When researchers tested 14 large language models on 1,000 benchmark questions, they discovered something wild. Complex, reasoning-intensive prompts can generate up to 50 times more carbon emissions than basic ones. Yeah, you read that right. Fifty times.
The study revealed that abstract algebra and philosophy questions produce six times more emissions than straightforward history queries. It’s all about those reasoning tokens. The more the AI needs to think, the more carbon it spews into the atmosphere. Simple recall? Low emissions. Complex reasoning? Welcome to carbon city. Reasoning-enabled models generate 543.5 thinking tokens per question compared to just 37.7 for concise models.
Different models, different problems. Some LLMs emit four times more CO₂ than others while delivering the same accuracy. Take DeepSeek R1. Getting it to answer 600,000 questions pumps out as much carbon as a round-trip transatlantic flight. Meanwhile, Qwen 2.5 cranks out nearly twice as many answers for the same environmental cost. Model architecture matters, apparently.
The type of task makes a huge difference. High school history questions barely register on the emissions scale. But throw some abstract philosophy at these models? Watch those carbon numbers climb. Mathematics and abstract algebra are equally guilty. The AI allocates resources differently for each task type, and complex reasoning demands serious computational muscle. With computational power for AI doubling every 100 days, energy demands continue to skyrocket alongside reasoning capabilities.
Hardware and infrastructure pile on more complications. Data center efficiency varies. Local energy sources matter. Cooling systems add indirect emissions. A prompt answered in one region might have a completely different carbon footprint than the same prompt answered elsewhere. The grid’s carbon intensity changes everything. Data centers already consumed 4.4% of U.S. electricity in 2023, with projections soaring to 12% by 2028.
Scale makes this scarier. Large enterprises querying AI systems thousands of times daily can rack up emissions comparable to major travel. Regular complex prompting by researchers or businesses dramatically increases organizational carbon outputs. Every query adds up.
The trade-off is brutal. Better AI reasoning means worse environmental impact. Users and organizations face a choice: informational depth or environmental responsibility. Efficient AI practices could help meet global emissions goals and corporate ESG commitments. But right now, every complex prompt is another small punch to the planet’s gut.
References
- https://www.business-standard.com/technology/tech-news/ai-chatbots-that-reason-emit-more-carbon-than-ones-with-simple-reply-study-125061900579_1.html
- https://time.com/7295844/climate-emissions-impact-ai-prompts/
- https://www.sciencedaily.com/releases/2025/06/250619035520.htm
- https://bioengineer.org/certain-ai-prompts-generate-up-to-50-times-more-co2-emissions-than-others-new-study-reveals/
- https://frontiersin.org/news/2025/06/19/ai-prompts-50-times-more-co2-emissions
