Researchers managed to create a low-cost AI reasoning model rivaling OpenAIs in just 26 minutes, as outlined in a paper published last week. The model, called s1, was refined using a small dataset of 1,000 questions and for under $50, according to TechCrunch.To do this, researchers at Stanford and the University of Washington used a method known as distillation which allows smaller models to draw from the answers produced by larger ones to refine s1 using answers from Googles AI reasoning model, Gemini 2.0 Flash Thinking Experimental. Googles terms of service note that you cant use Geminis API to develop models that compete with the companys AI models. The Verge reached out to Google with a request for comment but didnt immediately hear back.The researchers based s1 on Qwen2.5, an open-source model from Alibaba Cloud. They initially started with a pool of 59,000 questions to train the model on, but found that the larger data set didnt offer substantial gains over a whittled-down set of just 1,000. The researchers say they trained the model on just 16 Nvidia H100 GPUs.The s1 model also uses a technique called test-time scaling, allowing the model to think for a longer amount of time before producing an answer. As noted in the paper, researchers forced the model to continue reasoning by adding Wait to the models response. This can lead the model to doublecheck its answer, often fixing incorrect reasoning steps, the paper says.RelatedOpenAIs o1 reasoning model uses a similar approach, something the buzzy AI startup DeepSeek sought to replicate with the launch of its R1 model that it claims was trained at a fraction of the cost. OpenAI has since accused DeepSeek of distilling information from its models to build a competitor, violating its terms of service. As for s1, the researchers claim that s1 exceeds o1-preview on competition math questions by up to 27%.The rise of smaller and cheaper AI models threatens to upend the entire industry. They could prove that major companies like OpenAI, Microsoft, Meta, and Google dont need to spend billions of dollars training AI, while building massive data centers filled with thousands of Nvidia GPUs.See More: