In response to inquiries about Artichoke recipes made to OpenAI’s ChatGPT in mid-2023, whether for pasta or guidance on rituals related to Moloch, the ancient Canaanite deity, the feedback was quite harsh—2 watts—which consumes approximately the same energy as an incandescent bulb over two minutes.

On Thursday, OpenAI unveiled a model that powers the widely-used chatbot GPT-5. When queried about Artichoke recipes, experts suggest that the energy consumed for similar pasta-related text could be multiple times greater (up to 20 times).

The release of GPT-5 introduced a groundbreaking capability for the model to answer PhD-level scientific inquiries, illuminating rationales for complex questions.

Nevertheless, specialists who have assessed energy and resource consumption of AI models over recent years indicate that these newer variants come with a cost. Responses from GPT-5 may require substantially more energy than those from earlier ChatGPT models.

Like many of its rivals, OpenAI has not provided official data regarding the power consumption of models since announcing GPT-3 in 2020. In June, Altman discussed the resource usage of ChatGPT on his blog. However, the figures presented—0.34 watt-hours and 0.000085 gallons of water per query—lack specific model references and supporting documentation.

“More complex models like GPT-5 require greater power during both training and inference, leading to a significant increase in energy consumption compared to GPT-4.”

On the day GPT-5 launched, researchers from the University of Rhode Island AI Lab found that the model could consume up to 40 watts to generate a medium-length response of approximately 1,000 tokens.

A dashboard released on Friday indicated that GPT-5’s average energy use for medium-length responses exceeds 18 watts, surpassing all other models except for OpenAI’s O3 inference model launched in April, developed by Chinese AI firm Deepseek.

According to Nidhal Jegham, a researcher in the group, this is “significantly more energy than OpenAI’s prior model, GPT-4O.”

To put that in perspective, one watt of 18 watt-hours equates to using that incandescent light bulb for 18 minutes. Recent reports indicate that ChatGPT processes 2.5 billion requests daily, suggesting that GPT-5’s total energy consumption could match that of 1.5 million American households.

Despite these figures, experts in the field assert they align with expectations regarding GPT-5’s energy consumption, given its significantly larger scale compared to OpenAI’s earlier model. Since GPT-3, OpenAI has not disclosed the parameter count of any models. The earlier version contained 17.5 billion parameters.

This summer, insights from French AI company Mistral highlighted a “strong correlation” between model size and energy use, based on their internal systems research.

“The amount of resources consumed by the model size [for GPT-5] is noteworthy,” observed Xiao Len, a professor at the University of California Riverside. “We are facing a significant AI resource footprint.”

AI Power Usage Benchmark

GPT-4 was widely regarded as being 10 times larger compared to GPT-3. Jegham, Kumar, and Ren believe GPT-5 is likely to be even larger than GPT-4.

Major AI companies like OpenAI assert that significantly larger models may be essential for achieving AGI, an AI system capable of performing human tasks. Altman has emphasized this perspective, stating in February: “It seems you can invest any amount and receive continuous, predictable returns,” but that GPT-5 does not surpass human intelligence.

According to benchmarks from a study performed in July, Mistral’s LE chatbot exhibited a direct correlation between model size and its resource usage regarding power, water, and carbon emissions.

Jegham, Kumar, and Ren indicated that while the scale of GPT-5 is crucial, other factors will likely influence resource consumption. GPT-5 utilizes more efficient hardware compared to previous iterations. It employs a “mixture” architecture, allowing not all parameters to be active while responding, which could help diminish energy use.

Moreover, since GPT-5 operates as an inference model that processes text, images, and video, this is expected to lead to a larger energy footprint when compared to solely text-based processing, according to Ren and Kumar.

“In inference mode, the resources spent to achieve identical outcomes can escalate by five to ten times,” remarked Ren.

Hidden Information

To assess the resource consumption of AI models, a team from the University of Rhode Island calculated the average time taken by the model to answer queries—such as pasta recipes or offerings to Moloch—multiplied by the average power draw of the model during operation.

Estimating the model’s power draw involved significant effort, shared Abdeltawab Henderwi, a Professor of Data Science at the University of Rhode Island. The team faced difficulties in sourcing information about the deployment of various models within data centers. Their final paper includes estimates detailing chip usage for specific models and the distribution of queries among different chips in the data centers.

Altman’s blog post from June affirmed their results, revealing that his indicated energy consumption for queries on ChatGPT, at 0.34 watt-hours, closely matches findings from the team for GPT-4O.

Other team members, including Hendawi, Jegham, and others emphasized the need for increased transparency from AI firms when releasing new models.

“Addressing the true environmental costs of AI is more critical now than ever,” stated Marwan Abdelatti, a Professor. “We urge OpenAI and other developers to commit to full transparency in disclosing the environmental impact of GPT-5.”