> 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.
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)
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
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...