Every question you ask an AI model could be leaving a bigger carbon footprint than you imagine — and some prompts are 50 times worse than others.
Key Points at a Glance
- Some AI queries emit up to 50 times more CO2 than others
- Reasoning-enabled models generate significantly higher emissions
- Subjects like algebra and philosophy lead to more emissions than history
- Users can reduce emissions by choosing simpler prompts and smaller models
When you ask an AI a question, you’re not just getting a smart answer — you’re also triggering a hidden environmental cost. A new study has revealed that the energy used by large language models (LLMs) to generate responses can vary dramatically depending on the model, the complexity of the question, and even the subject matter. In some cases, a single prompt can produce up to 50 times more carbon dioxide than another.
Published in Frontiers in Communication, the research compared 14 already-trained LLMs by measuring their CO2 emissions while answering 1,000 standardized questions. The findings are striking: reasoning-heavy models like Cogito — which deliver highly accurate, detailed responses — also generate the highest emissions. In contrast, more concise models used fewer tokens and significantly less energy, though at the cost of accuracy.
“The environmental impact of questioning trained LLMs is strongly determined by their reasoning approach,” said lead author Maximilian Dauner from Hochschule München. The study found that reasoning models averaged 543.5 tokens per question, compared to just 37.7 for concise models — a huge jump in computational load and emissions.
The Cogito model, for instance, reached 84.9% accuracy but emitted triple the CO2 compared to similar models with concise outputs. The study concluded there’s a clear accuracy-sustainability trade-off. No model that kept emissions below 500 grams of CO₂ equivalent achieved better than 80% accuracy. In real-world terms, running DeepSeek R1 on 600,000 questions emits as much CO2 as a round-trip flight from London to New York.
Subject matter also plays a significant role. Questions in abstract algebra or philosophy require more computational reasoning, emitting up to six times more CO2 than simple history queries. These findings underscore the unseen environmental cost behind our growing reliance on generative AI tools.
But the good news is that users aren’t powerless. By choosing concise response options, avoiding unnecessarily complex queries, or selecting more efficient models like Qwen 2.5, users can minimize their carbon impact. The research encourages thoughtful AI use — especially when generating outputs that aren’t critical, like novelty images or idle queries.
While results may vary based on hardware, energy grid sources, and local infrastructure, the study is a crucial reminder: every digital convenience comes with a cost. If users are aware of the environmental price tag of their prompts, they may become more intentional in how and why they interact with AI systems.
In a time when AI is reshaping industries and daily life, understanding its ecological implications might be the most important prompt of all.
Source: Frontiers
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