OpenAI's New Model is Out: Thoughts on #o1

Recently, OpenAI introduced a fresh contender to the AI scene: their new model termed #o1. This model boasts optimization for "reasoning," a term that catches many an AI enthusiast's eye. The notion of a model excelling in reasoning is quite compelling, especially for a variety of use cases involving LLM agents.

The Excitement and Reality Check

There's always a buzz when a new model emerges, and #o1 is no exception. However, amidst the excitement, my initial impressions have been somewhat tempered by practicality. For those us entrenched in everyday AI applications, the model's reach feels limited for now.

When putting #o1 through its paces with some standard tests—namely in summarization and Retrieval-Augmented Generation (RAG) tasks—it became apparent that the results closely mirrored those of the well-known gpt-4o. But here's the kicker: #o1 runs notably slower and doesn't come cheap.

The Practical Constraints

For these reasons, the model isn't quite ready for prime time on platforms like airouter.io. The low rate limits currently imposed further relegate #o1 to the sidelines. As much as we're rooting for a reasoning superstar, the practicalities can’t be ignored.

For those keen to explore potential applications with #o1, it may be worth experimenting within specific environments where these limitations can be managed.

It's always thrilling to witness AI's evolving landscape, with models like #o1 stretching the boundaries of what's possible—however, sometimes the reality of integrating such advances lags behind the initial excitement.