Does ChatGPT use that much more energy than a Google search?

The energy gap between a ChatGPT query and a Google search ranges from x1.5 to x3, depending on the reference point used: both remain within the same order of magnitude. The "factor of 10" claim is that typing a question into ChatGPT would be 10 times worse for the environment than searching for it on Google. It comes from a 2023 calculation that overestimated ChatGPT's energy use by a factor of 10. This article reviews the figures for the 2 services, explains the origin of the error and then looks at how query volume affects their environmental footprint.

1How much energy do ChatGPT queries and Google searches use, according to recent studies?

We start with the figures for the 2 sides and where each comes from.

The AI side: ~0.3 Wh per query, estimated then measured

A short text query to ChatGPT uses roughly 0.3 Wh. This comes from a calculation by Epoch AI, built on deliberately pessimistic assumptions, which concludes that the earlier reference figure of ~3 Wh was 10 times too high. It is corroborated by the only production measurement ever published by a major provider: Google instrumented its own infrastructure and measured 0.24 Wh for the median Gemini query (arXiv 2508.15734). The breakdown by model and by prompt length is in how much energy a ChatGPT query uses.

The search side: between 0.04 and 0.3 Wh, depending on the reference point

The only official figure ever published for a Google search dates from 2009: 0.3 Wh (0.0003 kWh, Google blog). Servers have become more efficient since then, and recent academic estimates are closer to ~0.04 Wh (Vanderbauwhede, 2023-2025); Google itself has not published an update.

Depending on the reference point chosen, the gap between AI and search therefore ranges from parity to a factor of a few. Taking the middle of the range for search (roughly 0.1 to 0.2 Wh), the gap is between x1.5 and x3, far from x10. ChatGPT and a Google search remain within the same order of magnitude, separated by only fractions of a Wh.

2Why does generating an answer with ChatGPT use a little more energy than searching with Google?

It is no mystery that AI uses a little more energy per query: searching and generating are 2 different operations. A Google search draws on an index that has already been built. The engine indexed the web in advance, and your query is only used to find the right pages in that vast table of contents, which takes little computation.

A generative AI does not retrieve anything ready-made: it builds its answer from its model. This process, known as inference (running a trained model to produce an answer), takes more computation than a search in an index, which explains the slightly higher cost per query.

It comes down to tokens

AI models process text as tokens, which are fragments of words (about 0.75 of a word in French). To answer, a model produces these tokens one by one, and every token generated uses electricity. The longer the answer, the more it uses, and the same question can use very different amounts of electricity depending on the model that handles it. AI consumption is therefore better measured per token than per query, and the choice of model matters a great deal: the ChatGPT, Claude and Gemini comparison puts figures on these gaps model by model.

Water: one published measurement for AI, none for search

Water follows the same pattern of ranges. Again, the only published measurement comes from Google: 0.26 mL per median Gemini query, equivalent to five drops, mostly used to cool the servers. For a conventional search, no per-query figure has ever been published. Here too the gap per query is negligible: the water footprint of an AI query explains where this figure comes from and why the location of the data centre matters more than the logo.

3Where does the "10 times more" figure come from?

The factor of 10 comes from an outdated calculation that kept circulating after it had been corrected. There are 3 reasons for this.

• It rests on an outdated figure. In 2023, a study put a ChatGPT query at ~3 Wh, assuming very long responses (2,000 tokens) on older-generation graphics processors (GPUs). Set against Google's 0.3 Wh, that gave "10 times more". The ~3 Wh figure has since been invalidated, but the factor kept circulating without its correction.
• Google now builds AI into search. AI Overviews (the AI-generated summaries shown at the top of results) are seen by more than 2.5 billion users a month, and AI Mode has more than 1 billion monthly users (Google, May 2026). A "Google search" therefore increasingly generates an AI summary, which involves the same type of inference as ChatGPT, so the boundary between searching and generating is fading.
• There is little transparency. The only official figure for search dates from 2009, and on the AI side only Google has published a production measurement. Old data is being compared with third-party estimates, which is all the more reason to think in orders of magnitude rather than in terms of a precise multiple.

The x10 therefore described a world that never quite existed, one in which ChatGPT used 10 times more. Recalibrated against today's figures, and as search takes on more and more AI, the gap blurs until it disappears, and the question "ChatGPT or Google?" loses some of its meaning.

4Volume matters more than any single query

In each of the 2 cases, a single query is tiny: fractions of a Wh and a fraction of a gram of CO2 (the carbon intensity depends on the country (in French) where the data centre is located: ~50-60 g/kWh in France, ~400 g/kWh in the United States). Even intensive daily use amounts to between a few hundred grams and a few kilograms of CO2e over a year: nothing comparable to a single return flight (in French). The AI carbon footprint calculator gives this annual total for your own level of use.

The environmental burden comes from multiplication. ChatGPT handles around 2.5 billion messages a day (18 billion a week in summer 2025, according to OpenAI) and Google handles about 14 billion searches a day, which adds up to more than 5 trillion a year; global token volume is projected to grow 24-fold between 2026 and 2030 (Goldman Sachs Research); and data centre electricity consumption is set to rise from 415 to ~945 TWh between 2024 and 2030, according to the International Energy Agency (IEA) in Energy and AI.

Whether a query costs 1.5 or 3 times as much as a search says nothing about this dynamic. The same logic applies to a company's digital carbon footprint and to a website's carbon footprint: an email or a page view counts for very little on its own, and it is their number, together with the hardware that serves them, that adds up.

What each query delivers also has to be taken into account. An AI query often does the work of several Google searches in one go (it summarises, rephrases and calculates), where you would otherwise have had to open 5 tabs. Comparing one AI query with a single search can therefore be misleading in both directions.

At Projet Celsius, we find it more useful to ask how many tokens are used and what value they deliver than to choose between ChatGPT and Google: do not have the AI generate long blocks of text for a trivial question, and reserve AI for tasks where it adds value. That is also what makes AI manageable in a carbon footprint, under Scope 3.

5Key takeaways

• The "x10" claim is a myth, stemming from a 2023 calculation that overestimated ChatGPT's consumption at ~3 Wh. Corrected to ~0.3 Wh, the gap with a Google search narrows to between x1.5 and x3, depending on the reference point chosen.
• The figures converge: ChatGPT ~0.3 Wh (Epoch AI), Gemini 0.24 Wh (Google measurement); Google search from ~0.04 Wh (recent estimates) to 0.3 Wh (the only official figure, 2009).
• Generating costs a little more than searching, because Google draws on a ready-made index whereas AI builds its answer token by token. The gap with search remains a small multiple, while a factor of 100 separates a short question from a long prompt on a reasoning model.
• Google is adding AI to search (AI Overviews, more than 2.5 billion users a month; AI Mode, more than 1 billion): the boundary between searching and generating is fading.
• Volume matters far more than the individual query: billions of queries a day, token volume projected to grow 24-fold by 2030, and data centre electricity consumption rising from 415 to ~945 TWh. It is therefore worth choosing the tool to suit the need.

The question to ask is therefore less how ChatGPT compares with Google than whether the use itself is worthwhile: does this use of AI bring me enough to justify its tokens? To put all this in perspective at the scale of an organisation, the overview of the carbon footprint of AI places it below 1% of an SME's carbon footprint, even with intensive use. If you need to account for it, it falls under Scope 3 of your carbon footprint (in French).

Further resources

Frequently asked questions

1Does ChatGPT use 10 times more energy than a Google search?

No, it is a myth. The factor of 10 comes from a 2023 calculation that overestimated ChatGPT's consumption at around 3 Wh per query. Current references (Epoch AI, Google's own measurement) put a short query at between 0.24 and 0.3 Wh, and a Google search at between 0.04 and 0.3 Wh depending on the reference point. The real gap is roughly 1.5 to 3 times, never a factor of 10.

2Is a single AI query bad for the environment?

Very little on its own. A short text query uses roughly 0.3 Wh and 0.26 mL of water, which amounts to a fraction of a gram of CO2 and the energy of a Google search or two. The impact comes from the aggregate volume of billions of queries and the rapid growth of the data centres that run them. For an organisation, it is this volume that goes into the carbon footprint, through the AI method under Scope 3.

3How much energy does a Google search use?

The only official figure was published by Google in 2009: about 0.3 Wh per search (0.0003 kWh). Recent estimates put it lower, at around 0.04 Wh, but Google has not published an update, so it remains a reference point rather than an exact measurement. And with AI-generated summaries now appearing at the top of results, the cost per search is more likely to rise again.

4Is it better for the planet to Google or ask ChatGPT?

The gap is much smaller than people say: roughly 1.5 to 3 times for a short query, which means fractions of a Wh in both cases. For a trivial question (a date, a definition), a conventional search remains slightly less energy-intensive. But an AI query often does the work of several searches, summarising and rephrasing in one go. The best habit is to choose the tool to suit the need, using a lightweight model by default (our comparison of AI models quantifies the gaps).

5Do Google's AI Overviews use more energy than a normal search?

Yes. When Google shows an AI-generated summary at the top of the results, it adds an inference step to the conventional search (the model builds the summary token by token). That query therefore uses more energy than a search without AI and comes closer to a ChatGPT query. This is why the boundary between "searching" and "generating" is blurring, and why the idea of a clear gap between the two no longer holds.

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