Every AI feature uses electricity when it runs, but there is no single energy price for “one AI query.” A short text prompt, a long reasoning task and a generated video can require very different amounts. The most useful estimates specify the task, date and parts of the serving system counted; totals for data centers, meanwhile, show what happens as those requests multiply.
How much energy does one AI query use?
It depends on what the query asks the system to do and how its energy use is measured. Two recent estimates illustrate the range, but they are not provider-versus-provider scores: Google measured a median text-generation prompt in Gemini Apps, while Microsoft Research modeled frontier-scale model workloads on an H100 node.
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| Estimate | What it describes | Evidence and scope |
|---|---|---|
| 0.24 watt-hours (Wh) | Median Gemini Apps text-generation prompt | Google’s May 2025 production analysis, using its comprehensive serving-system boundary; the company says the results are not independently verified and do not represent every prompt. Google’s explanation and associated paper. |
| 0.34 Wh median; 0.18–0.67 Wh interquartile range | Query to a frontier-scale model with more than 200 billion parameters | Microsoft Research’s 2025 modeled estimate under stated H100-node, workload, GPU-utilization and power-usage-effectiveness assumptions—not a measurement of a named consumer feature. Microsoft Research. |
The figures differ in task, system boundary and evidence type, so comparing them as if they were measured under identical conditions would be misleading. “One query” is not a standardized unit: it might mean a short text answer, an extended exchange, a request that uses extra reasoning, or a multi-step agent workflow.
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The workload matters. Generating more tokens or performing additional computation takes more resources; handling images or video and carrying out multi-step actions can also be much more demanding than producing a simple text response. The International Energy Agency (IEA) says video generation, reasoning and agentic tasks can use hundreds or thousands of times more energy per query than simple text generation. That is a broad comparison, not a universal energy table for every feature or provider. IEA, Key Questions on Energy and AI (2026).
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Reasoning can change the workload substantially
In Microsoft Research’s modeled test-time-scaling scenario, median energy rises from 0.34 Wh in its baseline to 4.32 Wh when the workload uses 15 times more tokens. This is a specific modeled case, not a claim that every “reasoning” toggle or feature uses that amount. Request length, model, hardware and serving conditions all affect the result.
What the estimate counts matters, too
A calculation limited to an accelerator can produce a smaller figure than one that includes the broader serving system. Google’s May 2025 paper reports 0.24 Wh per median Gemini Apps prompt under its comprehensive method, compared with 0.10 Wh using a narrower method on the same product. In the comprehensive accounting, Google attributes 0.14 Wh (58%) to active accelerator power, 0.06 Wh (25%) to host CPU and DRAM, 0.02 Wh (10%) to provisioned idle machines and 0.02 Wh (8%) to data-center overhead. Those are Google’s reported components for that analysis, not a standard breakdown for other systems.
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Other factors include the number of tokens processed, batching, hardware and how heavily it is used. A useful estimate should therefore identify its workload and boundary—not merely give a watt-hour figure without context.
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Efficiency per task can improve even while total electricity use rises. Google reports that the median Gemini Apps text prompt’s energy use fell 33-fold between May 2024 and May 2025, and its calculated carbon footprint fell 44-fold over the same period. These are company-reported changes for that product and task, not a trend that can be assumed for every AI service. Google also says its May 2025 results are point-in-time estimates, are not indicative of future performance and have not been independently verified.
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The IEA says energy use per AI task has fallen by at least an order of magnitude annually in recent years, citing advances in hardware and software. It describes the pace as “unprecedented in energy history,” while warning that newer, more energy-intensive uses—including video generation, reasoning and agentic tasks—are increasingly being launched and used.
Why do individual prompt estimates not tell the whole story?
A small amount of energy per request can add up when services handle many requests. The IEA’s 2026 report says global data-center electricity demand grew 17% in 2025, while consumption from AI-focused data centers grew 50%. It puts total data-center consumption at 485 terawatt-hours (TWh) in 2025 and projects 950 TWh in 2030, around 3% of global electricity demand. The 2030 figure is a projection, and the total includes data centers serving both AI and non-AI workloads.
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- REAL-TIME ENERGY DATA: REQUIRES 2.4 GHz WIFI WITH AN INTERNET CONNECTION to monitor energy use with iPhone / Android / Web app. Vue sensors collect energy data and are accurate from ±2%. The Vue is UL and CE Listed for your safety. 1 second data is only available in the app (when actively open) and retained 3 hours. Minute and hour data are retained in the cloud. 1 minute data is retained 7 days, 1 hour data is retained indefinitely. Export cloud data whenever you want in the app.
Earlier IEA figures should not be mixed with those as if they were one continuous observation series: its 2025 report estimated that data centers used 415 TWh, or around 1.5% of global electricity, in 2024, and projected about 945 TWh by 2030. These figures also cover all data-center workloads, not AI alone. IEA, Energy and AI (2025).
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Query mix can also change a deployment’s daily total. In Microsoft Research’s modeled scenario, serving one billion queries per day uses an estimated 0.8 gigawatt-hours (GWh) per day in the baseline case. If 10% of those requests are long, the estimate rises to 1.8 GWh per day; targeted efficiency interventions in that modeled scenario reduce it to 0.9 GWh per day. These are illustrative model outputs, not measurements of a particular service. They show why the share of demanding requests can matter as much as the energy cost of a typical prompt.
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How does electricity use translate into emissions and water?
Electricity consumption is not itself an emissions figure. Converting it to carbon emissions requires an electricity carbon-intensity factor, which varies with the grid and the accounting period. Google reports 0.03 grams of carbon-dioxide equivalent (gCO2e) for its median Gemini Apps text prompt, calculated using the company’s 2024 average fleet-wide grid carbon intensity—not a direct measurement of each prompt’s local emissions. Its 0.26 millilitres of water per median prompt is likewise an estimate using Google’s 2024 average fleet-wide water-usage effectiveness, rather than a measurement of water consumed at a specific location for that request. Google’s methodology and results.
For wider context, the IEA estimates that data-center electricity consumption produced around 180 million tonnes (Mt) of indirect CO2 emissions in 2024. That figure covers all data-center workloads, not AI alone, and excludes emissions from backup power generation. IEA, Energy and AI (2025).
What to check when you see an energy estimate
- Task: Is it a short text response, long-context request, reasoning run, agent workflow, image or video generation?
- Boundary: Does the number cover only an accelerator, or also host CPU and memory, idle capacity and data-center overhead?
- Workload conditions: Are token count, batching, utilization and hardware specified? Is this a production measurement or an idealized benchmark?
- Evidence and date: Is it a provider disclosure, independent measurement, modeled estimate or projection—and when was it made?
- For emissions or water: Which grid-intensity or water-use factor was applied, and for what geography and time period?
Those details determine whether two numbers can sensibly be compared. A household plug-in meter cannot isolate the cloud electricity used by a particular AI request; it measures power at the device, not a provider’s remote serving system.
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