading the final
report. Drop a comment with which piece
of this surprised you most, the model
lineup, the agent side, or just how much
of this you were already using without
realizing it. I'll be back soon with a
deeper breakdown on how Deep Research
actually performs against a real
research task. Thanks for watching, and
I'll see you in the next one.