Factor version soma-ai-ef-2026.09-lifecycle-v2, dated . Location-based grid averages, not renewable contracts. Estimates from published benchmarks.
A query of 400 tokens to a mid-size AI model (class B, such as gpt-4o) served from Global average emits about 0.029 g CO₂e, counting electricity, the share of server hardware and amortised training; a million tokens emit 0.0721 kg CO₂e. A small model (class A) emits 0.007 g per query and a frontier model (class C) 0.041 g.
The global average is the factor to use when the serving region is unknown. It sits between the cleanest region, EU Sweden (0.0131 kg per million tokens for class B), and the highest, Japan (0.0871 kg).
| Model class | Electricity kg | Hardware kg | Training kg | Total kg CO₂e | g per query | Water L | mL per query |
|---|---|---|---|---|---|---|---|
| Class A (small) | 0.016 | 0.0008 | 0.0008 | 0.018 | 0.007 | 0.144 | 0.058 |
| Class B (mid-size) | 0.065 | 0.0030 | 0.0041 | 0.072 | 0.029 | 0.582 | 0.23 |
| Class C (frontier) | 0.082 | 0.0039 | 0.0179 | 0.104 | 0.041 | 0.740 | 0.3 |
Example: a company of 100 people where 80 use a class B assistant for 2,000 messages a month each sends about 768 million tokens a year. Served from Global average, that is 55 kg CO₂e a year.
A 400-token query to a class B model served from Global average consumes about 0.23 mL of water, and a million tokens 0.582 litres. The figure combines the water evaporated on site for cooling with the water embedded in generating the grid electricity.
Class B, total kg CO₂e per million tokens, lowest first: EU Sweden 0.0131, EU France 0.0161, EU Finland 0.0201, EU Belgium 0.0291, UK (London) 0.0451, EU Netherlands 0.0501, US East 0.0511, EU Ireland 0.0531, US West (Oregon) 0.0531, US Texas 0.0611, EU Germany 0.0661, US South 0.0691, US Central (Iowa) 0.0741, Singapore 0.0831, Japan 0.0871.
Electricity term: GPU energy per token from the ML.ENERGY Leaderboard v3, a facility overhead of 1.20, and grid carbon intensity from EPA eGRID 2023 and Ember 2023. Embodied hardware and amortised training terms are documented on the methodology page. Water combines on-site cooling water (Li et al. 2025) with the water embedded in grid electricity (Reig et al. 2020). Uncertainty on the electricity term is ±40% for class A and ±50% for classes B and C.
Dataset: AI Inference Emission and Resource Factors for Corporate GHG Inventories, Guillermo Llopis, SOMA AI, September 2026, CC BY 4.0, concept DOI 10.5281/zenodo.20443585 (this version 10.5281/zenodo.22767475). Method: Accounting for AI Inference in Corporate GHG Inventories: A Four-Tier Methodology for Scope 3 Category 1 Reporting, arXiv:2606.10660, under peer review.
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When citing a single value, name the region and class, for example "0.0721 kg CO₂e per million tokens, class B, Global average, SOMA AI factors September 2026", and link to app.somaai.earth/ai-factors/region/global-avg.