{"id":56130,"date":"2025-08-10T01:44:08","date_gmt":"2025-08-10T01:44:08","guid":{"rendered":"https:\/\/www.newsbeep.com\/uk\/56130\/"},"modified":"2025-08-10T01:44:08","modified_gmt":"2025-08-10T01:44:08","slug":"openai-will-not-disclose-gpt-5s-energy-use-it-could-be-higher-than-past-models-openai","status":"publish","type":"post","link":"https:\/\/www.newsbeep.com\/uk\/56130\/","title":{"rendered":"OpenAI will not disclose GPT-5\u2019s energy use. It could be higher than past models | OpenAI"},"content":{"rendered":"

In mid-2023, if a user asked OpenAI\u2019s ChatGPT for a recipe for artichoke pasta<\/a> or instructions on how to make a ritual offering<\/a> to the ancient Canaanite deity Moloch, its response might have taken \u2013 very roughly \u2013 2 watt-hours<\/a>, or about as much electricity as an incandescent bulb consumes in 2 minutes.<\/p>\n

OpenAI released<\/a> a model on Thursday that will underpin the popular chatbot \u2013 GPT-5<\/a>. Ask that version of the AI for an artichoke recipe, and the same amount of pasta-related text could take several times \u2013 even 20 times \u2013 that amount of energy, experts say.<\/p>\n

As it rolled out GPT-5, the company highlighted<\/a> the model\u2019s breakthrough capabilities: its ability to create websites, answer PhD-level science questions, and reason through difficult problems.<\/p>\n

But experts who have spent the past years working to benchmark the energy and resource usage of AI models say those new powers come at a cost: a response from GPT-5 may take a significantly larger amount of energy than a response from previous versions of ChatGPT<\/a>.<\/p>\n

OpenAI, like most of its competitors, has released no official information on the power usage of its models since GPT-3, which came out in 2020. Sam Altman, its CEO, tossed out some numbers<\/a> on ChatGPT\u2019s resource consumption on his blog this June. However, these figures, 0.34 watt-hours and 0.000085 gallons of water per query, do not refer to a specific model and have no supporting documentation.<\/p>\n

\u201cA more complex model like GPT-5 consumes more power both during training and during inference. It\u2019s also targeted at long thinking \u2026 I can safely say that it\u2019s going to consume a lot more power than GPT-4,\u201d said Rakesh Kumar, a professor at the University of Illinois, currently working on the energy consumption of computation and AI models.<\/p>\n

The day GPT-5 was released, researchers<\/a> at the University of Rhode Island\u2019s AI lab found that the model can use up to 40 watt-hours of electricity to generate a medium-length response of about 1,000 tokens, which are the building blocks of text for an AI model and are approximately equivalent to words.<\/p>\n

A dashboard<\/a> they put up on Friday indicates GPT-5\u2019s average energy consumption for a medium-length response is just over 18 watt-hours, a figure that is higher than all other models they benchmark except for OpenAI\u2019s o3 reasoning model, released in April, and R1, made by the Chinese AI firm Deepseek.<\/p>\n

This is \u201csignificantly more energy than GPT-4o\u201d, the previous model from OpenAI<\/a>, said Nidhal Jegham, a researcher in the group.<\/p>\n

Eighteen watt-hours would correspond to burning that incandescent bulb for 18 minutes. Given recent reports<\/a> that ChatGPT handles 2.5bn requests a day, the total consumption of GPT-5 could reach the daily electricity demand<\/a> of 1.5m US homes.<\/p>\n

As large as these numbers are, researchers in the field say they align with their broad expectations for GPT-5\u2019s energy consumption, given that GPT-5 is believed to be several times larger than OpenAI\u2019s previous models. OpenAI has not released the parameter counts \u2013 which determine a model\u2019s size \u2013 for any of its models since GPT-3, which had 175bn<\/a> parameters<\/a>.<\/p>\n

A disclosure this summer from the French AI company Mistral finds<\/a> a \u201cstrong correlation\u201d between a model\u2019s size and its energy consumption, based on Mistral\u2019s study of its in-house systems.<\/p>\n

\u201cBased on the model size, the amount of resources [used by GPT-5] should be orders of magnitude higher than that for GPT-3,\u201d said Shaolei Ren, a professor at the University of California, Riverside who studies the resource<\/a> footprint of AI.<\/p>\n

Benchmarking AI power usage<\/p>\n

GPT-4 was widely<\/a> believed<\/a> to be 10 times the size of GPT-3. Jegham, Kumar, Ren and others say that GPT-5 is likely to be significantly larger than GPT-4.<\/p>\n

Leading AI companies like OpenAI believe<\/a> that extremely<\/a> large<\/a> models may be necessary to achieve AGI, that is, an AI system capable<\/a> of doing humans\u2019 jobs. Altman has argued strongly for this view, writing<\/a> in February: \u201cIt appears that you can spend arbitrary amounts of money and get continuous and predictable gains,\u201d though he said GPT-5 did not surpass human intelligence.<\/p>\n

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In its benchmarking study<\/a> in July, which looked at the power consumption, water usage and carbon emissions for Mistral\u2019s Le Chat bot, the startup found a one-to-one relationship between a model\u2019s size and its resource consumption, writing: \u201cA model 10 times bigger will generate impacts one order of magnitude larger than a smaller model for the same amount of generated tokens.\u201d<\/p>\n

Jegham, Kumar and Ren said that while GPT-5\u2019s scale is significant, there are probably other factors that will come into play in determining its resource consumption. GPT-5 is deployed on more efficient hardware than some previous models. GPT-5 appears<\/a> to use a \u201cmixture-of-experts\u201d architecture, which means that it is streamlined so that not all of its parameters are activated when responding to a query, a construction which will likely cut its energy consumption.<\/p>\n

On the other hand, GPT-5 is also a reasoning model, and works in video and images as well as text, which likely makes its energy footprint far greater than text-only operations, both Ren and Kumar say \u2013 especially as the reasoning mode means that the model will compute for a longer time before responding to a query.<\/p>\n

\u201cIf you use the reasoning mode, the amount of resources you spend for getting the same answer will likely be several times higher, five to 10,\u201d said Ren.<\/p>\n

Hidden information<\/p>\n

In order to calculate an AI model\u2019s resource consumption, the group at the University of Rhode Island multiplied the average time that model takes to respond to a query \u2013 be it for a pasta recipe or an offering to Moloch \u2013 by the model\u2019s average power draw during its operation.<\/p>\n

Estimating a model\u2019s power draw was \u201ca lot of work\u201d, said Abdeltawab Hendawi, a professor<\/a> of data science at the University of Rhode Island. The group struggled to find information on how different models are deployed within data centers. Their final paper<\/a> contains estimates for which chips are used for a given model, and how different queries are parceled out between different chips in a datacenter.<\/p>\n

Altman\u2019s June blog post confirmed their findings. The figure he gave for ChatGPT\u2019s energy consumption per query, 0.34 watt-hours per query, closely matches what the group found for GPT-4o.<\/p>\n

Hendawi, Jegham and others in their group said that their findings underscored the need for more transparency from AI companies as they release ever-larger models.<\/p>\n

\u201cIt\u2019s more critical than ever to address AI\u2019s true environmental cost,\u201d said Marwan Abdelatti, a professor<\/a> at URI. \u201cWe call on OpenAI and other developers to use this moment to commit to full transparency by publicly disclosing GPT-5\u2019s environmental impact.\u201d<\/p>\n","protected":false},"excerpt":{"rendered":"In mid-2023, if a user asked OpenAI\u2019s ChatGPT for a recipe for artichoke pasta or instructions on how…\n","protected":false},"author":2,"featured_media":56131,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[20],"tags":[554,733,4308,86,56,54,55],"class_list":["post-56130","post","type-post","status-publish","format-standard","has-post-thumbnail","category-artificial-intelligence","tag-ai","tag-artificial-intelligence","tag-artificialintelligence","tag-technology","tag-uk","tag-united-kingdom","tag-unitedkingdom"],"_links":{"self":[{"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/posts\/56130","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/comments?post=56130"}],"version-history":[{"count":0,"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/posts\/56130\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/media\/56131"}],"wp:attachment":[{"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/media?parent=56130"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/categories?post=56130"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/tags?post=56130"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}