How we calculate this

Methodology

Every environmental number on ragornot — in the Benchmark impact section, the decision tool, and the digest — comes from the cited coefficients below. This page is the whole recipe: the figures, their sources, the formulas, and the caveats.

Read this first: these are estimates, not measurements

Every derived figure is an order-of-magnitude estimate. It tells you the shape of a tradeoff, not a certified measurement.
ragornot itself runs Claude Haiku on Amazon Bedrock over a small demo corpus. The frontier-model energy and water figures below are literature-derived proxies applied to the modes— they are not direct measurements of ragornot's own Bedrock calls. We use published figures because they're the most defensible numbers available, and we'd rather show them honestly than invent our own.
Water is always split into scope-1 (on-site cooling only) and full-scope (including the water used to generate the electricity). We never conflate the two.
No cross-user averages. Anything session-based (the self-consumption meter) is labelled as a this-session estimate that resets on refresh.

The order-of-magnitude that matters

Retrieval ≈ 0.3 Wh vs long-context ≈ 40 Wh — ~100× the energy

Retrieval sends the model only what's relevant, so each answer stays near a short-query energy budgetsource: Epoch AI, "How much energy does ChatGPT use?" (Feb 2025) (opens in a new tab). Attaching a large document to every prompt pays the full long-context tax on every callsource: Epoch AI, "How much energy does ChatGPT use?" (Feb 2025) (opens in a new tab). Same answer, ~100× the energy — the whole reason to ask “RAG or not?” before reaching for a giant context window. Order-of-magnitude estimate, not a measurement.

Energy per query

Electricity per query for modern models, from Epoch AI's analysis. A short chat query is the baseline a retrieval-grounded answer looks like; long-context stuffing is the expensive alternative.

Figure	Value	What it means	Source
Typical short chat query (GPT-4o-class)	0.1–0.6 Wh	Varies with response length. This is the baseline a retrieval-grounded answer looks like.	Epoch AI, "How much energy does ChatGPT use?" (Feb 2025)
Reasoning-heavy query (o3-class)	3.9 Wh	Extended chain-of-thought / test-time compute pushes energy up ~10× over a short chat query.	Epoch AI, "How much energy does ChatGPT use?" (Feb 2025)
Long-context query (~100k-token document stuffed into context)	40 Wh	Attaching a large document to every query is the expensive alternative to retrieving only what's relevant.	Epoch AI, "How much energy does ChatGPT use?" (Feb 2025)

Water per query

Scope-1 counts only the water evaporated by on-site data-center cooling — the figure vendors disclose. Full-scope adds the water consumed generating the electricity, which is roughly 4× larger. The Benchmark charts show full-scope and say so; here are both.

Figure	Value	What it means	Source
OpenAI, per query — on-site coolingscope-1 (on-site cooling only)	0.32 mL	Water evaporated by data-center cooling for the compute — excludes water used to generate the electricity.	OpenAI (vendor-disclosed)
Google Gemini, median per query — on-site coolingscope-1 (on-site cooling only)	0.26 mL	Vendor median for a text prompt; on-site cooling only.	Google (vendor-disclosed, median)
GPT-4o short query — full scopefull-scope (incl. electricity generation)	1.2 mL	Adds the water consumed generating the electricity to the on-site cooling water — roughly ~4× the scope-1 figure.	Jegham et al., "How Hungry is AI?" (arXiv, May 2025)
Estimated global AI water use, 2025full-scope, global aggregate	312–764 billion L	Data-center cooling is ~30–40% of a data center's energy draw.	de Vries, Patterns (Dec 2025)

Carbon

Carbon is computed from energy and the carbon intensity of the grid, which you can change in the Benchmark impact section. The assumption is always shown, never buried.

gCO₂ = energy(kWh) × grid intensity (gCO₂/kWh)

Figure	Value	What it means	Source
Grid carbon intensity (configurable)	50–480 gCO₂/kWh	CO₂ = energy(kWh) × grid intensity. The default is US-ish; the global average is higher (~480).	Ember / IEA grid-intensity averages

Grid preset	gCO₂/kWh	Note
US grid (default)	400	Roughly the US average carbon intensity.
Global average	480	Global average is higher than the US.
EU average	250	More renewables and nuclear than the US average.
Low-carbon region	50	Hydro / nuclear-heavy grids (e.g. Nordics, Québec).

RAG vs long-context (cost & tokens)

The energy contrast has a cost twin: stuffing documents into every prompt bills for those tokens on every call, while retrieval pays only for the slice it fetches.

Figure	Value	What it means	Source
Long-context is this much more expensive than RAG	20–24 ×	Stuffing documents into every prompt bills for those tokens on every call; RAG pays only for the retrieved slice.	arXiv, "The Token Tax" (2026)
RAG is this much cheaper in tokens for typical workloads	8–82 ×	Range depends on corpus size and query pattern; retrieval keeps the prompt small.	RAG 2025 review (RAGFlow)

The exact formulas

We translate a query's token count into energy — more defensible than deriving from dollar cost — anchored to the Epoch short-query figure. Water and CO₂ then follow from that energy, so every number moves together.

# anchor: a typical short query ≈ 500 tokens ≈ 0.3 Wh

energy_Wh_per_1k_tokens = 0.3 / (500 / 1000) = 0.6 Wh

energy_Wh = (total_tokens / 1000) × 0.6

co2_g = (energy_Wh / 1000) × grid_gCO2_per_kWh

water_full = (energy_Wh / 0.3) × 1.2 mL # full-scope

water_scope1= (energy_Wh / 0.3) × 0.32 mL # on-site cooling

# Flat / Hierarchical make no LLM call → ~0 marginal energy, water, CO₂

# Live LLM/RAG runs derive energy from the run's own token cost, same anchor

Everyday equivalents

The car-km and phone-charge comparisons are rough conversion factors for intuition only — not a claim about any specific vehicle or phone.

Figure	Value	What it means	Source
Passenger car tailpipe emissions	200 gCO₂/km	Rough conversion factor for intuition, not a claim about any specific vehicle.	EPA / EEA typical passenger-car average
Smartphone full charge	8 gCO₂	Rough conversion factor for intuition only.	EPA equivalencies (approx.)

All sources

Typical short chat query (GPT-4o-class) — Epoch AI, "How much energy does ChatGPT use?" (Feb 2025)
Reasoning-heavy query (o3-class) — Epoch AI, "How much energy does ChatGPT use?" (Feb 2025)
Long-context query (~100k-token document stuffed into context) — Epoch AI, "How much energy does ChatGPT use?" (Feb 2025)
OpenAI, per query — on-site cooling — OpenAI (vendor-disclosed)
Google Gemini, median per query — on-site cooling — Google (vendor-disclosed, median)
GPT-4o short query — full scope — Jegham et al., "How Hungry is AI?" (arXiv, May 2025)
Estimated global AI water use, 2025 — de Vries, Patterns (Dec 2025)
Grid carbon intensity (configurable) — Ember / IEA grid-intensity averages
Long-context is this much more expensive than RAG — arXiv, "The Token Tax" (2026)
RAG is this much cheaper in tokens for typical workloads — RAG 2025 review (RAGFlow)
Passenger car tailpipe emissions — EPA / EEA typical passenger-car average
Smartphone full charge — EPA equivalencies (approx.)

Spotted an error or a better source? ragornot is open source — open an issue.