Link: OpenAI's o1 models aren't as simple as the next step up from GPT-4o as they introduce major cost and performance trade-offs in exchange for improved "reasoning" (Simon Willison/Simon Willison's Weblog)

OpenAI has unveiled two innovative models, o1-preview and o1-mini, which are not merely advancements over GPT-4o but offer significant enhancements in AI reasoning.

These models are intricately trained to engage in a more thoughtful response process, emphasizing a step-by-step reasoning method that could handle complex problem-solving tasks more effectively.

Despite their capabilities, the o1 models come with certain trade-offs, such as longer response times and specific API restrictions, making them less suited for tasks requiring quick outputs or image inputs.

The introduction of "reasoning tokens" within these models marks a significant technical evolution, billed as output tokens yet invisible in the API responses, aiming to enhance the models' reasoning without compromising policy compliance or competitive edge.

Practical examples from OpenAI illustrate how these models perform particularly well in tasks like script writing, problem-solving, and data interpretation, showcasing their potential to transform challenging AI applications.

While these models present a shift in how AI can be applied to more complex reasoning tasks, their full potential and implications for future AI development are still unfolding in the broader community.

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