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An AI agent can use substantially more electricity than a single prompt because it may call a model repeatedly, generate far more tokens, and run several reasoning paths before it answers. There is no reliable universal multiplier: published estimates differ by task, model, serving conditions, and what equipment the measurement counts.
What makes an agent different from a single prompt?
A conventional request is often treated as one prompt followed by one completion. An agent works in a loop: the model chooses an action, a tool returns information, and the model runs again to interpret the result or decide what to do next. Some agents also explore multiple possible paths in parallel or through search.
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Each additional model inference requires computation. More generated tokens add work, too, and long-running requests may be served with less concurrency than ordinary requests. Tool calls themselves are not necessarily model calls, but the reasoning around them can trigger more inference.
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In a 2025 preprint, Jiin Kim, Byeongjun Shin, Jinha Chung, and Minsoo Rhu compared tool-augmented agents with a chain-of-thought (CoT) baseline on evaluated tasks. The tested ReAct, Reflexion, LATS, and LLMCompiler systems averaged 9.2 times as many LLM calls per request as CoT. LATS averaged 71 calls per request in that evaluation, as its search explored branches using separate inferences. Those are findings about the tested configurations and tasks, not a call count for every agent.
How much electricity does an AI agent use?
There is no single per-agent figure that applies across products. The estimates below illustrate different workloads and accounting methods; they should not be read as a direct, controlled comparison between providers or systems.
| Study and workload | Reported electricity estimate | What the figure represents |
|---|---|---|
| Microsoft Research, 2026: optimized frontier-scale inference | 0.31 Wh median per query; interquartile range (IQR) 0.16–0.60 Wh | A modeled estimate under the study’s production assumptions, not a universal prompt measurement. |
| Microsoft Research, 2026: test-time-scaling queries | 3.91 Wh median; IQR 2.15–7.05 Wh, about 13 times the 0.31 Wh median | The study describes these as queries about 15 times longer than typical, with roughly 5,000 output tokens. It estimates energy from workload characteristics. |
| Google, 2025: Gemini Apps text prompt | 0.24 Wh per median prompt | Google’s provider-published estimate, based on May 2025 measurement data and including more than active accelerator power. Google says it has not been independently verified. |
| Kim and colleagues, 2025: single-turn Llama 3.1 Instruct request | 0.32 Wh for 8B; 2.55 Wh for 70B | Measured baselines in the authors’ HotpotQA agent evaluation. |
| Kim and colleagues, 2025: selected Reflexion requests | 41.53 Wh for 8B; 348.41 Wh for 70B | Measured agent configurations on HotpotQA using Llama 3.1 Instruct models. |
| Kim and colleagues, 2025: selected LATS requests | 22.76 Wh for 8B; 158.48 Wh for 70B | Measured agent configurations on HotpotQA using Llama 3.1 Instruct models. |
The agent results show how repeated or branching inference can multiply energy in a particular setup. They do not establish that Reflexion, LATS, or agents generally consume those amounts in ordinary use. The 2025 paper is a preprint, and its selected configurations, benchmark task, models, and measurement setup differ from Microsoft’s modeled estimates and Google’s production estimate.
Why call count is only part of the energy cost
More tokens mean more generation
An agent may create intermediate reasoning, summarize tool results, and produce a final answer. Even if the number of calls is modest, longer inputs and outputs can increase inference work. The amount of context processed also matters, particularly when prior messages and tool results accumulate over a long task.
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Serving conditions change the result
Energy per request depends not only on how much computation a task needs but also on how the service handles it. Batching and concurrency affect how shared hardware is used; latency requirements can constrain those choices. Microsoft Research’s 2026 study attributes the increase it estimates for long reasoning and agentic queries to both increased token generation and reduced serving concurrency.
That study models multi-step and agentic workflows as multiple queries and uses output tokens to approximate energy. Its estimate does not include the additional overhead of orchestration, tool calling, or long-context management. A measured end-to-end system could therefore include work the model-based approximation leaves out.
What counts as “electricity used”?
A number can describe different parts of an AI service. Accelerator-only power is a narrower boundary than the power needed to operate and support a data-center inference service. Comparisons are meaningful only when the accounting boundary is clear.
Google’s August 2025 estimate for a median Gemini Apps text prompt was 0.24 Wh. Google says this includes actual chip utilization, provisioned idle machines, host CPU and RAM, and data-center overhead for cooling and power distribution. Under an active-TPU/GPU-only boundary, the estimate would be 0.10 Wh; Google describes that narrower figure as an optimistic, incomplete footprint. The company characterizes its figures as a point-in-time estimate, not a value for every prompt or future service behavior.
Electricity is also distinct from emissions and water use. For the same median prompt estimate, Google reported 0.03 gCO2e and 0.26 mL of water. Those are separate impact measures, not additional electricity figures.
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How to compare two energy estimates
Before comparing figures, check whether they describe the same kind of work and count the same parts of the system. TokenPowerBench, a peer-reviewed benchmark published at AAAI 2026, highlights how model choice, prompt set, inference engine, batch size, context length, parallelism, and quantization can affect results. It separates prefill—the processing of input—from decode, the generation of output, and can report GPU-, node-, and system-level power.
- Workload: Is the number for one prompt-completion, a reasoning request, a sequential tool-using agent, or a branching search?
- Inference volume: How many model calls, input and context tokens, output tokens, retries, or parallel branches are included?
- Model and hardware: Which model and inference hardware were evaluated?
- Serving conditions: Were batching, concurrency, throughput, or latency constraints part of the measurement?
- System boundary: Does the estimate cover only accelerators, or also host equipment, idle capacity, cooling, and power distribution?
- Evidence type: Is it a direct workload measurement, a modeled estimate, or a provider-reported production metric? What task, date, and system scope does it cover?
What readers can reasonably conclude
An agent can require much more electricity than one prompt when it makes more model calls, generates more tokens, searches multiple paths, or runs under less favorable serving conditions. Microsoft Research’s 2026 study estimates more than an order-of-magnitude increase for long reasoning and agentic queries under its modeled assumptions; Kim and colleagues measured much higher energy for selected agents than for their single-turn baselines in a separate HotpotQA evaluation.
Neither finding supplies a multiplier for every agent. The studies use different workloads, models, and methods, and there is no common, independently replicated end-to-end benchmark in these sources that compares diverse commercial agents with matched single-prompt baselines while accounting for tool execution, orchestration, context handling, hardware, and facility overhead. Microsoft Research also estimates an 8–20× potential energy reduction from combined model, serving, and hardware efficiency interventions; that is a study-level improvement pathway, not a guaranteed reduction for a particular service.
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