It's no secret that Cursor has been
trying to cook their own model for a bit
now, and it seems like they succeeded.
Composer 2 is incredible. It beats out
Opus in a lot of different things.
That's why I'm so excited to show you
guys. That's not the new cursor model.
That's Kimmy K2.5, the openweight model
from Moonshot AI. Why is that here? I
thought we were supposed to be talking
about Composer 2, the new model cursor
just shipped that is performing
incredibly well and for insanely cheap.
Well, that's what we were supposed to
talk about until this tweet occurred
where somebody happened to notice when
they were playing with the URLs for
hitting things through the cursor API
that the new model had an interesting
name in it. Counts any sphere models
Kimmy K2.5 RL 0317 S515 fast. Huh. Turns
out Cursor's new model might not be all
that new. There's a lot to dig into
here. From the actual performance costs
and experience you can expect from
composer to the weird building on top of
Kimmy that was not really disclosed and
might be against their license to the
deleted tweets from members of the Kimmy
moonshot team posting about this
confused questioning things and a hell
of a lot more. This one's been a
fascinating deep dive and I've done a
bunch of research ahead of time to try
and cover it to the best of my ability.
As you all know, I am an investor in
Cursor but they are not paying me in any
way, shape or form to cover any of this.
As you can imagine, they probably don't
want me covering this drama too much.
Never been the biggest fan of drama, but
this one's important enough and has
enough good learnings that I think it's
worth covering and discussing in detail.
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OoS. So, let's start from the top.
Composer 2. This is a new model that
exists incursor and only incursor, which
is a a thing I'm not the biggest fan of
for a bunch of different reasons. I do
kind of understand why they don't want
to expose this over API. It's a model
that's only good for code. So even the
benchmarks are going to be weird because
a lot of the benchmarks that exist,
things like artificial analysis, which I
love, have a lot of things in the bench
that aren't just testing coding
capabilities. And this version of the
model will probably be very bad at that
because Composer is meant to be a
fine-tuned model that does nothing but
code, which I actually think is cool.
It's nice to see this type of research
going into a bespoke model that does
just one thing that matters. But the
path that they use to get there is where
things get really interesting in my
opinion. I've been keeping an eye on the
composer models for a while and for a
reason that's probably a surprise to you
guys. Y'all remember Super Maven? Super
Maven was the first autocomplete AI
thing that I used in my editor that
truly blew me away. Copilot had moments
that were really cool, but Super Maven
had this weird way of like learning as
you went where it would adapt to how you
were coding and it was fast as hell. It
blew me away. It ended up being my first
personal investment I ever made where I
wired my own money instead of a scout
check because I was so confident in
Super Maven and specifically in Jacob.
the founder of Super Maven because he
came from Tab 9. Tab 9 was the first
attempt at an AI coding thing and this
started all the way back in what yeah
2018 by Jacob Jackson. I was blown away
to see him leave to go do his own new
thing and he more than succeeded. Super
Maven was incredible. There's still a
bunch of people who dearly miss it. So
why are we talking about Super Maven?
Well, spoiler if you weren't here at the
time. Super Maven got acquired by
Curser. I have a whole video about that
as well as an interview I did with both
the founders of Cursor and Super Maven
in the Cursor office back when it was a
tiny building with like six or maybe 10
people max in it. Great time. Wild how
much things have changed. They have like
five buildings now. But Super Maven was
a great project and it really showed
what can happen if you try to do
everything from the user experience to
the model itself around specific focused
coding tasks. And it's clear, if you
couldn't tell by now, that Jacob, the
founder of Super Maven, is obsessed with
making AI really, really good at code
and nothing else. When he joined Cursor,
him and his team went allin on
autocomplete, which is why to this day,
Cursor's autocomplete is just so much
better than anything else. It's the
thing that keeps me going back. I've
been trying to move to VS Code, cuz to
be frank, it's a much lighter thing.
It's less likely to cause me problems
when I use it. I've been very happy with
VS Code just not crashing and being
fast, but as soon as I have to actually
type in it, I find myself sitting there
waiting for the autocomplete. And when
eventually Copilot does kick in, it
gives a recommendation and I'm just
so used to using cursors because of the
the Super Maven magic that Jacob
injected into it. But Jacob got tired of
just working on autocomplete. Obviously,
his tag complete was great and he had to
build custom models and all of these
things for it internally, but he wanted
to go further. That's why he made
composer. Composer one was Cursor's
first attempt at making a new model for
full code generation from scratch. And I
could see why Jacob would want to do
this. When you look at his history,
building models for all sorts of code
tasks, as well as the amount of data
that cursor has on chat histories and
people building in these projects,
you'll realize that he's kind of set up
for success here. He has all the things
he needs to do something awesome. But it
also takes a ton of money and research
and people and GPUs to do something
crazy. He still managed to with Composer
1, though. Like that model blew me away.
was so much faster than everything else
and just it was good. It didn't wasn't
great. It didn't have the like deep
intelligence and understanding of my
code bases that other models did at the
time, but god damn was it capable of
doing incredible stuff. And that's why
they kept on iterating. That's why he
got to hire more. That's why they put
more money into GPUs, more effort into
curating all their data, and just did
everything they had to to set themselves
up to make better models. But now
there's the question of why making
models is super expensive and the
chances that you can make a model as
good as Frontier Labs at anything is
very low at best. Why would they put all
of this time into building something of
their own like this? Well, they have
some competition. I want to ask y'all a
question, chat. And do not answer this
question if you already know the answer.
To everybody who doesn't, I want you to
guess. How much inference do you think
you can do per month on the $200 a month
Claude Code plan? Like if you were
paying full API rates and you max out
your $200 a month plan on Cloud Code,
how much money of API usage do you think
you can do? Just drop numbers in chat.
You're putting in 200 bucks. What do you
think you can get out? Like 200, 500,
2,000? Okay, we see numbers like 2,000,
1,600, 3,000, 2K, 1K. Remember, you're
paying 200 bucks. So getting anything
over 200 is a deal. you could
theoretically get way more. So, here's
where things get interesting. When Cloud
Code first introduced the $200 a month
sub, the answer was around $1,500 to
$2,000, but they have continued ramping
it up since. According to Kurser's
internal analysis, their last year
estimate was that $200 per month cloud
code sub gave you $2,000 in compute, but
now the subsidiation appeared more
aggressive with $200 plans able to
consume about $5,000 in compute. Do you
see the problem here? That is insane.
This level of subsidization is absurd.
And I want to be clear, that's not going
to last. Anthropic is not going to be
able to keep giving you this insane
amount of inference for that type of
price. They just they can't. That's not
realistic. They just want to price
everyone out. That's why they're doing
this. That's why they're subsidizing so
hard. And for what it's worth, that's
why we don't sell our own subscriptions
in T3 Code. It is way too expensive to
hit Cloud models over API. And that's
why we let you bring your own sub. So,
if you're using Codeex with the OpenAI
subscription or Cloud with an anthropic
subscription, you can use T3 Code with
either of those. So, if you're on the
$200 a month plan for Codex and the $200
month plan for Claude, that's like
$10,000 of inference in a really good
interface. Just saying. We don't charge
money. It's free, open source. You can
change it and edit and do whatever you
want with it. But if you have these subs
and you want a UI that doesn't suck,
consider T3 Code. Just just saying. But
Cursor can't do that. And there's a
bunch of reasons why. The biggest one by
far is that they have to maintain a good
working relationship with both of the
labs because they rely on them so
heavily in their application and a lot
of cursor's revenue comes from
subscriptions that they're selling to
giant enterprises that are paying for
this inference through that cursor
subscription. So they need ways to
reduce costs because if Enthropic can
charge Cursor $200 for $200 of inference
and then they have to try and find a way
to justify that to their users, but they
go and resell the same inference for 25x
discount to people on their
subscription. You're kind of just
screwed. And I get it. Like I don't know
how we're going to make money on T3
code. Like we're not charging subs. It's
kind of crazy that we spent all of this
time and money and energy, effort and
space to build something that is making
it so OpenAI and Anthropic sell more
subscriptions and we don't get paid a
scent. This is not the most realistic
path as a business. We even make you
install the official cloud and Codex CLI
if you want to use those. Like we don't
do anything over API or with our own
integration or anything we can charge
for and I don't even know how we would
at this point. We have things we want to
build in the future some of which might
have to cost money but like we are doing
this because we want it. cursor needs to
make billions of dollars to justify
their billions of dollar valuation. They
need to be able to make a profit off of
the inference and anthropic is making it
very hard for them to do that. So they
need a wedge. We need to think about
leverage for a second here because
Anthropic has a lot of leverage. They
have the model everybody wants to use.
They have the APIs everybody has to use
to use them and as such everybody has to
pay them to get the thing they want
which is inference. So what does cursor
have? Cursor has a large install base
which helps a ton. It means they can
charge these people money and make some
money. But cursor has something else
that's really valuable. Chat histories.
When you do an API request through
something like cursor, when you make a
prompt in a thing like cursor that is to
go through their servers and that chat
history is a thing that exists. If you
don't click the switchite of cursor that
says don't train on my data, that data
all goes to cursor. So I would
personally estimate that maybe half or
so of all the anthropic prompts that go
through cursor are going through it in a
way where they're earmarked as usable
for training. That means they have a
shitload of history, a shitload of data
of existing models that are really good
working in these realworld code bases
that a lot of people just do not have.
They have the craziest wide variety of
data and chat histories they can use to
do reinforcement learning and training
on the models. You guys might remember
this article from earlier this year from
Anthropic where they talked about
detecting and preventing distillation
attacks where they claimed that labs
like Deepseek stole 150,000 exchanges as
in back and forths with the model in
order to train and refine their own
model. I can't properly put into words
how absurd this is, particularly this
number. 150,000 exchanges is nothing.
Since I started recording this video,
cursor's probably done 150,000 exchanges
150,000 times. This is not much data at
all. T3 Chat does more than this with
anthropic models daily. This is not a
number that matters. I do want to be
very very clear cuz I see people already
confused. Cursor is not using your data
if you have privacy mode on. I know this
for a fact because I had issues with
cursor when I had privacy mode on and
they literally could not debug with me
because they had no way to access
anything. I ended up turning off privacy
mode just so I could work with the team
more. So, our friends at Anthropic seem
to think 150,000 exchanges with an
anthropic model is enough for DeepS to
steal their reasoning capabilities
across diverse tasks. How much do you
think you can do with hundreds of
thousands of times more data? Because
obviously cursor has insane amounts as
well as all of their own internal work.
I would bet that employees at Cursor
using Cursor with Opus alone is way over
this number. They have a lot of data.
And while reinforcement learning is far
from a perfect technique, it has proven
to work really well in these types of
scenarios. When you have a ton of good
or good enough data that shows what a
successful run looks like, especially if
you can verify the results at the end in
some way, you can now train models with
that after they're already trained. The
pre-training kind of gives the models
their intelligence and knowledge. And
this type of reinforcement learning and
post-training gives them better
behaviors and just overall more likely
to comply with the systems that they
work in. And that's where we get to
today's drama, which is the involvement
with Kimmy. As I mentioned in the intro,
this started because Finn found in the
APIs that the model was named Kimmy K2.5
reinforcement learning 0317 S515 fast.
So what happened here? Well, the first
thing that happened was an employee at
Kimmy and Moonshot being very confused
because they had not heard anything
about this. This is an important detail
because it means that the knowledge that
this model was being used by cursor was
not very prolific within the Moonshot
team. This is particularly important
because of the modified MIT license
they're using. 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
users or more than 2 million US or
equivalent in monthly revenue, you shall
prominently display Kimk 2.5 on the user
interface of such product or service.
So, as far as we understand and as far
as I would ever guess, this would be in
violation of this license. But nobody's
really tested this yet. And I said this
the last time I covered this type of
license in a video and almost every time
before it, too. This modification isn't
something that's been tested in the
courts yet. We don't know what this
actually works for. And we also don't
know how much you can obuscate. If you
had a middleman, for example, you made
your own new corporation that thing was
reselling Chem K2.5 and you sold that
back to your own business for the 100
million monthly users or the 20 million
US in revenue. That shell company you
put between the two could be the thing
that displays Kimmy K2.5 and then it
sells a service to another company that
doesn't enforce that, that isn't
affected because they're not agreeing to
this license. The company between the
two agree to the license. This company's
just buying a service. This is an
important distinction for reasons that
you will learn momentarily. As
speculation caught fire on Twitter, Lee
Rob, everyone's favorite, came in out of
nowhere and actually has some really
good things to say. Composer 2 started
from an open- source base. We will do
full pre-training in the future. As I
mentioned before, the pre-training is
how you get a lot of the knowledge into
the model. It's the process where you
take a shitload of data and do a bunch
of transformations on it to try and make
the set of parameters that represent the
knowledge that it is capable of. RL is
mostly a post-training process, but
what's really interesting here is the
compute split. Kimmy K2.5 is a great
model. As someone who's been a fan of
Moonshot and Kimmy for a while, this was
a very fun week for me because I've been
saying for a bit that the Kimmy models
are underrated and really good. To this
day, Kimmy K2 is the default model in T3
checks. I think it's one of the best
options for just talking to a model. Its
pros is great. Its knowledge is more
than good enough. It's fast. It's nice.
It's just a good model. I genuinely
enjoy using it a lot. And 2.5 was much
smarter and more capable. Slightly worse
at writing, but still really good
overall. Turns out Cursor agreed and
chose Kimmy Kate 2.5 as the base model
that they would do all of their RL on
using their insane amount of data, all
of their harnesses, all of the other
stuff they've built with their small
team of researchers to try and make
Kimmy the best coding model per price.
And that's why they chose to use Kimmy
K2.5 as the starting point for this
crazy pile of post- training in RL on an
existing model so they could try and
make it the best coding model for the
price ever. They wanted a really good
coding model that worked great in their
harness that they could serve for cheap
and really fast. And they went well past
what I would imagine even they thought
they'd be able to do with this strategy.
Taking this model that was pretty good
before and bumping it to be in the GPT5
tier of capability according to their
own benchmarks is nuts. They did run it
on other external code benches like
terminal bench 2 where it still
outscored Opus 45 and 46. Seems to be a
good code model and having used it a
bunch myself, it's a pretty dang good
model. It makes dumb mistakes here and
there. You have to steer it every once
in a while, but it's so cheap and so
fast that I find it really nice to use.
It's also pretty good at design. Shout
out to D, by the way, for making this
very useful website where I can compare
runs on a specific prompt with and
without the anthropic design skill
across various models. I think this will
be fun. Here's an example of a design
that was made with Composer 2.0 for this
example site. I think this looks quite
good. And we look through these. Yeah, I
hate these retro blue pink glowy styles,
but the other ones all look really
solid. These aren't bad. And if we
compare to Kimmy K2.5, there's
definitely overlap in some of these
designs, but overall the composer ones
are slightly better. Not a lot better,
but enough better that it's pretty cool.
I still think Opus 4.6 with the front
end design skill is the most reliable
for good designs overall. But it's
actually the gap's not that big here. I
think Compose are kind of cooked. Yeah,
good Yeah, I'm impressed. Composer
did very well here. It's a good model.
It's very solid. And the speed in price
is insane. Their default version does 80
to 100 TPS and it costs basically
nothing. Yeah, 50 cents per million in
and 250 per million out. For comparison,
Opus is $5 per mill in and $25 per mill
out. Yes, that means it's exactly 10x
cheaper than Opus 46. That is insane,
but that doesn't mean they can just get
away with this. So back to these post
turns out that post training is very
valuable because Kimmy K25 didn't get
close to any of these numbers and that's
because Kimmy K25 was only
representative of a fourth of the
compute they used for the training here.
K25 is the model we all have access to
to download and use however we want. Can
use it through open router whenever they
put 3x additional compute into the post
training based on their estimates. I
don't know if they actually know how
much compute went into the original
model or not, but with their
assumptions, they did 3x more on top in
order to make this model so much better
at code and also worse at other things.
That's why their evals are very
different cuz the model's effectively a
whole different model. I really want to
emphasize how big of a deal this is.
Most of the major model releases from
the major labs are not a new
pre-training base. The pre-training is
the first step. It's almost like a
template in a way and then they do a
bunch of refinement after to turn that
into the models we actually like to use.
Most models start as like an
autocomplete model with a bunch of
information in them, but they're not
good at talking or tool calling or all
these other things, and then
post-training brings those behaviors
out. Models like GBT 5 and 5.1 were
based on the same pre-training. 52
onwards also seem to all be the same
pre-training. Gemini 3 to 3.1 was a
post-training change. The pre-training
was the same. It is very common for labs
to take existing models and use
post-training to make them behave
entirely differently in ways that are
often massive wins. What we haven't seen
before though is a lab using post
training to put out something that gets
frontier level numbers by doing this
post- training on someone else's model.
But to go back to a thing I mentioned
earlier about this license and in
particular the ability to obuscate who's
responsible by having a middleman. Lero
says here we are following the license
through our inference partner terms.
Sometimes being asserted into the middle
of something like this is good. Just
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Sorry about that. As I was saying, the
model has an inference partner that
allowed cursor to not have to disclose
by saying Kimmy K2.5 in the UI. Lero
replied here since people really want me
to say this. Kimmy K2.5. Yes, that is
the base we started from and we are
following the license through inference
partner terms. In this case, fireworks.
If you're not familiar with Fireworks,
they're a compute platform for hosting
openweight models and using them to do
fast inference. They also provide info
for people who want to do their own post
training, which seems to be where a lot
of the inference that cursor was running
was run. And because people were still
speculating about this, eventually Kimmy
came out and said what effectively
Cursor was saying as well. Congrats to
the Cursor AI team on the launch of
Composer 2. We are proud to see Kimmy
K25 provide the foundation. Seeing our
model integrated effectively through
Cursor's continued pre-training and
highMP RL training is the open model
ecosystem we love to support. Note
Cursor accessed Kimkate 2.5 via
Fireworks AI's hosted RL and inference
platforms as part of an authorized
commercial product in partnership. This
is the important piece. Since Cursor is
going through Fireworks and Fireworks is
disclosing properly to Cursor that
they're using Kimk 2.5. this is how they
get around that license. I knew this
would be a thing. In fact, I speculated
as much in the past that this type of
thing would allow for people to hide
what model they're using, even if it's
not within the spirit of the license. I
think it's important for businesses to
honor the spirit of these licenses in
general. We're still so early in all of
this. Nobody knows what we can and can't
do like technically speaking, much less
legally speaking, across all of these
things. This is the first time I've seen
someone try and more importantly succeed
in skirting the license. How big of a
deal is this is up for debate.
Personally, I would have liked at the
very least for the cursor team to have
come out and said that their new model
is a post-trained refined different
version of Kim K 2.5 and try to take the
opportunity to educate us because people
seem to think that this is them just
stealing the model and rebranding it as
theirs and not changing much about it.
It's not that. This is them working very
hard to make the best possible thing in
their tools using an insane amount of
compute and research and time and then
not properly disclosing what the
starting point was. I don't like that. I
think this isn't within the spirit of
openw weight models. And obviously open
weight does not mean open source. You
don't have all of the things you need to
recreate the model from scratch yourself
just cuz it's open weight. But open
weight is open weight. It means the
starting point is out there for people
to do things with. And the whole point
of doing that is so we can build on top
of it and do cool things with what we
have. The weird part here is that
traditionally with open source, we
expect that to be independent groups,
side projects, hobbyists, and people
like that building on top. But all you
can really do with an openweight model
is run it on your own GPUs unless you
have hundreds of millions of dollars to
spend trying to adjust it to do
different things, which is what cursor
can do. So, it's one of the few times we
have actually had a company that could
use these models to do something bigger
and bolder than we had seen before. I
don't think there's been anything like
this in the past, but I do think there's
going to be a lot more things like this
going forward. As companies are
integrating these models and they're
trying to get out of the absurd pricing
subsidization wars that companies like
Enthropic and OpenAI are doing, they
need an edge. Openweight models are that
edge in a lot of ways because the prices
are just absurdly cheaper. As I had
mentioned before, Opus' pricing is
expensive. We're talking $5 per million
tokens in and $25 per million out. If
you look at Kimmy K2.5, it's closer to
45 cents per mill in and 220 per mill
out. So, cursors charging a little bit
more than that, but not much. Or you
look at models like GLM5, 72 cents per
mill in and 230 per mill out. Huge gap.
These are 10x or more cheaper. And
they're not 10x dumber. They're like 20%
dumber for 20x cheaper. That's insane.
So if you're trying to deal with the 25x
subsidization that is being done by
companies like Anthropic where you spend
200 and get 5 grand, well if you find a
model that's way cheaper, 10 to 20 times
cheaper, you can cover a lot of that
subsidization gap. But those models are
not as good at a lot of things, in
particular coding. So what if you were
willing to compromise on everything else
just to focus on the coding side and
make something that was really good at
that? And that's what we now have with
Composer, the pretty dang cool, powerful
model. I just wish they had shared a bit
more and took the opportunity to educate
us about what this type of post training
looks like instead of us having to start
a whole bunch of drama, flame my and
everyone else's DMs in the process. And
now I'm here filming on a Saturday
evening just to make sure we all
understand what happened. There is one
last thing here I think is worth
covering. The long-term impact. Dax
posted this before things had settled
down, but I think it's as relevant now
as ever. Whether this being the drama
around using Kimmy K2.5 is true or not,
it's going to cause every company
producing open- source models to
re-evaluate if they should continue to
do so. That is incredibly frustrating. I
agree. There's a very good chance that
Cursor ends up making more money on
Kimmy K2.5 than Moonshot does. Hell,
there's a good chance that they spent
more money doing this post training on
Kimmy K2.5 than Moonshot has had in the
entire history of their business. That
sucks. And if you're one of those small
labs that is fighting to make these
powerful models and the only way you can
get people interested is releasing them
open weight, you're now looking over
your shoulder like should we even be
doing this? And that's a scary thing
because we need these openweight models
in order to keep things progressing. All
the cost improvements we've seen across
the flagship models are because the
openweight models got cheaper and better
enough that these companies have to
rethink things and I really hope we
don't lose that as a result of how this
release went. So wrapping up, Composer
is a good model. Kimmy K25 is also a
good model. They are very, very
different models. A lot of money was
spent in order to train Composer 2. And
they definitely should have disclosed
the fact that Composer 2 was being built
on top of Kimmy K2.5. Everyone kind of
screwed up here. I think the license
terms are not clear enough and also are
not well defined enough for us to know
what they really mean in practice. I
think Fireworks didn't do a good enough
job disclosing and forcing the
disclosure to go down the pipeline. And
I really don't think Curser did a great
job with their disclosures here either.
I am disappointed in how they handled
all of this, but I am thankful that they
are acknowledging their failures and
trying to do better in the future. I
hope, fingers crossed, this is the last
time we see something like this from
Cursor, but I have a bad feeling that
not only will they do something like
this again, but many other companies
will too. We are now in an era where
having cheap enough compute to compete
with anthropic and open AAI really,
really matters. And the way companies
get there are going to get more
interesting and probably more
problematic before things settle down.
Let me know how y'all feel about this.