Even as OpenAI continues clinging to its assertion that the only path to AGI lies through massive financial and energy expenditures, independent researchers are leveraging open-source technologies to match the performance of its most powerful models — and do so at a fraction of the price.
Last Friday, a unified team from Stanford University and the University of Washington announced that they had trained a math and coding-focused large language model that performs as well as OpenAI’s o1 and DeepSeek’s R1 reasoning models. It cost just $50 in cloud compute credits to build. The team reportedly used an off-the-shelf base model, then distilled Google’s Gemini 2.0 Flash Thinking Experimental model into it. The process of distilling AIs involves pulling the relevant information to complete a specific task from a larger AI model and transferring it to a smaller one.
What’s more, on Tuesday, researchers from Hugging Face released a competitor to OpenAI’s Deep Research and Google Gemini’s (also) Deep Research tools, dubbed Open Deep Research, which they developed in just 24 hours. “While powerful LLMs are now freely available in open-source, OpenAI didn’t disclose much about the agentic framework underlying Deep Research,” Hugging Face wrote in its announcement post. “So we decided to embark on a 24-hour mission to reproduce their results and open-source the needed framework along the way!” It reportedly costs an estimated $20 in cloud compute credits, and would require less than 30 minutes, to train.
Hugging Face’s model subsequently notched a 55% accuracy on the General AI Assistants (GAIA) benchmark, which is used to test the capacities of agentic AI systems. By comparison, OpenAI’s Deep Research scored between 67 – 73% accuracy, depending on the response methodologies. Granted, the 24-hour model doesn’t perform quite as well as OpenAI’s offering, but it also didn’t take billions of dollars and the energy generation capacity of a mid-sized European nation to train.
These efforts follow news from January that a team out of University of California, Berkeley’s Sky Computing Lab managed to train their Sky T1 reasoning model for around $450 in cloud compute credits. The team’s Sky-T1-32B-Preview model proved the equal of early o1-preview reasoning model release. As more of these open-source competitors to OpenAI’s industry dominance emerge, their mere existence calls into question whether the company’s plan of spending half a trillion dollars to build AI data centers and energy production facilities is really the answer.
When you’re playing around with ChatGPT or Gemini for the first time, it’s easy to just toss in a quick, simple prompt and see what happens.“What can
Chinese smartphone brand, Honor, is usually at the helm of camera-centric smartphone innovations and delivering ultra-slim foldable phones. For its ne
Apple’s efforts in the health segment are a class ahead of the competition. But more than just racing ahead with innovation, the company has taken a m
The shift away from Google Assistant, and into the Gemini era, is nearly in its last stages. One can feel nostalgic about the eponymous virtual assist
I don’t know about you, but I’m tired of the term AI. When every company in the industry is using the term, it loses its meaning, and I’m tired of mor
The first landmark win in an AI copyright case is here. A Delaware court has ruled that a tech startup used copyright-protected material to build a co
OpenAIOpenAI CEO Sam Altman announced via an X post Wednesday that the company’s o3 model is being effectively sidelined in favor of a “simplified” GP
Elon Musk-led xAI has announced their latest AI model, Grok-3, via a livestream. From the get-go, it was evident that the company wants to quickly fil
We are a comprehensive and trusted information platform dedicated to delivering high-quality content across a wide range of topics, including society, technology, business, health, culture, and entertainment.
From breaking news to in-depth reports, we adhere to the principles of accuracy and diverse perspectives, helping readers find clarity and reliability in today’s fast-paced information landscape.
Our goal is to be a dependable source of knowledge for every reader—making information not only accessible but truly trustworthy. Looking ahead, we will continue to enhance our content and services, connecting the world and delivering value.
