How much does a Copilot prompt consume?
A Copilot prompt most likely uses about 0.31 Wh of electricity, somewhere in a 0.16 to 0.60 Wh band. The figure comes from Microsoft, and it does not measure Copilot: it is the median of a simulation of large-scale inference, published in Joule in April 2026 (checked June 30, 2026). That distinction decides what the number is worth, and this page unpacks it.
Research verified on June 30, 2026 · Sources: Oviedo et al. : « Energy use of AI inference, efficiency pathways, and test-time scaling », Joule (avril 2026, DOI 10.1016/j.joule.2026.102430 ; préprint arXiv 2509.20241) : médiane de 0,31 Wh par requête, intervalle interquartile 0,16 à 0,60 Wh · Microsoft Cloud Blog (15 juin 2026) : « Scaling AI with 8 to 20x energy efficiency », 0,16 à 0,60 Wh et 0,0 à 0,067 mL d'eau pour une requête type · Google Cloud (21 août 2025) : impact environnemental de l'inférence IA, 0,24 Wh, 0,26 mL d'eau et 0,03 g CO₂e pour le prompt texte médian de Gemini Apps · OpenAI / Sam Altman : « The Gentle Singularity »
Where does the 0.31 Wh come from?
From a peer-reviewed paper by eight Microsoft researchers: Felipe Oviedo and co-authors, “Energy use of AI inference, efficiency pathways, and test-time scaling”, Joule, April 2026 (DOI 10.1016/j.joule.2026.102430, preprint arXiv 2509.20241 posted in September 2025). No power meter was clamped to a server. The method is a Monte Carlo simulation. The authors start from published throughput for open-weight models, in tokens per second on H100 GPU nodes, with a blended 200-billion-parameter reference model. They then vary query length and serving conditions. The output is a distribution, not a single number: median 0.31 Wh, interquartile range 0.16 to 0.60 Wh. The code released alongside the paper calls these results “directional” rather than definitive measurements, and says what they leave out: no hardware manufacturing, no full life-cycle accounting.
Why does that figure not measure Copilot?
Because Microsoft never claims it does. The Microsoft Cloud blog post of 15 June 2026 names Copilot exactly once, in its opening line, as an example of a service built on a large language model. That is an illustration, not an attribution. Copilot does not run on the open-weight models the study simulates. It runs on OpenAI models and on Microsoft's own models, whose real throughput is not public. The simulation brackets Copilot; it does not weigh it. The situation mirrors Claude, where no vendor figure exists either, except that the bracket here is far better documented.
Does it agree with Google's and OpenAI's numbers?
Yes, and that is what makes it useful. Google measured 0.24 Wh for the median text prompt in Gemini Apps on 21 August 2025, on its own fleet. OpenAI stated 0.34 Wh for an average query on 10 June 2025, with no method note. Microsoft simulates 0.31 Wh. A measurement, a claim and a simulation, produced independently, all land between 0.24 and 0.34 Wh. The 0.3 Wh this site uses for a text prompt sits within 3% of the Joule median. The authors then go further and challenge the most-quoted public estimates head on: they argue those overstate energy use by 4 to 20 times, because they assume non-production conditions, without batching and on underused hardware. The argument is technical and it holds. It also comes from a company with an interest in a low number, and this site keeps that in view.
What about water?
The same post gives 0.0 to 0.067 mL per typical query, with a median Microsoft describes as less than a single drop. The gap with Google is stark: 0.26 mL for Gemini, close to four times the top of Microsoft's range. Both figures cover water consumed on site to cool the servers, and neither counts the water withdrawn upstream to generate the electricity. The gap comes from the fleet and the climate: how much cooling is done with air, how efficiently water is used, and where the data centers sit weigh more than the model does. Comparing two water figures across vendors compares two geographies, not two AIs.
What changes with a reasoning query?
Everything, and this is the paper's most quotable result. A reasoning or agentic query raises energy use by more than an order of magnitude: it emits far more tokens and lets fewer queries run in parallel. The authors scale that up to a whole service. If just 10% of daily requests switch to reasoning mode, total energy use more than doubles. Copilot is pushing exactly those agent and reasoning modes. The 0.31 Wh median therefore describes yesterday's usage, not where the product is heading. The same authors expect the opposite pull as well: efficiency gains now landing in models, serving stacks and chips could cut energy per query by 8 to 20 times. The two forces work against each other, and nobody knows yet which one wins.
A Copilot prompt in everyday actions
No official figure exists for Copilot: these equivalents use the site's standard assumption for a text prompt (0.3 Wh, 0.3 mL, 0.2 g), not vendor data.
- 1 hour of LED light = 33 Copilot prompts
- 1 hour of TV = 233 Copilot prompts
- 1 glass of water = 833 Copilot prompts
- One 5-minute shower = 166,667 Copilot prompts
- 1 email with attachment = 95 Copilot prompts
- 1 km by car = 850 Copilot prompts
This figure says what a prompt costs, not what it returns. For a small business the real question comes next: which Copilot habits earn the time they take, and which are pure reflex. That is the ground the ghis.fr studio works on, and it publishes this calculator. Here we measure and cite, and stop there.