Speaker 1: If you've opened up an AI tool lately, instead at
Speaker 1: a list of names like flash so one, it opens Fable, Mythos,
Speaker 1: plus a pile of numbers that seem to go up
Speaker 1: and down at random, You are not alone. The naming
Speaker 1: within these platforms is genuinely confusing, and most of us
Speaker 1: just pick one and hope for the best. Today, Neo
Speaker 1: and I walk through the big four platforms Gemini, Chatch, FT, Cord,
Speaker 1: and Copilot and sort out which model to actually use
Speaker 1: and when. You will learn some tricks that cut through
Speaker 1: all these confusing names and how to avoid the token
Speaker 1: bills that have left some companies hundreds of thousands of
Speaker 1: dollars out of pocket just for one employee using the
Speaker 1: wrong models for the wrong tasks. By the end of
Speaker 1: this episode, you will know exactly how much brain power
Speaker 1: to throw at any given task for the AI tool
Speaker 1: that you're using.
Speaker 2: Welcome to how IAI with me, Doctor Amantha Imba and
Speaker 2: Neo Applin, head of Inventium AI. Each episode we share
Speaker 2: one practical way to use AI better at.
Speaker 1: Work and in life. No fluff, no tech jargon, just
Speaker 1: things you can use straight away. So Neo I feel
Speaker 1: like in the news at the moment, there is a
Speaker 1: lot of talk about new and different models launching, like
Speaker 1: Mythos and Fable, and they've all got different names, and
Speaker 1: then there's different numbers, and I feel like it can
Speaker 1: be very confusing to the average person what is going
Speaker 1: on with all these models you have?
Speaker 3: These AI companies are horrendous when they're naming their models.
Speaker 3: There's agents, there's GPTs, there's projects as models, there's fable.
Speaker 3: There's too many different terms that mean very little to
Speaker 3: the average person. But what it's about is how much
Speaker 3: the model thinks and how deeply it thinks through your problem.
Speaker 3: Is for the different models you've got, and so you've
Speaker 3: got smaller light models that do maybe a little bit
Speaker 3: of thinking, and you might have the heavier thinking models
Speaker 3: that are smarter and will work through your problem more. So,
Speaker 3: that's what it's about. It's about how much brain you
Speaker 3: would like to put towards your problem. Now, the thinking
Speaker 3: models will cost more for these companies to run. Therefore
Speaker 3: they'll let you use it less each hour, each month,
Speaker 3: each year, whatever, in a particular period of windows. And
Speaker 3: so you'll find that if you use the deep thinking
Speaker 3: big models, you'll get fewer uses each hour each day
Speaker 3: than you would if you're using the small and light models.
Speaker 3: So it's really about you're getting the right model for
Speaker 3: the right task. And so if you've got something important,
Speaker 3: use the thinking model. When you don't have anything particularly important,
Speaker 3: use a smaller model.
Speaker 1: So let's unpack the four main AI platforms in terms
Speaker 1: of getting into Gemini, CHATCHABT, Claude and copile it to
Speaker 1: understand and what are the models within each one and
Speaker 1: what should we be using when? How does that sound
Speaker 1: as a plan?
Speaker 3: Love? It sounds great.
Speaker 1: Let's start with Gemini, I think, and that's probably anecdotally
Speaker 1: for us and the clients that we're working with with
Speaker 1: inventium AI. We're not saying too many Gemini users. But
Speaker 1: let's start there and cover off what should we be
Speaker 1: using for what in Gemini.
Speaker 3: Now, when I go into this, I'll tell you the names,
Speaker 3: but also underneath the names when you've got there's a
Speaker 3: model picker on each of these tools. Usually it's in
Speaker 3: the prompting bar or around the prompting bar. When you
Speaker 3: see the names, there's always a little bit of text
Speaker 3: underneath it which gives you a clue as to what
Speaker 3: it is. So, for example, here in Gemini on my
Speaker 3: pick list today, I've got three point one flash light
Speaker 3: and then underneath that it goes fastest dances. Underneath that
Speaker 3: you've got three point five flash and it goes all
Speaker 3: round help. And then the three point one pro says
Speaker 3: advanced math and code. So it's giving you a clue
Speaker 3: of which one to use when. So you can then
Speaker 3: look at your go other numbers. Yeah, who gets but
Speaker 3: the numbers. The numbers will always change, but it's a
Speaker 3: little comments underneath that often mean more than the actual
Speaker 3: name of it. So in Gemini they've got three point
Speaker 3: one flash light, three point five flash and three point
Speaker 3: one pro. Now the numbers can be confusing because you
Speaker 3: look at it going three point one pro. Well, three
Speaker 3: point five is better than three point one, so three
Speaker 3: point five flash should be better than three point one pro.
Speaker 3: It doesn't actually work that way. So that's why I'm
Speaker 3: suggesting forget the numbers, go for the little text underneath it,
Speaker 3: so that the pro one, the advanced method code, would
Speaker 3: be the smartest model. And so when would you use that?
Speaker 3: You'd use it on things that matter. So if you're
Speaker 3: working out something like strategy, or you're doing problem solving
Speaker 3: or Hey, I'm dealing with this really complex problem and
Speaker 3: a market thing and a research thing, and how do
Speaker 3: I put those things together? Then you'd use the pro
Speaker 3: model here everyday use, I'd probably use flash And then
Speaker 3: if you've got something just as quick fast light though,
Speaker 3: summarize this paragraph into two words or whatever, I'd probably
Speaker 3: use flash light for that.
Speaker 1: And we'll get in to chat chapt next. But just
Speaker 1: as a general question with the deeper versus quicker thinking,
Speaker 1: something I wonder when let's say I'm working on refining
Speaker 1: a really important piece of content, and I think to myself,
Speaker 1: this is not a complex task like refining copy based
Speaker 1: on the rules that I give it, but it's a
Speaker 1: really important one to me. So in that kind of scenario,
Speaker 1: which model should I be using.
Speaker 3: If it's more language and just summarize these things, then
Speaker 3: I would be fine with a smaller model, one of
Speaker 3: those just everyday ones, which in this case in Gemini,
Speaker 3: the every day one is probably the three point five
Speaker 3: flash or you can get away with a flash light
Speaker 3: if it's just words and how do I put these
Speaker 3: phrases together? Things like that, But if you want it
Speaker 3: to actually analyze things and think through things, if it's
Speaker 3: important to get it right. In your case, it's important
Speaker 3: to get it right. I would definitely be using the
Speaker 3: everyday one here, that's the three point five flash rather
Speaker 3: than light because it's actually think through how it does
Speaker 3: its word summary, those kind of things. But often we
Speaker 3: need to do things that really matter. If it really matters,
Speaker 3: I'm often doing PRO. But if it's really matter, usually
Speaker 3: it's about thinking through a problem really matters, rather than
Speaker 3: just summarize the sentence. Well, I need to summarize it, right, Yeah,
Speaker 3: probably everyday model is probably fine for those things.
Speaker 1: Okay, let's move on to chat GPT. What are the
Speaker 1: different model options in chat GPT and what should we
Speaker 1: be using when they've improved.
Speaker 3: There's an instant then thinking, then PRO. That makes a
Speaker 3: lot more sense to me. Instant is don't think, just
Speaker 3: shoot me the answer straight away. And I love that
Speaker 3: and that's exactly what it does. Instant effectively, don't think,
Speaker 3: just do so use your own brain, go ahead thinking,
Speaker 3: we'll actually think through it. That's what I'm generally on
Speaker 3: all of the time. And then PRO, if it matters,
Speaker 3: that's the one you'd pick, So do not. You get
Speaker 3: quite a few pro uses, so a lot of people
Speaker 3: are too scared to use them until they actually need them,
Speaker 3: in which case you've actually got a whole bunch of
Speaker 3: potential uses that you've missed out on using. So do
Speaker 3: not be afraid to use the higher models.
Speaker 1: So let's look at Claude now, which I know is
Speaker 1: the AI tool that we use most often, and that
Speaker 1: certainly we're saying a lot of our clients moving a
Speaker 1: lot more towards Claude. And this is where certainly the
Speaker 1: term fable you might have heard in relation to Claude.
Speaker 1: What's going on there and what models should we be using?
Speaker 3: Okay, so Claude have four models at the moment, well
Speaker 3: almost four. So there starts at Haiku, that's their small
Speaker 3: fast light model, then it goes to Sonnet, then to opus,
Speaker 3: and then more recently there is fable. So instead of
Speaker 3: giving you the words like chachipt does what's chechibit? He
Speaker 3: got instant thinking and pro it kind of does what
Speaker 3: it says in the box. I kind of like that
Speaker 3: now with Claude, think of it as poems. A haiku
Speaker 3: is a Japanese poem which only has a couple of words.
Speaker 3: If it's only that three lights it's a tiny poem
Speaker 3: is a larger poem, and Opus is quite a large poem,
Speaker 3: and a Fable is obviously a huge poem. So it's
Speaker 3: about poem sizes. That's how they've named their models. So
Speaker 3: if you kind of get that in your mind, and.
Speaker 1: Can I just say neo, I've never heard anyone describe
Speaker 1: it like that. To me, that is quite revelatory, So
Speaker 1: thank you. Continue.
Speaker 3: So, yeah, these are the poem sizes and effective. It's
Speaker 3: how smart you want it to be and how much
Speaker 3: thinking you want it to do as well. So, as
Speaker 3: I said, we've got hik So and Opus. Don't worry
Speaker 3: about the numbers, just go for the names here. The
Speaker 3: numbers will always change. Now, just recently they had had
Speaker 3: released Fable. If you've also heard of Mythos, Mythos and
Speaker 3: Fable are pretty much the same model. This is the
Speaker 3: way you saw in the news. It was like, oh,
Speaker 3: we can't release it. It's too scary, it's too smart,
Speaker 3: a little hack every system in the world and all
Speaker 3: those kind of things. So Fable is Mythos. It's the
Speaker 3: full blown model. But they've just said you can't talk
Speaker 3: about certain things, so you can't talk about chemical stuff
Speaker 3: because someone might create chemical warfware. You can't talk about
Speaker 3: security stuff because you might try to hack something. And
Speaker 3: so what they've tried to do is get Fable without
Speaker 3: those problematic topics, and then they released it. They've released it,
Speaker 3: and since then there's obviously politics involved, but the US
Speaker 3: government has said, well, in a way, you could actually
Speaker 3: get it to review your security on your code, and
Speaker 3: so therefore, no, we're not happy with that, and because
Speaker 3: of that there's been discussions, and then Fable has been
Speaker 3: paused will put it that way, and so you can't
Speaker 3: actually use Fable at the moment. So for most people,
Speaker 3: do you need something which is as awesome as Fable, No,
Speaker 3: I'd probably say no. If you're getting AI to build
Speaker 3: your full software suite or whatever, Fable would be absolutely
Speaker 3: amazing for that. Opus is great Fable from everything I'm
Speaker 3: reading from people who've been using it, it's mind blowingly amazing.
Speaker 3: But it's not available just yet. So what I'd be
Speaker 3: doing is Haiku small fast light every day kind of small,
Speaker 3: like summarize this kind of stuff. Haiku is great. I
Speaker 3: work in Sonnet pretty much all the time, but then
Speaker 3: I turn it up to Opus when I need to
Speaker 3: because Opus is it does use more tokens, which we
Speaker 3: talk about later. But Opus is very smart and does
Speaker 3: an awesome job. But don't sit in the most thinkingest
Speaker 3: model all the time because you're just going to waste
Speaker 3: all your usage and then you won't have the great
Speaker 3: usage ready for when you need to use it.
Speaker 1: Okay, let's now talk about co pilot. What are the
Speaker 1: different models.
Speaker 3: There right now? I need to give you a little
Speaker 3: bit of market knowledge here. So Microsoft have had invested
Speaker 3: in chat ChiPT, so actually Copilot under the hood has
Speaker 3: a chat chipitt Brain, not just that you can use
Speaker 3: chat Chipt's models in Copilot, and they've also recently done
Speaker 3: a deal with Anthropic so you can use Claude inside copilot.
Speaker 3: So this is within the copilot model. Pickup, So in
Speaker 3: copilot you've got auto and I like the little text
Speaker 3: and decides how to think. It's got quick response. That's
Speaker 3: it's a qui equivalent of haiku, so don't think, just
Speaker 3: give me a response and then think deeper, which is
Speaker 3: its equivalent of something like the thinking ish models. So
Speaker 3: I'm generally on auto all the time and it will
Speaker 3: decide how long to think. But if it's important I
Speaker 3: will go think deeper because it will always do a
Speaker 3: better job of thinking if I tell it to think
Speaker 3: deeper than auto if it thinks so if it's important, strategy,
Speaker 3: market research, all those kinds of things, or how do
Speaker 3: I do this is really important, I'm going to get
Speaker 3: it right. For a client, absolutely think deeper. But I
Speaker 3: encourage you to do some tests and say, all right, oh,
Speaker 3: now try the Opus model because you can use Opus
Speaker 3: that's Claudes model inside Copilot, which is quite amazing. And
Speaker 3: there's also a GPT from AI Open AI and so
Speaker 3: you can actually use one of the GPT once you've
Speaker 3: got the think deepers in quick responses and all the
Speaker 3: same things, so you've got more choices in Copilot, which
Speaker 3: is actually pretty cool.
Speaker 1: Let's finish by talking about tokens because this is so
Speaker 1: important because you know you might just be listening and going, well,
Speaker 1: why wouldn't I just have the AI think deeply about everything?
Speaker 1: I mean, what are the implications of that? And I'd say,
Speaker 1: in if you're a solopreneur and you've just got your
Speaker 1: own individual license, or maybe you're in a small business
Speaker 1: where I think the pricing is a little bit different
Speaker 1: depending on how things are structured in your company. It's
Speaker 1: so different to if you are working in a large
Speaker 1: corporate and you've got an enterprise plan because what we
Speaker 1: know there is that often there's a per user charge,
Speaker 1: but there is also a charge for tokens. So how
Speaker 1: do we need to be thinking about this because quite frankly,
Speaker 1: I know we've both heard some nightmare stories around token
Speaker 1: usage going crazy and skyrocketing the prices that people are
Speaker 1: paying for their AI tool.
Speaker 3: Yeah, so it's all about II recommend use the right
Speaker 3: model that you need for the task you're doing. So,
Speaker 3: for example, I need to summarize twenty customer feedback things
Speaker 3: into a couple of themes. An auto model, a regular
Speaker 3: model is probably fine for that. Or you might need
Speaker 3: to say, hey, those same twenty comments do they reveal
Speaker 3: a product strategy risk or does that really impact what
Speaker 3: I need to do for the next kind of sets
Speaker 3: of investments that matters, in which case I probably use
Speaker 3: a larger top tier, a thinking model and Opus model
Speaker 3: for that. There's also a timeframe thing, So if I'm
Speaker 3: using Opus, it's going to spend a lot of time
Speaker 3: thinking through the thing. It will take me ages to
Speaker 3: get that response back and if you're going to get
Speaker 3: frustrated with it taking a lot of time, then you're
Speaker 3: wasting a lot of tokens. Okay, but let's actually go
Speaker 3: back to the token thing. So if you're in a
Speaker 3: particular plan, and only some plans have this, So if
Speaker 3: you're in say everyday claud plan, this is a thirty
Speaker 3: bucks a month plan, then you will have a usage limit.
Speaker 3: So you can actually go to your settings page and
Speaker 3: under settings there's a thing called usage and you can
Speaker 3: see that you've got a five hour windows, a little
Speaker 3: bit of a bar how much you've used, and there's
Speaker 3: a weekly limit window and there's a bit of a
Speaker 3: bar there, and you can also top up and buy
Speaker 3: more on top of that if you need. And what
Speaker 3: they're basically saying is you can have a certain number
Speaker 3: of uses of your different models over that five hour
Speaker 3: window or one week window until we say you've used
Speaker 3: it enough, you've overused what our models do now, Haiku
Speaker 3: is about two to three times cheaper than Sonnet, and
Speaker 3: son It is minimum two times cheaper than Opus, so
Speaker 3: there's like a minimum six times differential in price on
Speaker 3: these different models. So if you're using Opus for hey,
Speaker 3: summarize this email, you are burning through your tokens and
Speaker 3: you'll hit at the end of the five hour period
Speaker 3: and AI is going to say tote sos. You're going
Speaker 3: to have to come back in a couple of hours, right,
Speaker 3: And that becomes a problem if you actually needed to
Speaker 3: get some work done. So picking the right model means
Speaker 3: you can actually get some work done, which is great.
Speaker 3: Now if you're in a company plan, and some companies
Speaker 3: have a plan where it's simply you can you've get
Speaker 3: access to it. But each time you get AI to
Speaker 3: run a query to get a prompt or together to think,
Speaker 3: that goes kachin kachin, kachin kachin on your bank balance.
Speaker 3: So if that's the case, then you might be costing
Speaker 3: an awful lot of cash to your poor IT department.
Speaker 3: And we hear stories where some companies have got like
Speaker 3: a five hundred thousand dollar AI bill for that month
Speaker 3: and things like that because people have just been using
Speaker 3: these things on summarize this email or stuff they don't need,
Speaker 3: or they're letting it go crazy. And so my encouragement
Speaker 3: is pick the right model for the job. But also
Speaker 3: if you do have, say Claude, check your usage limits
Speaker 3: and see whether you're actually using it right, so you
Speaker 3: can actually see whether you're over using any of these models.
Speaker 1: And just for context, that five hundred thousand dollar AI
Speaker 1: bill that you talk about, that was just from one
Speaker 1: employee from memory that particular story that you're referring to,
Speaker 1: and they.
Speaker 3: Were also getting AI. This is APIs and chlor Cowork
Speaker 3: and sorry clock code, I should say, and all those
Speaker 3: kind of things. So they were going nuts, right, But
Speaker 3: here's the they were going nuts but not watching what
Speaker 3: they're doing, and that's coming to be a bigger problem
Speaker 3: because like Claude has its own usage limits and a
Speaker 3: lot of companies they're off this. You know, you can
Speaker 3: be the five our limit you stop and instead it's
Speaker 3: just a paper usage. There's another thing coming out in
Speaker 3: a couple of months time, which is Microsoft Cowork, which
Speaker 3: is the same as clor COK in Microsoft, they will
Speaker 3: also have usage limits. So picking the right model is
Speaker 3: good practice now for when all of these things go
Speaker 3: to usage only.
Speaker 1: Thank you so much, NEO. Hopefully you are now feeling
Speaker 1: more confident and clear in terms of which model you
Speaker 1: should be using for all the different tasks that you're
Speaker 1: using AI for. Thank you so much for listening and
Speaker 1: we will see you next week. How IAI was hosted
Speaker 1: by me Amantha Imber and Neo Applan. A big thank
Speaker 1: you to Martin Imber who does our sound editing, and
Speaker 1: Jim Rubio for production support, and thank you to John
Speaker 1: Kilby who composed the theme music