The video reviews OpenAI’s new model, GPT-01, highlighting its superior performance over previous models like GPT-4 in benchmarks related to math, coding, and reasoning tasks, particularly in high-stakes environments. The reviewer conducts live tests demonstrating GPT-01’s advanced capabilities and discusses its underlying reinforcement learning algorithm, while also addressing its accessibility and pricing for users.
In the video, the reviewer discusses OpenAI’s newly released model, referred to as GPT-01, which is based on the enigmatic Strawberry or QAR project. The reviewer emphasizes that GPT-01 significantly outperforms previous models, including GPT-4, in various benchmarks such as competitive math, coding, and reasoning tasks. The video includes a detailed analysis of benchmark scores, showcasing GPT-01’s superiority in complex problem-solving scenarios, particularly in high-stakes environments like the American Invitational Mathematics Examination (AIME) and competitive programming platforms like Codeforces.
The reviewer presents a series of charts comparing GPT-01’s performance against GPT-4 and other leading models. Notably, GPT-01 excels in challenging reasoning tasks, achieving higher accuracy rates than both GPT-4 and expert human performance in scientific questions. The video highlights that GPT-01’s advancements are not just marginal but represent a substantial leap in capabilities, making it a formidable tool for programmers, mathematicians, and researchers alike.
To demonstrate GPT-01’s capabilities, the reviewer conducts live tests, comparing its performance with Claude 3.5 Sonet, another leading AI model. The tests include coding challenges, such as creating a Tetris game and a 3D game similar to Minecraft, where GPT-01 shows a remarkable ability to generate functional code. The reviewer notes that while GPT-01 takes longer to respond, this delay is indicative of its deeper reasoning process, which ultimately leads to more accurate and functional outputs.
The video also delves into the underlying mechanics of GPT-01, explaining that it employs a reinforcement learning algorithm that enhances its problem-solving abilities. This algorithm allows the model to evaluate multiple strategies and scenarios before arriving at a conclusion, akin to the Monte Carlo tree search used in advanced AI applications. The reviewer emphasizes that this approach enables GPT-01 to tackle complex problems more effectively than its predecessors, making it suitable for tasks that require strategic thinking and multi-step reasoning.
Finally, the reviewer discusses the accessibility and pricing of GPT-01, noting that it is currently available to GPT Plus and Team users, with a limited number of messages per week. The model’s pricing is higher than that of GPT-4 and Claude 3.5 Sonet, reflecting its advanced capabilities. The video concludes with a call to action for viewers to share their thoughts on GPT-01 and its potential applications, while also encouraging them to subscribe for more updates on AI developments.