The World's BEST New AI Model is 100% Free (Kimi K2 Thinking)

The world's latest and greatest AI model

is 100% free for you to download and run

at home right now. Kimmy K2 thinking is

the latest reasoning model from Moonshot

AI Labs, which is a Chinese frontier AI

lab, and it beats OpenAI's GPT5,

Anthropics Claude, and Google's Gemini

across pretty much all benchmarks. But

that's not even the most shocking part.

The most shocking part is that it only

costs $4.6 6 million to train and build,

which is only a fraction of the billions

of dollars spent by OpenAI to train GPT

in the first place. It's also 100% open

source, which means that you can

download and run Frontier AI right at

home where you're sitting right now. Um,

but of course, it begs two very

important questions. Number one, is

open- source AI the winning strategy?

We've been led to believe that closed

source is typically the better strategy

when you run a business, but China and

their AI models are proving us wrong

here. And the second question, the more

ominous question is, will the US stock

market bubble finally pop? Josh, what

have we got here? What is this new

model? And why is it taking over social

media everywhere I look? They did it

again. The Chinese did it again. They

knocked it out of the park. Grand slam,

home run. It's an unbelievably

impressive model. And this happens every

time. We get this amazing flagship model

out of the US. A couple months later, we

get the same thing marginally better at

onetenth of the cost. Like full orders

of magnitude cost less than what it

costs for the leading AI labs in the US

today. The specs are really impressive.

We're going to get into everything.

We'll start with I guess just like the

high level spec sheet. State-of-the-art

on um humanities last exam, which is the

reference point that we kind of use in

terms of benchmarks. It scored the

highest anyone's ever scored, 44.9%.

Um, it has a bunch of these really cool

breakthroughs, but the big thing that it

excels at, like it says in the post

here, reasoning, agentic search, and

coding. Now, there's a few cool things

that we could talk about here, EJ, maybe

we'll just get into the charts because I

feel like that's an easy way to

visualize how much better this model

really is than all the others. And what

we're seeing on the chart is that well,

GPT5 was the best. Kim K2 is now the new

best. And this is as it relates to

thinking and reasoning. And this again,

this is so impressive because one, this

model is fully open source. You can go

download the model and run it yourself

locally for free. What were your first

thoughts when you saw this? Cuz to me, I

was like, oh my god. Like, why would I

use anything else? My first thought, if

I'm being honest, Josh, was like to look

at the stock market. I was like, is this

going to crash the entire US stock

market? Like when Deepseek initially

released their uh R1 thinking model, do

you remember? Uh it was at the end of

last year. um people's kind of entire

bubble and vision of how AI models were

trained was completely burst. And since

then, China has repeatedly delivered on

breaking uh models, one of which is uh

the Moonshot AI lab team which built Kim

K2. Um it's such an impressive model for

a few different reasons. Uh for me,

number one, it can now compete with all

the best. And um personally, GPT5 is

something that I use pretty much every

day, whether it's for like kind of

casual prompts and requests or whether

it's kind of like for deeper thinking

and research and some of the lines of

work that I do. Um, so it's become kind

of like quintessential for me now to

have a a separate model that I can

download and run privately on my own

computer at home that I'm showing on

this tweet here that costs60

uh per million token input and $2.5

output is just an insane cost cutting

average where if I was running a

business using an AI model, I would

there's like very little reason for me

not to switch over to something like

this aside from maybe like maintenance

and setup and and stuff like that. The

other really impressive thing for me,

Josh, um was the team itself. Like this

is only a 2year-old startup, which

reminds me of another 2-year-old

startup, which is Elon Musk's X AI,

right? And there's a funny link between

these two models, Josh, which is um

Kimmy K2's reasoning, this thinking

model, um can do so because it does this

like really neat little chain of thought

experiment where it takes many steps to

kind of think to a logical answer versus

just kind of like splurging an answer

for you. That's something that Grohee 4

did uh when they that they pioneered

when they launched their new product.

So, Kim K2 is kind of like drawn on some

of these learnings from XAI to to

produce a similar model. The other

really cool thing is it does this thing

called tool use or tool calling whilst

it's thinking. Um, so if you imagine uh

as I'm kind of like trying to think

through a complex problem, um, I will

leverage different tools to be able to

help me get to the answer. So, if I'm

doing a maths exam, I can use a

calculator or if I'm doing a deep

research question, I might use Google.

Um, this AI model naturally does and has

access to over two to 300 different tool

calls and tool uses whilst it does its

thinking. So just overall a very

impressively new looking AI model.

>> Yeah, EJ, you mentioned the the cost

being 60 cents per million tokens. And I

just want to add a little bit of context

as to how low that actually is. I was

looking at the the GPT5 Pro cost for per

inputs and it is $15 per million tokens.

15 for the GPT5 Pro cost. Currently, the

output is $120

per million tokens. Granted, this is the

top of the top. If you're using GBT5

standard, input is $1.25 per million

tokens. Output is $10. So any way you

you scrape it, it's at least a 2x multic

cost reduction up to like 100x on the

highest end assuming it can compete with

GPT5 Pro which all those benchmarks

suggest it very well can. So the cost is

really like it's it's a big deal and to

get kind of dig more into the the point

that you were making e and how it

actually works. Well, we get to first

sorry no we'll get there. Save the

memes. Don't spoil the memes yet. We got

to get to the funny jokes next. But

basically the way this works is like

there's this very complicated diagram on

the screen. I'm not going to try to even

explain what that is. But there's this

this fun way that I like to describe it

when when I was describing it to my

friend earlier this morning, which is

that like Kim K2, it's like this giant

school and it has these things called

specialists. And in fact, Kim K2 has 384

specialists. You could think of these

specialists as like a math club or a

history club, coding club, debate,

whatever it is. And when you ask it a

question, it doesn't invite the whole

school. It doesn't invite all the clubs.

It's just ej, if you ask for a math

question, it will query the math club

and it chooses eight out of those 384

clubs to help combine their answers,

pick the experts, and decide how it's

going to solve this problem. So, it has

a trillion parameters, but it only uses

32 billion of them at once. And that's

how we're able to get the huge cost

reduction because it uses this thing

called mixture of experts. A lot of

people describe it as but basically what

it is is instead of using the entire

model's intelligence to answer what

should I have for breakfast this

morning. It will take the chef club, it

will take the health club, it will

combine those together and it will form

an answer that should hopefully give you

just as good as a result if you took the

entire model, but it's much more

efficient in terms of cost, in terms of

energy, and in terms of the amount of

tokens it could generate because it's so

much cheaper across the board. And I

think that's one of the big really

exciting things that has been cool to

see coming out of China. We saw it with

Deep Seek, we see it with Kimmy and it's

this mixture of agents architecture

where they're really kind of

modularizing the entire model and only

using the stuff that's important for the

specific query. Um they were put in a

very constrained position um which is

they didn't have access to the latest

GPUs or Nvidia GPUs. There's been a

bunch of US tariff restrictions on

Chinese labs getting access to these

kinds of things. So they've really

needed to kind of like work within their

bounds and means. Um and so coming up

with an architecture like mixture of

experts or the one that they did is

super important. And it brings me to

this meme, Josh, which is what are we

doing here? There is an obvious mismatch

between Americanmade AI models and the

uh Chinese ones. Uh you've got Open AI,

which is now projected to spend $1.4

trillion over the next 5 years. That's

trillion with a T versus Kimmy training

for $4.6 million. Now, I know there's a

bit of like clickbaitiness here. That

$4.6 million was rel relative to one

training run and usually takes a few

training runs. But let's say it took

like 20 training training runs, right?

At $4.6 million, that's still only like

a like a 100 mil, right? Or less than

that. So, it doesn't really matter when

you put it into the context that GPT5 is

rumored to have cost 1.7 to 2.4 $4

billion for OpenAI to train. So, there's

a mismatch that I don't quite

understand, Josh. And that's what makes

me the most nervous when it comes to um

what Americanmade companies and Frontier

Labs are doing. I feel like they're

missing the mark. I don't quite know

what it is, whether it's this mixture of

experts thing, but there's someone's

being sold a lie and I don't know

whether it's me or whether it's um me

like looking at this Kim K2 model and

being like, "Wow, it's so amazing."

>> Yeah. When I think about the role that

China plays versus the United States in

terms of like open source companies or

closed source companies here in the US,

uh the the thing that is reassuring to

me at least is a lot of these innovative

breakthroughs that happen on the

software level actually do happen in

these private AI labs. Um we do get like

chain of thought and reasoning and

there's like this whole slew of new

innovation that becomes standard very

quickly. That all happens in the United

States AI labs. And as far as we're

concerned, the AI labs in the US still

have they're making the most progress

the fastest. They are creating the most

innovation. And then what you kind of

see like we described earlier in the

episode is that innovation starts to

trickle down whether it's voluntary or

whether it's stolen and it gets

implemented into these new models. And

they just completely cut out the bottom

in terms of cost and efficiency because

that's kind of all they're able to do.

They don't have access to the resources

of like millions of GPUs from Jensen

Hong and Nvidia. They don't have the

access to $50 billion of capex just to

spend on employees, just to spend on

salaries and compensation. Um, so it

seems to me like I mean we're still

doing very well. It's just China is very

good at implementing the technology and

applying it at scale in a way that's

open sourced. and the open source thing

there's there's a lot to say for that

because it's it's very impressive and

it's kind of this community effort that

we saw early days with the United States

but once they became better they closed

it off so what happens is you get

innovation in one company like Kimmy and

then you see it implemented in deepseek

and then you see it implemented in Quen

and then suddenly this technology is is

kind of synchronously growing between

the three because it's all open source

they're publishing all the code all the

open weights and it's much more easier

for them to thrive whereas innovation in

the United States very much happens

behind behind a closed wall and it's

only leaked out at the advent of a new

model when they release it to the world

and people kind of reverse engineer how

it works.

>> Mhm. Um I was reading an article in the

Financial Times where they interviewed

Jensen Hang um and he said verbatim that

China will win the AI race if they

continue down the path that they're

currently on and if the US doesn't kind

of ramp up their energy production. he

was making a wider point that their open

source strategy is uh pretty effective

in the way that they're that they're

building these new AI models with the

constraints that you just mentioned. Um

kind of speaking more about the open

sourceness and the benefits of this. Um,

I I've got a tweet up here which shows

that Kim K2 uh thinking this new model

can basically run on two MacBook M3

Ultras, which is the like a couple of

thousand dollars worth of cost, which is

an insane thing to do to run a Frontier

AI model at home privately in your house

trained and fine-tuned on any of your

own private data. So, you don't need to

kind of like sell that data to Sam or

Mono or whoever. Um, just super cool and

super cheap, right? Cuz you're running

local inference at home. So you don't

have to worry about anyone kind of like

spying on any of your queries or your

prompts or your research. It's just all

at home which I thought was super cool.

Um the other part of the open sourceness

which I found interesting Josh was the

fact that they had an MIT license with

this new release or an adjusted MIT

license and we'll dig into that in a

second. But the point being when

Deepseek um released their first major

open source model and it took the world

by storm there wasn't any major licenses

around that. So you could pretty much

download and do whatever the hell you

wanted to it for it. You could implement

it into your own product whether you

were an American founder and if let's

say you scale that up to a million users

that used a feature that was um

leveraging that deepseat model you

wouldn't have to credit that team at

all. Um, Kim K2 kind of like takes a

step in a different direction here where

they've released an MIT license where I

think if you hit I think it's either 10

million or 20 million users for your

product, you need to show the Kimmy K2

label and say that listen, I'm using

this model under the hood, but there's

uh there's some differences with this

license, right, Josh? Um, can we can we

dig into that?

>> I believe it's it's modified. I don't

know to the extent that it is modified,

but I know that there is something

different going on here. What does this

say? Our only modification part is that

if the software or any derivative works

thereof is used for any of your

commercial products or services that

have more than 100 million monthly

active users or more than 20 million US

or equivalent other currencies in

monthly revenue, you shall prominently

display Kimmy K2 on the user interface

of such product or service.

>> That's a fun little marketing ploy. Fair

enough. Fair enough. You know what it

reminds me of, Josh? Um, it's what Meta

tried to do with their llama models,

right? So, um, Meta is the only other

major American company that I can think

of that went down this opensource AI

route. And the goal or the intended goal

at the time was to basically level the

playing field uh, between Meta and Open

AI and other frontier model AI labs

which had raced so far ahead. So if you

released all this cutting edge AI tech

for free and accessible to anyone then

it kind of drives down the cost premium

that open AI and all these other

frontier AI labs can charge you uh to

access this thing. China's doing that as

a vast hole on the on the American AI

stock market, right? So that's why we

saw like Nvidia crash I think 4.2% on

the news getting released and such. Um

I'm curious whether this kind of pops

the bubble and the capex bubble in

America. Josh, is that a crazy thing to

say? I mean, the markets reacted pretty

viscerally to this news.

>> I I don't think I have a problem with

this. I don't think it's popping a

bubble. I don't think we're in trouble.

I think this is just totally fine so

long as we continue to stay slightly

ahead or at least at par. I think we're

really excellent at making software,

distributing software, creating

products. I think China's really good at

shamelessly innovating and deploying

without needing to go through all the

hoops and intellectual

problems that the United States mostly

has. Um, so I don't think this will lead

to any sort of bubble popping. I think a

lot of the frontier innovative stuff

still happens in the US. The place where

I will begin to start to get a little

worried is when this switches to

embodied AI. Once we start moving from

large language models to implementing

these into robots or implementing these

into physical hardware, that's where I

think we have problems. On the software

front, we're good. We're crushing it.

Everyone's spending tons of money. Um,

on the hardware front,

>> we don't have the same lead. And over

the last what 30 to 50 years, we've kind

of outsourced our manufacturing

capabilities to other places and

therefore are just kind of I mean

everyone knows we just can't really make

things cost effectively here in the

United States. If we are at a foot race

with China when it comes to making

embodied AI like humanoid robots,

specialized robots, whatever it may be,

that's where things start to get a

little bit scary because that's where

there is a significant lead and that

lead comes in the form of atoms which

are much more difficult to move than

bits because you can steal some open

source code, create this slight

innovation on top, roll it out to a

billion users overnight and that's

innovation. That does not happen between

version two and version three of your

humanoid robot. you actually have to

build it with a factory with real

materials and people and places and it's

it's very difficult and challenging to

do and China very much stands to be the

largest winner in that so I think on the

software front I feel really confident

and as of now that's all that we're

battling on but in this near future

where things start to become embodied

where AI be becomes physically

manifested in the world around us that

that seems like a place where I would

start looking at Chinese investments a

little bit more than the American ones

>> okay I I I think uh I might push back a

little bit and say that there is

reasonable evidence to be bearish on the

software side before it gets to embodied

AI. I mean, so a few ways to think about

it. Um, there is such a gross

discrepancy when it comes to capital

expenditure for these things. On one

side, you've got the US spending

trillions of dollars literally to train

AGI or the best AI models, and on this

side, you're you're in like the hundreds

of millions of dollars, which is like an

order of magnitude less, right? Um, so

there's an obvious mismatch here that we

aren't seeing. Uh, whether it comes down

to training architecture, training

design, or just kind of like hardware

manufacturing. I don't know where that

um kind of advantage is being played.

But the Chinese have found it and

they're able to kind of really push down

on that lever to get ahead or on par

with the US. And they've been able to

successfully do this for years now. At

this point, Deepseek was kind of like

test case one. Now I've seen like you

know at least 50 open source models come

out of um Chinese Frontier AI labs since

then. Um number two it's not like the US

government has kind of like not tried to

to constrain them. Um we've imposed a

number of different sanctions which

include you know u constraining um which

GPUs um Nvidia and other manufacturers

within the US can sell to China. But

that still hasn't stopped them. um

they've been able to maintain and train

these frontier AI intelligences despite

all of these different things. So um I

think if I were to look on the other

side of this, it would be so what if you

have an open source model that is super

cool. Um why aren't you using it right

now? Like I'm not using Kimmy K2

regularly, even though I use GPT5 and it

might be better than GPT5. And the

answer for me is pretty simple. Um I'm

locked into an ecosystem in OpenAI that

I'm pretty happy with, which is um it

has memory on me. It understands who I

am. It has a context of all the previous

chats that I have with it. But also,

most importantly, Josh, if there's an

issue with something on my account or

something that I'm trying to use,

there's a community that I can access.

There's a support team that I can speak

to. There's a software ecosystem that

supports me, right? Um, versus me

jumping ship to kind of Kimmy K2,

setting it up on my own, and then having

to like troubleshoot it myself. I think

a lot of people will be disincentivized

to to to do that. It's it is difficult

but I mean we're seeing market forces

from both sides right like I I saw you

included a link here somewhere where

Kurser and Windsurf's um new AI models

they they were using some sort of

Chinese models and in fact they were

thinking in Chinese and I found this

really fascinating that like

Americanmade products are now thinking

in the Chinese language. So that's

certainly a concern in terms of the

commercial side where those API costs

really matter where if you can get a

million tokens for 60 versus $10 that's

that really affects the margins of your

business. For consumers like us um there

there's no real interest to use Kim K2

and the phenomenon you spoke about

earlier where you can actually run a

quantized version of Kim K2 on two Mac

studios running the M3 Ultra chips. uh

it generates tokens at like 13 to 15

tokens per second. So it's very slow

like that you're you're getting like a

sec a sentence or two every second. Um

which it's it's much slower. It's going

to feel groggy. It's not going to feel

well. There's a case to be made that

that changes because this year and it's

funny that Apple's really the only

computer that that supports this now.

They're releasing the M5 Ultra, which

will be the new version. And um it's

going to be interesting to see how it

plays out. What I found interesting,

this one side note actually that I

wanted to share with you e because you

might find it cool too is the version

that runs on these Apple computers, the

Apple studios, um it's a it's a slightly

quantized version. And

>> I heard about this and I learned about

this recently in the Tesla um earnings

call that they had the shareholder

meeting recently and we're going to have

an episode on this later this week. But

there's this interesting thing that Elon

mentioned during the episode where he

was talking about quantiz versus

floatingpoint uh AI and I was like what

the hell is that? like what why are you

spending so much time talking about

this? It doesn't make sense. And what I

realized is a lot of AI models they they

use like many many points after the

decimal in terms of data to get more

precise results and that is floating

point. When you quantize a model you

remove all of the data to the right of

the model and you just go to single

integers. So you lose the variance of

maybe up to like 60%. But you gain so

much faster efficiency, so much better

speed improvements, cost improvements,

and you can actually run it locally on

these things. So I think it's

interesting to see the different

decisions that people are making in

terms of well how precise does the model

have to be versus how cost effective and

how efficient does it need to be. And

what we're seeing with Kimmy Gay too is

it's very easy to to overindex on the

efficiency but maybe that's not the

stated goal of OpenAI where if they

really wanted to they could sign

quantize these models they could go more

to integer like type compute. Um, and it

was just something I was thinking about

is how they approach them because it

could just be well Kimy's just kind of

optimizing for speed and efficiency and

the downstream effect is it's also

really fast whereas OpenAI kind of

hasn't really optimized for that

specifically yet,

>> right? And the the counterargument to to

that point would be, well, Josh, it's

crushing all the benchmarks that we've

evaluated all the other American models

on, right? So surely it's much better.

And my my push back on that would be

like, well, benchmarks don't really um

materialize in real life use. So what if

it crushes uh 50% on humanity's last

exam? Is it useful for me to use? Does

it understand what I'm trying to say?

Does it understand the context of the

prompts that I'm putting into it?

>> Um the other side of this um you know on

the point of quantization, Josh, is um I

think that a lot of frontier American AI

labs like OpenAI, Google, etc. actually

have enough compute to give you the best

experience, the um the highest floating

point uh experience um to put it to put

into that context, but they're using the

majority of that compute to train the

next big model that we haven't even seen

yet, right? Um there were there was news

that broke last week that OpenAI is

doing this, right? So technically they

have enough compute to give you like

amazing service all year round, but

they're using 70% of that compute to

train GBT6. So I think it's just a

matter of prioritization right now until

we reach some kind of parity that these

AI models are are good enough. But I I

will say from all of the things that

we've discussed on this episode so far,

there is one clear winner and that is

the consumer. It's you, I, and everyone

listening to this show, which basically

gets access to frontier level

intelligence for the cost of next to

nothing. Download it completely free and

run it privately at home. Um on this

tweet that I have pulled up here uh it

basically says for every closed model

there is an open- source alternative and

it and it goes through a list like sonet

4.5 you've got glm 4.6

Groc code fast, you've got GPT OSS, um

GPT5, you got Kimmy K2 thinking, and it

just goes on and on and on. And if we

look at this kind of like a year and a

half ago, maybe even two years ago, this

list would be non-existent. It would

just be Frontier AI Labs on the closed

source side and zero open source side.

So to see this kind of progress is

really, really encouraging.

>> Mhm. Yeah. It's going to be a race. It's

going to be a battle between open and

closed source. And and perhaps that's

not even the battle. Perhaps it's open

source until they catch up to closed

source and then it's closed source

across the board. So, it's going to be

interesting to see the developments. Um,

we have a new batch of models that are

coming. We're kind of in this weird

limbo where Gemini 3 is hopefully coming

soon. We'll have some new benchmarks and

and one of the things that that was this

harsh truth to kind of wrap my head

around, which is what you just

mentioned, EJZ, and the fact that

everyone's just compute constrainted

like OpenAI could have made GPT5

probably twice as impressive if they

really wanted to. they just have no

compute to serve that and it would have

been way too expensive and way too slow.

So, it's not that it's they can't it

can't be done. It's just that people

don't have the resources to do it. So,

it's this constant balancing act and

it's going to be fun to see how how

companies kind of slot themselves into

that that curve of like how much they

want to spend on compute versus cost

versus just what they have available to

actually use to train these models and

deploy them at scale to users.

>> And that's it for today folks. Um, super

fun episode. Uh I it is always

surprising to me how quickly open source

catches up with closed source

centralized AI. I always think kind of

like it's going to lag a few years and

now it's come down to the fact that it's

lagging a few weeks. Um we have a

jam-packed week. Uh we have potentially

a new nano banana model being released

by Google tomorrow.

>> Fingers crossed. I'm praying for that.

>> Fingers crossed. I'm also praying for

that as well. And we have a second

episode based on Tesla's investor today

which had some really jam-packed

exciting news. Um, now listen, if you

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kind of popping too. So, yeah. Anyway,

>> thank you for all the support.

>> We do not pick and choose. Wherever you

listen, go for it.

>> There you go. All right, we will see you

guys in the next one. Thank you for

watching as always. Much appreciated.

Um peace.

[Music]

[Applause]