Today on the AI Daily Brief, counting down the five most impactful AI model releases of 2025.

The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI.

All right, friends, quick announcements before we dive in.

First of all, thank you to today's sponsors, robots and pencils, blitzie, and super intelligent. To get an ad-free version of the show, go to patreon.com slash AI Daily Brief, or you can subscribe, of course, on Apple Podcasts. And if you are interested in learning about sponsoring the show, you can find out more information at AIdailydief.aI. Or send us a note at sponsors at AIdailydailybrief.com. Now, we are in the thick of end-of-year coverage, and you might have heard me say during my episode about the 10 biggest stories of AI overall that I had been planning on bundling this five biggest AI model releases as its own section of that show. Now, of course, that show got really long, and I didn't want to

overwhelm the list with just model releases, which are obviously in some ways the quintessential

events around which we mark our AI calendars. And so instead, what we're doing is we're breaking

this out into its own category, its own episode. And whereas that top 10 episode did not rank and count down the

stories other than saying that I thought that vibe coding was the most important, this one is

actually a countdown. I labored over the ranking because I think it's kind of fun to give you guys

something to debate and tell me either how right I am or more likely how wrong I am.

We're going to start off with a couple of honorable or maybe as the

case might be dishonorable mentions. Specifically, I want to talk about the absence of a strong

model from meta this year. Now yes, Lama 4 did technically come out at the beginning of the year.

However, it flopped. One of the challenges for META was that Lama was coming into existence in a post-deep-seek

world. And in that post-deep-seek world, everything around open source had changed. For a couple of years,

meta got to be the standard bearer of open-source AI models. And even if their models weren't as

state-of-the-art as the closed labs, they had this distinct and unique space. Now, that changed a little when Mistral came on the scene and started to compete for that narrative and intellectual and practical space, but it has changed dramatically this year in the context of the rise of the Chinese open-weight models. Now, even back then, people were surprised at what we got with Lama 4. In the local

Lama subreddit, someone wrote, Lama 4 didn't meet expectations. Some even suspect it might have been

tweaked for benchmark performance. But meta isn't short on compute power or talent, so why the

underwhelming results? Meanwhile, models like Deep Seek and Quen blew Lama out of the water

months ago,

it's hard to believe Meta lacks data quality or skilled researchers.

...