Ox Alpha Revealed: China's GLM-5.3-Flash Gave Away Free AI

We discuss the World Humanoid Olympics in China, where humanoid robots competed in human-style events and set new performance marks. We also cover autonomy, reward-based training, and the comparison between China and the U.S. in robotics and AI.

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TIMESTAMPS

0:00 Mystery Model Appears
1:30 Stealth Launch On OpenRouter
4:31 Chasing The Model’s Origin
6:09 Benchmarking Ox Alpha
6:59 Flash Model Theory
10:00 Blind Taste Test Buzz
12:11 Why Continual Learning Matters
14:58 Data, Subsidies, And Strategy
18:47 China’s Copycat Advantage
20:21 Mystery Solved For Now
21:11 More Models Incoming

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RESOURCES

Josh: https://x.com/JoshKale

Ejaaz: https://x.com/cryptopunk7213

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Not financial or tax advice. See our investment disclosures here:
https://www.bankless.com/disclosures⁠

Josh works with Anthropic as a contractor. All views expressed are his own and do not represent Anthropic, its leadership, or its affiliates. Nothing in this episode is investment advice.

Creators and Guests

Host

Ejaaz Ahamadeen

Host

Josh Kale

What is Limitless: An AI Podcast?

Exploring the frontiers of Technology and AI

Josh:
There's a new top dog in town, and the question on everyone's minds is, who is this?

Josh:
There's a secret model codenamed 0xAlpha that's been live since August 20th,

Josh:
the last six days, and it is offering the seemingly unbelievable things to the public.

Josh:
They're offering 100 trillion tokens of free usage to anyone who wants to go

Josh:
and get it. They're offering a million token context window,

Josh:
and the benchmarks of this thing are pretty unbelievable.

Josh:
There's also this weird thing going on in the background where seemingly every

Josh:
single day the outputs of the model are getting better there were tests on day

Josh:
one that were far inferior to the tests that were currently on day six as of

Josh:
this morning we think we have the answer to who this person is but before we talk about that

Josh:
we have to discuss what is 0x alpha you guys this has been the mystery of the

Josh:
week it seems like frontier level intelligence it's offering all of these unbelievable

Josh:
things like 100 trillion tokens for free

Josh:
do you know how much that would cost if you were to use like fable or gbt's 5.6

Ejaaz:
Salt like i think it's like 15 to to like 20 million bucks a day.

Josh:
It's a lot of money

Ejaaz:
A day okay so just with that figure alone who who on earth is giving away 20

Ejaaz:
to 30 million dollars on inference per day in this economy right it has to be

Ejaaz:
a big frontier lab the second clue is josh it's not 0x alpha

Ejaaz:
it's ox alpha which seems kind of weird right Like who's talking about the animal ox?

Ejaaz:
Until you realize that it's probably part of like the Chinese kind of folklore

Ejaaz:
and Zodiac side of things, which again, might be a little hint as to where we're

Ejaaz:
going. But anyway, let's rewind six days ago very quickly.

Ejaaz:
Open Router, which we've covered on the show before, it's a platform that kind

Ejaaz:
of like hosts a bunch of different models, allows you to pick and choose,

Ejaaz:
revealed a stealth model. Now a stealth launch is where they launch the model.

Ejaaz:
You go to their website, you can access and use the model for free.

Ejaaz:
But they don't tell you the name of the model. Now, they've done this with previous

Ejaaz:
launches through Google Gemini's model. They've done it through Anthropic.

Ejaaz:
They've done it through OpenAI as well.

Ejaaz:
And they haven't had a stealth model launch in a while, but now this is the

Ejaaz:
first one and it was in high demand.

Ejaaz:
So as you mentioned earlier, 100 trillion tokens, which means that you can effectively

Ejaaz:
go there and do like all your sorts of coding works, very heavy AI usage tasks.

Ejaaz:
And you can kind of use that in a day.

Ejaaz:
Again, 20 to $30 million worth of inference costs.

Ejaaz:
It has a massive 1 million context window, which is very competitive with the

Ejaaz:
top models that we see from Fable 5, as well as GPT-5.6 Sol, and,

Ejaaz:
and this is the most important part, it's a uni model, which means that you

Ejaaz:
can use not just text, but images, models. It's really good at rendering all

Ejaaz:
these different types of medium.

Ejaaz:
Now, the final point, which I think is the most exciting one that you referred

Ejaaz:
to earlier, Josh, is this concept of continual learning.

Ejaaz:
So people, as they started to use this model, realized a very curious phenomenon.

Ejaaz:
They realized that as they were using this model, let's say on day one,

Ejaaz:
day two and day three, the model got exponentially better at doing the very

Ejaaz:
same tasks that they fed it in day one.

Ejaaz:
And so people started to be suspicious that this is a model that doesn't just

Ejaaz:
kind of take your input and spit back out an answer, which is what a lot of

Ejaaz:
models do right now, but it can continually self-learn and improve 24-7 to give you a better response.

Ejaaz:
So the question on everyone's mind is, who created this model at all?

Ejaaz:
And when you look at the kind of usage or when you look at the kind of uptake on OpenRouter itself.

Ejaaz:
Although no one knew, it became the most viral launch on OpenRouter.

Ejaaz:
It became the new number one model and dethroned DeepSeek's Flash,

Ejaaz:
which launched about a couple weeks ago. And DeepSeek has been kind of like

Ejaaz:
the name brand on OpenRouter for the longest time ever. So just a really exciting model to see.

Josh:
Yeah, it was not only the most popular model on OpenRouter immediately upon

Josh:
launch, but by a factor of two.

Josh:
And it more than doubled DeepSeek's use. Like, This is hugely popular and very

Josh:
interesting because of how well it performs.

Josh:
The benchmarks from it, the Deep SWE subset benchmark, it had an 80% pass rate,

Josh:
which for those who aren't familiar, Fable 5 was at 65% and GLM 5.3 was at 62%.

Ejaaz:
Hold on, hold on. So you're saying that it's crushing the METOS level models

Ejaaz:
and the GPT-5.6 models at coding?

Josh:
In one particular subset of coding benchmarks.

Josh:
I don't want to i definitely don't want to say it's better than the frontier

Josh:
models because upon further investigation we will come to find out it's not

Josh:
and it's pretty far from it but it's an incredible model for what it is and

Josh:
the reason i was comparing it to glm 5.3 is because

Josh:
There's this thing when you're producing models called the tokenizers,

Josh:
how tokens get generated. And it was just so happened to be identical to GLM.

Josh:
So people were like, huh, this is interesting. Maybe we can go further down

Josh:
the rabbit hole and see if there's more here.

Josh:
So you can test oftentimes whether a model is Chinese or not based on asking

Josh:
it a series of questions that are not really allowed to be answered in China.

Josh:
One of which is asking whether Taiwan is part of China.

Josh:
And what's funny is if you're using like an American frontier lab,

Josh:
they'll very clearly give you the answer to this question but chinese models do not and

Josh:
what we realized is that the output of the answer to this question was pretty

Josh:
much identical to the glm model and so like okay it's got a similar tokenizer

Josh:
it's giving similar outputs but we still don't really know

Josh:
Up until this morning, in which we discovered that GLM is actually the new version of GPOO.

Josh:
It is, or sorry, that Ox Alpha is the new version of GPOO.

Josh:
And it seems like this is going to be a new GLM model that we have.

Josh:
And that, when I heard it, it actually landed me even more suspicious because

Josh:
over the course of this last week on X and all of his social media,

Josh:
everyone has been kind of vague posting about who this is, why they're doing

Josh:
it, why they're offering so many tokens and so much compute.

Josh:
And then the Google team started chiming in, which I thought was really bizarre.

Josh:
A lot of developers on the Google DeepMind team who work on Gemini,

Josh:
they started vague posting about this model. And as of this morning,

Josh:
Bloomberg released a post, and it seems like it is everything but confirmed.

Josh:
The model actually is from Z.ai and OxAlpha is now known.

Josh:
And I think that was like a really interesting way of coming to this conclusion.

Josh:
And now that we know, I just have more questions. How on earth are they serving

Josh:
all this inference to all these people for free. Like the cost must be tremendous.

Ejaaz:
I mean, let's see how this model shapes up against all the other models.

Ejaaz:
So we have a chart over here, which shows kind of like the average tokens output

Ejaaz:
per task on the DeepSuite benchmark that you mentioned.

Ejaaz:
DeepSuite, for those of you who don't know, is a good test of a model's capability

Ejaaz:
to actually code really well. And code is kind of like the premium benchmark

Ejaaz:
that a lot of companies and AI labs kind of like benchmark their model assets against.

Ejaaz:
And so OxAlpha is actually quite high up there. It's up there in terms of reduced

Ejaaz:
token output, but still maintaining a very high intelligence score.

Ejaaz:
Now, as you mentioned earlier on, that's not actually quite what was revealed

Ejaaz:
when people started playing around with this model.

Ejaaz:
It ended up achieving not really 80% that you mentioned earlier, but around 63%.

Ejaaz:
And that's under like real usage. So there's a suggestion there that the Chinese

Ejaaz:
lab GPU kind of like maybe bench backs this.

Ejaaz:
To just talk about the model and why it's so impressive, just in its own merit,

Ejaaz:
there's a few things to look at.

Ejaaz:
Number one, the rumor has it that this model isn't a foundation model.

Ejaaz:
This is a flash model. So what that means is it's a smaller derivative of a

Ejaaz:
much larger model that Jeepoo is training or has already trained.

Ejaaz:
So what does this mean? Let's take Mythos or let's take JeepT5.6.

Ejaaz:
These are rumored to be around 6 to 10 trillion parameter models.

Ejaaz:
Now, when you look at Jibu's flash model, if that's true, you're only looking

Ejaaz:
at around like two to three trillion parameters in size, which,

Ejaaz:
you know, is a decent size, but it's still very small compared to these bigger models.

Ejaaz:
And then the fact that this model is not only likely going to be cheaper in

Ejaaz:
its usage, let's say maybe 30 to 50 cents per usage.

Ejaaz:
So like they gave away like 15 to $30 million per day of 100 trillion tokens,

Ejaaz:
it is able to work much faster, and they're going to open weight the entire model.

Ejaaz:
So, I mean, to answer your question, I don't really have an answer,

Ejaaz:
Josh, as to how they've been able to kind of create this, aside from the usual,

Ejaaz:
maybe they distilled a Western model, or maybe they had a breakthrough in continual

Ejaaz:
learning that we suggested earlier.

Ejaaz:
But the point is, this is a very serious competitor. And we saw.

Ejaaz:
A kind of exchange between Elon Musk and the founder of Jipu,

Ejaaz:
where about a month or two ago,

Ejaaz:
Elon Musk was responding into a thread as to when he believed that Chinese or

Ejaaz:
open source models in general will become mythos level, you know,

Ejaaz:
have that cyber security risk.

Ejaaz:
And he said, probably Q1 of next year, of 2027.

Ejaaz:
And the founder, Ji Tang, of Jipu replied says, it won't take that long.

Ejaaz:
And he goes on to say, it doesn't show it in this thread right here,

Ejaaz:
that it'll probably take two to three months.

Ejaaz:
And he was true to this word. We now have an AUX alpha mythos level model that

Ejaaz:
is from Jipu that is flash, that is smaller, that is cheaper,

Ejaaz:
that anyone and everyone can use, and it's gonna be open-weighted a few weeks from now.

Josh:
And that's probably how it was served. It's just a low cost model to serve.

Josh:
And it's really funny watching the public reaction to this and kind of,

Josh:
I don't want to say overreacting, but getting very excited and enthusiastic

Josh:
about this because a lot of the posts about this were like, oh,

Josh:
it does have continual learning and it is getting better every day.

Josh:
And suspicions as to like, why are there more tokens being available every single

Josh:
day? And we have this post from Jeffrey Emanuel, who's actually,

Josh:
I guess, on the Limitless Show.

Josh:
And he just said, just thinking through this free 0x alpha model or aux alpha

Josh:
model how could it possibly make sense to give out this many tokens for free

Josh:
maybe there's been a continuous learning breakthrough and the best way to pour

Josh:
more gas in the fire is to source as many coding related requests as possible

Josh:
i don't know if that's right i think the kind of where i'm landing here is that

Josh:
this is a flash model this is some sort of distilled model that is meant to

Josh:
serve inference very cheaply and what we're seeing instead of continual learning it's just

Josh:
New checkpoints that they're kind of releasing every day. And we're able to

Josh:
track the checkpoints as they come into play. And they're just kind of generally

Josh:
iterating over time. I'm not sure it's self-improving.

Josh:
We are going to find out more on that soon.

Josh:
The interesting phenomenon that I have really enjoyed seeing over the last week

Josh:
is the blind taste test that's been going on.

Josh:
This is like known kind of like as the Pepsi challenge, where you put like Pepsi

Josh:
and Coke against each other in a blind taste test and people don't know what they're getting.

Josh:
And one of the things I found really interesting is how excited people got about

Josh:
this model because of the mystery and the lore.

Josh:
And it really makes you question the value of true frontier level intelligence

Josh:
when people were doing seemingly incredible things with this open source free flash model.

Josh:
And when you're comparing the two next to each other, for some tasks,

Josh:
it's very clear that the frontier models are stronger.

Josh:
But for many other tasks, that's not apparently obvious.

Josh:
And if you are just sending a prompt into a text box, and it is this Ox Alpha

Josh:
model, or it is like a mid-tier ChatGPT or Claude model, the outputs are mostly the same.

Josh:
And people didn't realize and people don't really care where the tokens come

Josh:
from. So I find this to be an

Josh:
A true stealth model that is good oftentimes it's connected to a brand and there's

Josh:
very strong brand affinity so when you remove the brand affinity from the equation

Josh:
what happens you kind of get this phenomenon here where it turns out people just want

Josh:
pretty smart tokens and they don't actually care where it comes from it's interesting

Josh:
to see the the continual improvement or whatever's happening here where there's

Josh:
an example of a rocket ship taking off and it very clearly looks like day over day

Josh:
its abilities to render 3d graphics and understand physics and create this like

Josh:
real-world emulation are significantly better.

Josh:
So I'm really excited to learn more as to what's going on behind the scenes.

Josh:
Like, are these pre-prepared checkpoints, are they actually taking all the data

Josh:
they're collecting from these 100 trillion tokens and using it to continuous,

Josh:
to apply like RL on top of the model? Like, I don't know. These are things I'm

Josh:
very excited to find out over the next couple of days, though.

Ejaaz:
Yeah, I think in the future, no one's really going to care which model they use.

Ejaaz:
They just want to make sure that the output is what they expected.

Ejaaz:
They want to have the most effective output for the cheapest possible cost that

Ejaaz:
they can pay for that output.

Ejaaz:
And we're seeing like a lot of companies move to some of these Chinese open

Ejaaz:
source models or just other models, cheaper models from other smaller American

Ejaaz:
labs, just so that they are able to save on costs and create like a more fine-tuned

Ejaaz:
model for this specific bit of work.

Ejaaz:
Now, I want to spend a bit of time to talk about if this model has achieved

Ejaaz:
continual learning, why that's important and why that puts

Ejaaz:
China, if this is Jipu's actual model, which they confirmed in a very advantageous

Ejaaz:
position because it kind of shifts the Game of Thrones around a bit.

Ejaaz:
Well, right now, you look at Anthropic, you look at OpenAI, they have the world's leading models.

Ejaaz:
They have internal models that they haven't even released yet that are supposedly

Ejaaz:
even more intelligent than the ones that they have publicly available.

Ejaaz:
And they have all the compute in the world and they're spending so much money

Ejaaz:
trying to train the best model.

Ejaaz:
So if you were to reason within yourself, maybe no one else can have the capability

Ejaaz:
to catch up in China, although they've been able to put out very good open source

Ejaaz:
models hasn't been able to surpass the frontier.

Ejaaz:
Now, one way that you can kind of cheat that dynamic is if you have a continual learning model.

Ejaaz:
Now, the best way to think about what this means is think of a really intelligent

Ejaaz:
15 year old or 18 year old when you teach it how to ride a bike on day one.

Ejaaz:
And then let's say you teach it how to make pancakes day 50s,

Ejaaz:
50 days later, it'll still remember or know how to learn how to ride a bike or how to ride a bike.

Ejaaz:
Current models right now doesn't work like that. They lose sight of their memory.

Ejaaz:
It's something called catastrophic memory loss or something like that.

Ejaaz:
And they're unable to kind of remember some of the smart intelligence stuff

Ejaaz:
that they learned earlier on.

Ejaaz:
Continual learning solves for that. Now, you can imagine if a model is actually

Ejaaz:
able to learn like a real human is, like a really smart kid,

Ejaaz:
you could reason to say that that model will be more valuable to you.

Ejaaz:
Especially if you could run it locally at home on your local device on all your types of private data.

Ejaaz:
So I can see two types of individuals looking at this kind of a model and being

Ejaaz:
really incentivized to use it over a Claude or over an OpenAI chat GPT type model.

Ejaaz:
And that one is enterprises who don't want to give all their data away to major

Ejaaz:
American labs. They would want to use an open weights model that can continue to learn.

Ejaaz:
And the second is any kind of respective retail user who doesn't want to give

Ejaaz:
away any of their personal records to Anthropic and open AI.

Ejaaz:
Let's say it's medical records or finance information.

Ejaaz:
Let's say they want to have an AI agent that handles their finances.

Ejaaz:
You know, you may have different reasoning behind whether you want to use a

Ejaaz:
certain brand or a certain lab.

Ejaaz:
But if you could run it privately at home, maybe you might want to use something

Ejaaz:
like this. But continual learning, just assume you have this model that learns

Ejaaz:
24-7, even when you're asleep, it could probably catch up with the frontier

Ejaaz:
models. And I think it's really interesting to see.

Ejaaz:
I don't know if this is that. I'm not convinced that it is.

Ejaaz:
But if China has made a breakthrough, that's going to put them in a very advantageous position.

Josh:
Yeah i also don't think it's that i find it hard to believe that this is what

Josh:
it looks like and that they've reached that point so far i mean that'd be an

Josh:
amazing breakthrough in the case they do

Josh:
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Josh:
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Josh:
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Josh:
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Josh:
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Josh:
It offers a series of tools that allow you to work with agents in a three-step

Josh:
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Josh:
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Josh:
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Josh:
in which you engage with llms and is open source and available today so you

Josh:
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Josh:
in our show thank you ledger for sponsoring this episode and then i guess we

Josh:
kind of have to talk about that like is it really a free case because like is

Josh:
free ever really free and i guess there's two possible answers to this question

Josh:
one is yes it's just being heavily subsidized

Josh:
perhaps by the chinese government as a vampire attack to pull some demand from

Josh:
the frontier labs into these chinese models the other is perhaps they're just using the data

Josh:
one of the big problems that we have been seeing people complain about is data

Josh:
retention and zero data retention policies because people don't want to feel like they are

Josh:
Having their data harvested and used to train other models. And when you think

Josh:
about giving away these free models, China for a fact is collecting all of the

Josh:
inputs from the context window. And what's been happening is a lot of people

Josh:
are using this on perhaps proprietary code bases.

Josh:
People have been testing this model in their own private projects.

Josh:
They've been using it for their own personal work.

Josh:
And a lot of data gets derived out of that. I mean, it's a million token context

Josh:
window in this one chat box and you just paste it all in and you let it go to town.

Josh:
So I'm sure the amount of data that's collected from offering all of these tokens

Josh:
just through collecting all the inputs has been tremendous.

Josh:
And I'm sure that gives them a unique advantage in a way that some frontier

Josh:
labs might not, because oftentimes they are criticized for their retention policies.

Josh:
And people really want to make sure that there is that zero data retention.

Josh:
China doesn't face the scrutiny, in particular, doing it in stealth kind of

Josh:
shields that for at least the first week while people have used it.

Josh:
So they were just kind of freely and liberally using this thing,

Josh:
probably aware that they were having their prompts used for future training,

Josh:
but not really caring too much.

Josh:
So I thought that was also interesting. The other thing is that like the status

Josh:
of Jeep as a company still isn't very strong.

Josh:
They had, I think, like $100 million in revenue last year, and they had a net

Josh:
loss of like seven times that or something like that. It's currently priced at 750 times revenue.

Josh:
The company itself clearly can't sustain this type of...

Josh:
Funding and this free giveaway forever. And I'm curious to know what the type

Josh:
of business model eventually, if any, there is going to be in order to keep this thing alive.

Josh:
Like, is it just going to be a government subsidy until they figure it out?

Ejaaz:
The business model is its state back, dude. Yeah.

Ejaaz:
All these Chinese labs are backed by the state. So that's how they get their

Ejaaz:
infinite amounts of capital to be able to kind of fund all these things.

Ejaaz:
And this is a strategy that China has been pursuing for three plus years now.

Ejaaz:
They want to catch up with the frontier Western models. they distill attack

Ejaaz:
a bunch of these model labs so that they can train similarly capable models.

Ejaaz:
And then they flood the market with cheap intelligence, which means that Anthropic

Ejaaz:
can't charge the highest price that they can for the high quality model that

Ejaaz:
they have. OpenAI can't do the same.

Ejaaz:
So this drives model costs down. This is what kind of Meta and Mark Zuckerberg

Ejaaz:
is also trying to do, but failing to do as effectively as the Chinese labs.

Ejaaz:
They're just very good at doing it. The second thing I'll say is,

Ejaaz:
at least according to Open Rata from their official statement.

Ejaaz:
They said that this stealth model provider, which we now know to be Jipu,

Ejaaz:
is retaining prompts and responses, but explicitly state that they're not using

Ejaaz:
it for training. Now, this isn't verifiable in any kind of way.

Ejaaz:
Like, okay, China, you've never done anything nefarious before, I'm sure.

Josh:
You're really going to give this away totally for free? Like,

Josh:
no way. Coming from the people that stole all the items, I can go and generate

Josh:
a full episode of Spongebob on those models.

Ejaaz:
Exactly. Exactly. Well, this is another unique thing, right?

Ejaaz:
Culturally, in China, and this applies for every vector of tech that they've

Ejaaz:
built in the past, electric cars, mobile phones, et cetera.

Ejaaz:
They don't really see copyright as a thing or IP infringement as a thing.

Ejaaz:
They see, okay, if you have a good product, I can copy that.

Ejaaz:
And Chinese in particular are very, very good at copying effectively.

Ejaaz:
There was this great interview I watched with, who was it? It was Travis Kalanick,

Ejaaz:
on David Semra's podcast. I don't know if you caught that, right?

Ejaaz:
Absolute legend, right? Twice. And he talks about his story where,

Ejaaz:
you know, how he brought Uber over to China and he made a success there,

Ejaaz:
but he said it was very tough. But there was also some really unique cultural

Ejaaz:
things that he needed to understand.

Ejaaz:
And he realized that in America, or in the West at least, the innovation level

Ejaaz:
is super high. Coming up with net new zero-to-one ideas, super high.

Ejaaz:
In China, less so, but they are masters of copying. And he originally looked

Ejaaz:
down at copying as a really menial thing.

Ejaaz:
But he said that the way that they do it is masterful and artful in itself.

Ejaaz:
And this, I feel like, is what some of these Chinese labs are really good at doing.

Ejaaz:
It doesn't matter if they're earning $100 million. Their IPOs are popping off

Ejaaz:
anyway. They're never going to compare to the Western IPO market at all.

Ejaaz:
But the point is, they want to flood the market and kind of like keep America

Ejaaz:
at a cost level where maybe they're spending too much money than they actually

Ejaaz:
need to until they're able to figure out some kind of a breakthrough,

Ejaaz:
which might be continual money. Who knows?

Josh:
Maybe. We'll see. It's an exciting story and we're going to find out more.

Josh:
So this will be covered and followed up in the roundup tomorrow.

Josh:
Stay tuned for that. There's also a reference. It's kind of cool.

Josh:
The main character of the story is Open Router.

Josh:
And we just covered an entire Open Router episode just last week.

Josh:
So everyone who is unfamiliar with Open Router and why it is so valuable,

Josh:
please go and check out that episode.

Josh:
And that's mostly the state of the mystery as of now. Ox Alpha has been solved,

Josh:
but there is still a lot of behind the scenes that is left to uncover.

Josh:
We're hoping to get that information shortly. when we do we will share it with

Josh:
you right here on the roundup hopefully this week so stay tuned for that one

Josh:
um this has been kind of a crazy week i mean it's

Josh:
China's always throwing like, they're throwing wrenches in everyone's plans.

Josh:
First it was Deep Seek. Now they're doing this like kind of really fun stuff.

Ejaaz:
Did you see Quen as well?

Josh:
Yeah, we got a new Quen model. We're going to talk about that tomorrow.

Ejaaz:
Dude, they're just like spaying out these models. I know.

Ejaaz:
And you know, the craziest part about all of this is I'm seeing rumors from

Ejaaz:
trusted sources that a bunch of American frontier labs are also about to like drop a few models.

Ejaaz:
So like, I'm just saying here, like, you know, we're getting like the best from

Ejaaz:
the Chinese, We're getting the best from the West and there's just too many models to use right now.

Ejaaz:
But if you're listening to this and you're curious about this Ox Alpha model

Ejaaz:
or if you don't believe anything that we're saying, if you don't think it's

Ejaaz:
good enough, go out and try it right now. You can use.

Josh:
It for free

Ejaaz:
On OpenRouter. Go check it out. There is no promo code. Just sign up,

Ejaaz:
make an account and you can kind of like inference it and use it yourself.

Josh:
And I'm curious, send us,

Ejaaz:
Exactly. Like use the free tokens, please. Use the Chinese state funded money

Ejaaz:
budget to kind of do your AI tasks.

Josh:
Just don't give it your proprietary information.

Ejaaz:
Do not, definitely don't do that because they're definitely using it to train

Ejaaz:
the next model. But like, let us know in the comments whether you actually like

Ejaaz:
the model, whether it actually is good at the toss that you give it.

Ejaaz:
And if it magically improves the next day because it's continual learning. We don't know.

Ejaaz:
Like, let us know in the comments, DM us, we're available on all socials.

Ejaaz:
Yeah, anything else, Josh?

Josh:
Yeah, no, just to, again, share this with your friends if they enjoyed it.

Josh:
If you have something to say, please leave a comment. We try to read all of them.

Josh:
If you enjoyed on your favorite podcast player you can leave us a five-star

Josh:
review there if you're listening to this

Josh:
you should be watching it's very fun visually you can watch this on youtube

Josh:
on spotify and yeah as always thank you so much for watching feel free to go

Josh:
get caught up on the other episodes and tomorrow we got a banger roundup like

Josh:
it's just gonna there's so much stuff to talk about it

Ejaaz:
Is our favorite episode of the week.

Josh:
We've been in like a lull lately where there had i mean it's been exciting but

Josh:
it hasn't been like oh my god this is crazy there's new frontiers forming and

Josh:
like hopefully this is the first of many instances in which we're going to rev

Josh:
back up that engine and get

Josh:
a whole bunch of new models so i'm very excited stay tuned for all of that where

Josh:
we'll be covering it and yeah we'll see you guys in the next episode