Skip to content
AIWaterUse.org
ES
Menu
↑ ↓ to move · Enter to open · Esc to close

Calculator

Your AI footprint

Estimate the water, energy and carbon behind your AI use, with sources for every number.

Type it the way you'd say it. The dials below follow.

Model
Fable 5no data yet

standard class · 0.8 Wh per 1,000 tokens (mid benchmark)

How much do you use it?25 prompts/day
Show results
What gets counted

The same prompt can be “five drops” or “a bottle of water” depending on what you count: just data-center cooling (Low), the water behind the electricity (Mid), or training and hardware too (High).

Read the methodology
WaterMid: + electricity

6.8gallons

= 25.6 liters

Claude Sonnet, 25 prompts/day · per year, Mid scenario

Energy

5.1 kWh

Carbon

1.8 kg CO₂e

That's about…

Sized to your result, Mid scenario
  • 51.1water bottles
  • 6.4almonds' worth of water
  • 4.3toilet flushes
  • 0.39showers
  • 63.9hours of streaming
  • 4.5miles in a gas car
ShareDownload image
  1. 01

    Say it or set it

    Describe your use in a sentence, or set the model, usage and timeframe by hand. Both drive the same dials.

  2. 02

    Tokens become energy

    Each model sits in a benchmarked energy class, or uses its official figure where one is published.

  3. 03

    Energy becomes water

    Multiplied by the factor for the boundary you pick: cooling only, plus electricity, or the full lifecycle.

How we calculate thisWhere do I find my token count?

Sources

Cited on this page, in order of appearance.

  1. 1Jegham, N., Abdelatti, M., Elmoubarki, L., & Hendawi, A. (2025). How Hungry is AI? Benchmarking Energy, Water, and Carbon Footprint of LLM Inference. arXiv preprint arXiv:2505.09598 (v6, Nov 2025). (accessed 2026-06) preprint
  2. 2Mistral AI (2025). Our contribution to a global environmental standard for AI. Mistral AI lifecycle analysis with Carbone 4 and ADEME, reviewed by Resilio and Hubblo: 45 mL water & 1.14 gCO₂e per 400-token Le Chat response (marginal inference); Mistral Large 2 training plus its first 18 months of use: 20.4 ktCO₂e and 281,000 m³ of water. No energy (Wh) figure disclosed. (accessed 2026-09) official disclosure
  3. 3AIWaterUse (2026). AIWaterUse methodology: disclosed site assumptions and derivations. aiwateruse.org/methodology: blended tokens per exchange, input/output split, unit definitions and arithmetic derivations, reviewed quarterly. (accessed 2026-06) site assumption
  4. 4Siddik, M. A. B., Shehabi, A., & Marston, L. (2021). The environmental footprint of data centers in the United States. Environmental Research Letters 16(6): watershed-scale direct + indirect water footprint. (accessed 2026-06) peer-reviewed
  5. 5US Environmental Protection Agency (2024). Emissions & Generation Resource Integrated Database (eGRID). US EPA: grid carbon intensity by region. (accessed 2026-06) government
  6. 6Water Footprint Network (Mekonnen, M. M., & Hoekstra, A. Y.) (2011). Product water footprint database. Water Footprint Network: agricultural water footprints (coffee, beef, cotton, almonds). (accessed 2026-06) peer-reviewed
  7. 7US Environmental Protection Agency (2024). WaterSense: residential water use and fixture flow rates. US EPA: 82 gal/person/day household use; fixture flow rates. (accessed 2026-06) government
  8. 8Kamiya, G. (2020). The carbon footprint of streaming video: fact-checking the headlines. International Energy Agency commentary: ~0.08 kWh per viewing hour across device, network and data center. (accessed 2026-06) government
  9. 9US Environmental Protection Agency (2024). Greenhouse gas emissions from a typical passenger vehicle. US EPA: ~404 g CO₂ per mile for an average US gasoline car. (accessed 2026-06) government