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<\/p>\nGPT-5 may consume as much power daily as 1.5 million US homes<\/p>\n
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OpenAI’s latest model, GPT-5, is drawing scrutiny from researchers for what they say could be an immense increase in energy consumption \u2013 and for the company’s refusal to disclose official power usage figures.<\/strong><\/p>\n Experts say GPT-5’s enhanced capabilities, from building websites to solving PhD-level science problems, come at a significant resource cost.<\/p>\n Early independent benchmarks suggest it may consume vastly more electricity per response than earlier versions of ChatGPT.<\/p>\n In mid-2023, answering a simple question – like providing a recipe – might have taken GPT-4 roughly 2 watt-hours, about the power of running an incandescent light bulb for two minutes.<\/p>\n Researchers at the University of Rhode Island’s AI lab found that GPT-5 could use as much as 40 watt-hours for a similar-length answer of about 1,000 tokens, with an average usage of just over 18 watt-hours, significantly higher than GPT-4o<\/a>, OpenAI’s previous flagship model.<\/p>\n Nidhal Jegham, a researcher at the lab, said this aligns with expectations for a model believed to be several times larger than GPT-4.<\/p>\n At GPT-5’s average consumption rate, and assuming ChatGPT’s reported 2.5 billion daily requests, the model could theoretically draw enough power each day to supply 1.5 million US homes.<\/p>\n OpenAI has not released energy data for any of its models since GPT-3 in 2020, which had 175 billion parameters.<\/p>\n CEO Sam Altman published general resource figures in June \u2013 0.34 watt-hours and 0.000085 gallons of water per query \u2013 but these were not tied to a specific model and lacked documentation.<\/p>\n Rakesh Kumar, a professor at the University of Illinois, said larger models inevitably draw more energy, both in training and in inference.<\/p>\n “A more complex model like GPT-5 consumes more power both during training and during inference. It’s also targeted at long thinking \u2026 I can safely say that it’s going to consume a lot more power than GPT-4,” Kumar said<\/a>.<\/p>\n Shaolei Ren of the University of California, Riverside, noted that GPT-5’s multimodal reasoning abilities and extended computation time likely push its footprint even higher.<\/p>\n “Based on the model size, the amount of resources [used by GPT-5] should be orders of magnitude higher than that for GPT-3,” said Ren who studies the resource footprint of AI.<\/p>\n However, GPT-5’s efficiency may be aided by a “mixture-of-experts” architecture, which activates only parts off the model per query, and by deployment on more advanced hardware than previous iterations. Still, both Ren and Kumar warned that GPT-5’s reasoning mode could multiply resource use five- to tenfold.<\/p>\n