Turns out, it’s not that hard to do what OpenAI does for less

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Turns out, it’s not that hard to do what OpenAI does for less

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Even as OpenAI

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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

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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

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’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

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tools, dubbed

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, 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

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. “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

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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.

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#Turns #hard #OpenAI

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