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...

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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.

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gavinsyancey 3 hours ago | parent | prev [-]

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