Factor version soma-ai-ef-2026.10-dataset-v3.0.0, dated . Location-based grid averages, not renewable contracts. Estimates from published benchmarks.
A query of 1210 tokens to a mid-size AI model (class B, such as gpt-4o) served from Asia Pacific - Singapore emits about 0.055 g CO₂e, counting electricity, the share of server hardware, data-centre construction and amortised training; a million tokens emit 0.0401 kg CO₂e. A small model (class A) emits 0.016 g per query and a frontier model (class C) 0.21 g.
Asia Pacific - Singapore ranks 15 of 15 regions for class B, where 1 is the lowest carbon. Compared with EU North - Sweden, the same query here emits 5.6 times as much; compared with Asia Pacific - Singapore, 1.0 times as much. The grid, not the model, sets most of the difference.
| Model class | Electricity kg | Hardware kg | Construction kg | Training kg | Total kg CO₂e | g per query | Water L | mL per query |
|---|---|---|---|---|---|---|---|---|
| Class A (small) | 0.01090 | 0.00027 | 0.00014 | 0.00060 | 0.012 | 0.016 | not available | not available |
| Class B (mid-size) | 0.03571 | 0.00101 | 0.00046 | 0.00293 | 0.040 | 0.055 | not available | not available |
| Class C (frontier) | 0.13296 | 0.00388 | 0.00169 | 0.01284 | 0.151 | 0.21 | not available | not available |
Example: a company of 100 people where 80 use a class B assistant for 2,000 messages a month each sends about 2,323 million tokens a year. Served from Asia Pacific - Singapore, that is 93 kg CO₂e a year, against 17 kg in EU North - Sweden.
Water: not available for Asia Pacific - Singapore. The dataset publishes no off-site water factor for this region, so SOMA prints no water figure rather than a partial one.
Class B, total kg CO₂e per million tokens, lowest first: EU North - Sweden 0.00714, EU West - France 0.0082, EU North - Finland 0.0102, EU West - Belgium 0.014, EU West - UK (London) 0.0212, Netherlands 0.0235, EU West - Ireland 0.0245, US East - N. Virginia 0.0261, US West - Oregon 0.0275, EU Central - Germany 0.0303, US South Central - Texas 0.0312, US South - Southeast 0.0351, US Central - Iowa 0.0381, Asia Pacific - Japan 0.0395, Asia Pacific - Singapore 0.0401.
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, data-centre construction 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). The modelled range of the models inside a class, around the class value, is −41% to +48% for class A, −39% to +24% for class B and −67% to +55% for class C.
Dataset: AI Inference Emission and Resource Factors for Corporate GHG Inventories, Llopis, G., SOMA AI, October 2026, CC BY 4.0, concept DOI 10.5281/zenodo.20443585, which resolves to the latest version (this version 10.5281/zenodo.23371447). Method: Accounting for Purchased AI Inference in Corporate GHG Inventories: A Token-Based Method, arXiv:2606.10660, under peer review.
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When citing a single value, name the region and class, for example "0.0401 kg CO₂e per million tokens, class B, Asia Pacific - Singapore, SOMA AI factors October 2026", and link to app.somaai.earth/ai-factors/region/singapore.