You have to read about half of the article before it mentions this, which should have been in the first paragraph:

> The researchers found the energy and accuracy disparity by measuring the amount of energy used by three open-source AI models, Alibaba’s Qwen 3, Google’s Gemma 3, and Meta’s Llama 3.1.

▲

fxwin 6 hours ago | parent | next [-]

I feel like that info is placed appropriately. The abstract mentions that energy disparity persists across models, the rest follows the standard Intro -> Related Work -> Experiments structure

Edit: sorry, i went straight to the paper. I agree, the article itself (and the headline) are bad (as they tend to be on this topic)

▲

wongarsu 6 hours ago | parent | prev | next [-]

Is the publishing cycle this long? Those models were relevant 18 months ago

Working out the kinks in your methodology obviously takes some time. But is there any structural reason not to rerun the benchmarks on current models in the final stages before submission? Assuming the results generalize to those newer models

▲

raddan 33 minutes ago | parent | next [-]

It's challenging to keep running experiments. In a perfect world, we would run our experiments again and update the results. But some experiments are expensive, and some experiments are inherently difficult to reproduce. I have a paper nearing completion that involves interaction between trained (human) professionals and a model. It was a logistical challenge to bring those people together (also, we had to pay them), and the roleplaying that we asked LLMs to do can't be done anymore--they refuse for safety reasons. Stuff like this is pretty common.

I am hopeful that we can eventually move to open weights models, but at the time we did the experiments, the open weights models did not have the capabilities we needed. The ever-shifting sands of frontier models makes reproducibility difficult, and not just for scientists.

▲

armcat 6 hours ago | parent | prev [-]

Unfortunately it is this long. I know people in academia studying human-AI interfaces and up until recently they were still using gpt-3.5.

	▲	mh- an hour ago \| parent [-]
		Yes and let's all remember that lag when the studies about AI outcomes are posted.

▲

curt15 5 hours ago | parent | prev [-]

How would one evaluate the energy consumption of models accessible only through Anthropic or OAI APIs? You can't fire them up on your own computers and measure power consumption.

▲

rhdunn 4 hours ago | parent | next [-]

The Anthropic and OAI APIs report token usage (input, output, and in some cases reasoning/thinking). The input tokens consist of the system prompt, the user input/messages, and any other context data.

Given the same model the only variable is the token count. The more tokens, the more passes through the LLM (to generate the next output token), the more energy used.

It's well known that non-English text consumes more tokens [1], [2] and thus more energy. This is due to two factors:

1. Latin script languages having different character pairings to English and modified characters, including language features like modifying verbs for person/tense/etc. (e.g. Spanish);

2. Non-Latin scripts that take more bytes to represent in UTF-8 and (esp. for ideographic and syllabic languages like Chinese, Japanese, and Korean) have lower common character pair sequences, meaning shorter tokens for those scripts and thus more tokens for a given text sequence.

[1] https://ikriv.com/blog/?p=5322 "LLM tokens and foreign languages" (5 Aug 2025)

[2] https://medium.com/@programmerraja/how-your-llm-costs-5x-mor...

▲

vova_hn2 3 hours ago | parent | next [-]

> token usage

It is not exactly accurate, because it ignores speculative decoding which can often save a lot of time, compute and energy.

See this branch: https://news.ycombinator.com/item?id=50441444#50497854

	▲	rhdunn 2 hours ago \| parent [-]
		Speculative decoding will be model/software based and apply to all languages. The time, compute, and energy will be less compared to other models, but for the same model, hardware, and software used my reasoning still applies -- comparing English to other languages (unless optimized by the tokenizer) then English will consume less tokens and thus less energy. That is, it is still a function of the tokens used.

▲

gavinsyancey 3 hours ago | parent | prev [-]

So basically, the tokenizer is optimized for English and performs worse on other languages?

▲

simonw 4 hours ago | parent | prev | next [-]

Look at the token usage counts.

▲

yolo1897 5 hours ago | parent | prev [-]

It's explained too..