Recently on the A16Z podcast, Arthur Mensch, CEO of Mistral, thought that the gap between closed and open source LLMs might be 6 months. That might have been way off. It may have already closed.

Mistral 8x7B, which just dropped last month, is now a rising star on the Hugging Face LLM Leaderboard, where AI models are voted on in real-time. It's not a perfect system, but it's a strong indicator of what research might later validate.

Debuting last month, the 8x7B quickly climbed to the 7th spot on the leaderboard, becoming the only open-source model in the top 10.

While it seems that open and closed source LLMs will converge on the text side, there some other factors to consider.

First, Multimodality. Excelling in text alone isn't enough. Modern AI models must handle audio, speech, images, and soon, video. ChatGPT does this pretty well, where you can generate funny images and also speak directly with the model.

Next, the Application Layer. It's all about user experience. ChatGPT exemplifies this, offering an engaging and user-friendly interface, especially when compared with something like Google Bard, which I’ve personally found frustrating to work with.

Lastly, Ecosystem Integration. It's about how these models integrate with our data and teams. ChatGPT's recent team plan launch, promoting sharing and internal custom GPTs, underscores the importance of connecting these models to our data to make them smarter about what we care about.

In conclusion, while open source models are rapidly advancing in text capabilities, proprietary models, supported by their companies, are likely refocusing on multimodality, enhanced application layers, and deeper integration with user data and teams.

I’m eager to read your thoughts. Please share them below.