Today on the podcast, we're talking about Open AI, which has just dropped two open source models.

Now, this is actually really big news because this is the first time in five years that they've

actually dropped any open source models back to GPT2. And this is something that's gotten a ton of

criticism, basically all of Elon Musk's online AI beef, pretty much why he says he started

XAI. And just a lot of drama and heat

that has been thrown at Open AI is basically on the fact that they started as an open

source company and hadn't dropped anything. And they have now officially launched some

quote unquote open models. Now, I'm going to be talking about the difference between open source

and open and where these models sit. I'm also going to go through the benchmarks of

basically how these models perform because of criticism that a bunch of them have gotten is like they just dropped like these models that are, you know, just to say that they're open source, but they're not actually that good. And I'm actually not going to lie, impressed by some benchmarks, but interested. And there's a couple of interesting nuances I want to go over. At the same time, Microsoft has just announced that they're going to be bringing some of

their smallest open models to Windows users.

So there's a ton of really interesting things that are getting rolled out right now.

We'll be covering all of that on the podcast today.

Before we get into it, I wanted to mention if you want to try any of the AI models that we

talk about on the show, I'd love for you to go check out my own startup, which is called AIbox.com, where we essentially have the top 40 different AI models from Anthropic, Cohere, deepseek, Google, Open AI, meta, tons of others, audio models like 11 labs and a bunch of really interesting image models, all for 20 bucks a month, you get access to all of them. So my hope there is not just that it will save you some money on the absorbent amount of AI models that you can subscribe to, but really that you'll be able to find and try out a whole bunch of different AI models that you hadn't heard of or used before. I think there's a lot of really great unheard of models that can do some great things in specific

tasks. We have kind of benchmark data and we break down what models are best for what on the

platform. So go check it out. It's 20 bucks a month, AIbox.a. All right, let's get into what OpenAI is

doing. So the first benchmark that I want to talk about is the Code Force benchmark.

They basically ran the GPTOSS 120 billion parameters.

That's the bigger of the two open source models.

They have a 120 billion parameter one and then they have a smaller one.

But basically the bigger parameter, 120 billion parameter one, got an ELO score on code force of 2,600, roughly.

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