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An analysis of 14 different AI models consistently showed that questions requiring extensive logical thought and reasoning led to higher emissions. None of the models that kept emissions below 500 grams of carbon dioxide equivalent achieved higher than 80 per cent accuracy on answering the 1,000 questions correctly,” Dauner explained. For example, answering 600,000 questions with DeepSeek R1 can emit as much carbon as a round-trip flight from London to New York. In comparison, Alibaba Cloud’s Qwen 2.5 can answer over three times more questions with similar accuracy while producing the same emissions.
Queries demanding complex reasoning from AI chatbots, such as those related to abstract algebra or philosophy, generate significantly more carbon emissions than simpler questions, a new study reveals. These high-level computational tasks can produce up to six times more emissions than straightforward inquiries like basic history questions. A study conducted by researchers at Germany’s Hochschule München University of Applied Sciences, published in the journal Frontiers (seen by The Independent), found that the energy consumption and subsequent carbon dioxide emissions of large language models (LLMs) like OpenAI’s ChatGPT vary based on the chatbot, user, and subject matter. “Currently, we see a clear accuracy-sustainability trade-off inherent in LLM technologies.