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AI usage report · July 2025 to June 2026

Sample. The data is fictional; the format is what the delivered report looks like.

Claude usage footprint

1.82 t

Methodological range: 1.14 t to 2.84 t of CO₂e.

of CO₂e over the period, for 12,573 Mtok of processed tokens.

That is roughly 15,199 km by car and 492 showers.

Volumes measured server-side by Anthropic.

Spend

71,982 €

At Anthropic's public prices, cache included. Spend doubles as the internal reference: the same token volumes drive both cost and footprint. Your actual invoice may differ if you have negotiated pricing; the footprint only depends on volumes.

Energy
4.9 MWh
Water
29.5 m³
Cache reads
106,350 Mtok

89% of the period's tokens are cache re-reads, counted at the reduced factor published in the methodology.

Model mix

CO₂e per month and per model family. The mix is your main lever: for the same usage, model choice moves both cost and footprint.

ModelInputOutputCostCO₂eConfidence
Claude Sonnet8,670 Mtok694 Mtok59,533 €1.41 tdirect measurement or calibrated decomposition
Claude Opus633 Mtok51 Mtok7,530 €210.4 kgdirect measurement or calibrated decomposition
Claude Haiku2,278 Mtok247 Mtok4,920 €205.7 kgdirect measurement or calibrated decomposition

57% of these tokens go through Claude Code, the rest through the direct API.

Intensity

Footprint relative to output rather than raw volume: the reading that lets you compare two periods or two teams without penalising the one that ships more.

active developers / month
33
CO₂e per dev per month
4.7 kg
lines added
936,000
CO₂e per 1,000 lines
1.9 kg
commits
10,920
sessions
8,580

Scope: these counters come from Claude Code analytics (no one is named, only totals are kept). Direct API usage is included in the footprint but not in the output counters.

Routing decisions worth studying

Two volume shifts to the model family one step down, priced at constant volume over the period. Orders of magnitude to support a decision, not commitments.

Route half of the Opus volume to Sonnet

Long orchestration tasks stay on Opus. The rest moves to Sonnet, one capability step down. Computed at constant volume over the period.

CO₂e
-48.9 kg
Cost
-1,322 €

Route a fifth of the Sonnet volume to Haiku

Short reviews, rewrites, extraction: Haiku's capability is enough there. Computed at constant volume over the period.

CO₂e
-131.1 kg
Cost
-7,151 €

Month by month

MonthTokensCostEnergyCO₂eWater
July 2025547 Mtok3,309 €222.8 kWh82.7 kg1.3 m³
August 2025602 Mtok3,637 €244.4 kWh90.7 kg1.5 m³
September 2025800 Mtok4,824 €324.2 kWh120.3 kg1.9 m³
October 2025945 Mtok5,725 €384.5 kWh142.7 kg2.3 m³
November 20251,039 Mtok6,289 €421.3 kWh156.4 kg2.5 m³
December 2025892 Mtok5,378 €360.1 kWh133.7 kg2.2 m³
January 20261,133 Mtok6,869 €460.4 kWh170.9 kg2.8 m³
February 20261,171 Mtok6,576 €451.2 kWh167.5 kg2.7 m³
March 20261,262 Mtok6,905 €478.9 kWh177.8 kg2.9 m³
April 20261,329 Mtok7,172 €500.0 kWh185.6 kg3.0 m³
May 20261,396 Mtok7,495 €522.4 kWh193.9 kg3.1 m³
June 20261,458 Mtok7,805 €543.6 kWh201.8 kg3.3 m³
Total12,573 Mtok71,982 €4.9 MWh1.82 t29.5 m³

Scope and exclusions

Measurement source
Token volumes come from the Anthropic Admin API, which measures them server-side, model by model. Nothing is estimated client-side, nothing is sampled.
Claude Code seats
Claude Code seat usage (subscriptions) was queried and is included in this measurement, alongside API usage.
Cache treatment
Cache writes count as full input, for energy and for price. Cache reads count at 8% of the energy of an input token (the memory re-read residual documented in the methodology) and at the public price of 0.1x input. Tokens read from cache are shown separately, outside the processed-tokens counter.
Period boundaries
The period covers July 2025 to June 2026, in calendar months: from the first day of the first month at 00:00 UTC to the first day of the month after the end, exclusive. Every boundary is set in UTC, never in local time.
Factor coverage
100% of the period's tokens are covered by published emission factors: every gram shown traces back to a documented model.
What this document is
This report measures the footprint of a usage: Claude model inference, datacenter share. It is not an organisational GHG assessment: end-user devices, networks and model training remain out of scope, as documented in the methodology.

Methodology annex

Token volumes are measured server-side by Anthropic, not estimated. Each model is converted to energy, CO₂e and water using published, versioned factors, then the infrastructure parameters below apply. Full equations live at tokenclimate.com/methodology.

PUE1.14
Grid carbon intensity (CIF)0.287 kgCO₂e/kWh
On-site water (WUE)0.18 L/kWh
Off-site water (EWIF)5.11 L/kWh
Amortised manufacturing44 gCO₂e/kWh

Uncertainty range

The total lies between 1.14 t and 2.84 t of CO₂e according to the published ranges of the methodology (factors tokenclimate-v3-2026-07): cache read factor 0.05 to 0.15, grid carbon intensity ±30% depending on location, amortised manufacturing 22 to 66 gCO₂e/kWh. Parameters without a published range stay at their central value.

Conversions and equivalences

Costs are converted from Anthropic's public USD prices into euros at the ECB reference rate of 22 June 2026: 1 USD = €0.8729. Equivalences: 120 gCO₂e per kilometre for an average European passenger car (ADEME factor, well-to-wheel) and a 60-litre shower (ADEME).

Wording ready to paste into your responses and reports

“Over the period July 2025 to June 2026, Claude (Anthropic) usage amounts to 1.82 t of CO₂e for 12,573 Mtok of processed tokens (cache excluded). Volumes are measured server-side by the provider. Conversion follows the public TokenClimate methodology (factors tokenclimate-v3-2026-07, tokenclimate.com/methodology), versioned and sourced.”

Glossary

Mtok
One million tokens, the volume unit used throughout this document.
Input / output tokens
Input is the text sent to the model (prompt, context), output the text it generates. Output costs far more energy per token than input.
Cache
Reuse of context that was already processed. A write stores the context, a read reuses it at reduced cost and energy.
CO₂e
Carbon dioxide equivalent: the unit that aggregates greenhouse gases by their warming potential.
Wh / kWh / MWh
Watt-hour and its multiples. Electrical energy consumed by the inference servers, before the PUE is applied.
T1 / T2 / T3
Source tiers of the parameters: T1 measurement or published primary report, T2 documented derivation from T1 sources, T3 engineering estimate.

References

  • Jegham N., Abdelatti M., Hendawi A., "How Hungry is AI? Benchmarking Energy, Water, and Carbon Footprint of LLM Inference", arXiv:2505.09598, v6, 2025.
  • Mistral AI, "Our contribution to a global environmental standard for AI", 2025.
  • Google, "Measuring the Environmental Impact of Delivering AI at Google Scale", arXiv:2508.15734, 2025.
  • Luccioni A. S., Jernite Y., Strubell E., "Power Hungry Processing: Watts Driving the Cost of AI Deployment?", ACM FAccT 2024.
  • "EcoLogits: Evaluating the Environmental Impacts of Generative AI", Journal of Open Source Software, 2025, doi:10.21105/joss.07471.
  • NVIDIA, "Product Carbon Footprint Summary: HGX H100", 2024; Boavizta, BoaviztAPI.
  • AFNOR, SPEC 2314, "Référentiel général pour l'IA frugale", 2024.
  • Llopis P., arXiv:2606.10660, 2026.
Generated on 7 July 2026 · Computed, not written by an AI: same inputs, same numbers, reproducible from the public methodology.

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