Executive Guide · Open access
Research Summary: The ultimate carbon cost of a ChatGPT query
- Original authors
- Attribution requires verification
- Original source
- arXiv — Computers and Society
- Summary & Analysis prepared by
- Aziz Shuaib Ausi
- Resource type
- Research Summary / Knowledge Resource
- Resource published on AZIZ OS
- 19 August 2026
- Last updated
- 22 September 2026
- Reading time
- 1 min
- Publication type
- Executive Guide
- Availability
- Open access
About this Summary & Analysis
AZIZ OS provides independently prepared summaries and analytical interpretations of externally published research and knowledge sources. The underlying works remain attributable to their original authors and rights holders. This resource is intended to improve accessibility and understanding and does not replace the original publication.
Recent research from arXiv analyzes the carbon cost associated with each query to a Large Language Model (LLM), such as ChatGPT. It estimates an environmental cost of approximately $0.4 USD per query for future generations due to environmental disruptions, corresponding to emissions around 10 gCO2eq/query. The primary variable impacting this cost is the number of tokens processed per query, which can range significantly.
Why it matters
This research highlights the environmental footprint and future financial liabilities associated with the increasing adoption and usage of AI, particularly large language models. Understanding these costs is critical for developing sustainable technology strategies and integrating environmental considerations into AI development and deployment frameworks.
Key insights
- A single LLM query (e.g., ChatGPT) incurs a carbon cost estimated at $0.4 USD for the future human population, primarily due to environmental disruptions.
- This financial cost corresponds to approximately 10 gCO2eq of emissions per query.
- The total number of tokens computed per query is the most significant variable, leading to a wide range of potential costs from $0.012 to $1.20 per query.
Source
arXiv — Computers and Society — https://arxiv.org/abs/2608.16657
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- Verification ID
- ASA-EXG-2026-00399
- Version
- v1.0 · r0
- Issued
- 19 August 2026
- Resource prepared by
- Aziz Shuaib Ausi
- Resource status
- Research Summary / Knowledge Resource
- Underlying work
- The ultimate carbon cost of a ChatGPT query
- Original authors
- Attribution requires verification
- Original source
- arXiv — Computers and Society
- Provenance status
- Attribution requires verification
- Rights
- Underlying publication rights remain with the respective copyright holder(s). Refer to the original source for authoritative publication and licensing information.
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