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arXiv · 2608.16657

The ultimate carbon cost of a ChatGPT query

Paul Kron
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Abstract

This paper reviews and combines findings from the fields of product and life-cycle analysis [36, 38], the usage of modern transformer- based large language models (LLM) [6], as well as on greenhouse gas emissions and the ultimate cost of their subsequent consequences for future generations [2]. In this paper, it is shown that the carbon cost of a LLM query is in the order of magnitude of (USD) $0.4 per query for the future human population in the form of environmental disruptions. This corresponds to emissions in the magnitude of 10 gCO2eq/query. The most significant unknown factor in that calculation being the number of tokens computed (1k to 100k tokens equal 1.2 cent/query to 120 cent/query). This number is subject to a wide range of calculation uncertainties and is less to be seen as a matter of fact and more as an order of magnitude estimate. This estimate is aimed towards aiding the discourse surrounding AI systems by uncover- ing the inevitable consequences of technological development by the means of attaching a consequence in a familiar unit to it. By the introduction of the per query ultimate carbon cost (QCC), even if attached to great uncertainty, it is highlighted that the use of AI services happens within hypercomplex interdependent systems and has concrete consequences for our planetary health. The spread of the awareness about the interdependence of planetary health and AI usage can be useful for the individual user in the formation of political opinion through discourse [5] as well as a literate usage of AI systems [31]. Ways to increase the accuracy of the estima- tions, such as incorporating the cost of AIs water consumption or further determining the realistic token count of a query, have been identified as further research targets.

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Paul Kron. 2026-08-17. The ultimate carbon cost of a ChatGPT query. https://arxiv.org/abs/2608.16657

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