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The environmental impact of questioning trained LLMs is strongly determined by their reasoning approach, with explicit reasoning processes significantly driving up energy consumption and carbon emissions," said first author Maximilian Dauner, a researcher at Hochschule München University of Applied Sciences and first author of the Frontiers in Communication study. "We found that reasoning-enabled models produced up to 50 times more CO₂ emissions than concise response models." Reasoning Models Burn More Carbon, Not Always for Better Answers The team tested 14 different LLMs, each ranging from seven to 72 billion parameters, using 1,000 standardized questions from a variety of subjects. "None of the models that kept emissions below 500 grams of CO₂ equivalent achieved higher than 80% accuracy on answering the 1,000 questions correctly." CO2 equivalent is the unit used to measure the climate impact of various greenhouse gases. The researchers said that their results may be impacted by the choice of hardware used in the study, an emission factor that may vary regionally depending on local energy grid mixes, and the examined models."