Over the past year, the AI world has been abuzz with the rapid release of large language models (LLMs), each boasting advancements that push the boundaries of what’s possible with generative AI. The pace at which new models are emerging is breathtaking. Just last weekend, xAI released its Grok language model, a behemoth with 314 billion parameters, under the Apache 2.0 license.
These models, powered by an ever-increasing number of parameters and trained on colossal datasets, have improved our efficiency to generate text and write (as well as understand) complex code. However, the sheer number of options available can feel both exciting and daunting. Making informed decisions about which to use — considering output quality, speed, and cost — becomes a problem.
The answer lies not just in the specifications sheets or benchmark scores but in a holistic understanding of what each model brings to the table. In this blog post, we curate a select list of LLMs making waves over the past year. At the same time, we look to provide answers to some of the frequently asked questions.