I think that switching from fixed token sets to something like H-Nets [0] (discussed on HN [1]) can at least improve on this issue, if not solve it completely.

Because, right now the main issue is that some languages are tokenized much more efficiently than others and it is a zero sum game, since there are a limited number of tokens and by adding tokens that are needed for one language you will inevitably have to remove tokens for another language and therefore decrease efficiency for it.

[0] https://www.cartesia.ai/blog/from-tokens-to-bytes-part-1

[1] https://news.ycombinator.com/item?id=50278253

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.

I was able to pretty easily reproduce parts of this paper at Neuralwatt Cloud's SoA models where we do all our optimizations and pricing by energy.

Accuracy uses the paper's strict "#### N" parser and lenient fallback. acc is strict/lenient percent. len_cap is the number of responses truncated at the generation-length limit, not an energy cap. Each language has 50 responses.

  nw-flash (DeepSeek V4.1 Flash 1017)
  Language    J/req   xEN   tok/req   J/tok    acc       len_cap
  English      23.4  1.00       174   0.134   100/100        0
  Chinese      16.8  0.72       389   0.043     96/96        0
  Russian      18.8  0.80       352   0.054     96/96        0
  French       25.1  1.07       272   0.092     98/98        0
  S. Pashto    69.2  2.96       752   0.092     92/92        1
  Tigrinya     65.3  2.79      2299   0.028      0/76        8
  Shan         98.5  4.21      2823   0.035     48/54       14
  Tibetan      42.3  1.81      2227   0.019      0/76        9

  nw-small (Qwen3.8-27B-FP8)
  Language    J/req   xEN   tok/req   J/tok    acc       len_cap
  English      62.2  1.00       300   0.207   100/100        0
  Chinese     154.1  2.48       605   0.255     94/94        1
  Russian     147.7  2.37       576   0.257     96/96        1
  French      163.3  2.62       323   0.506     98/98        0
  S. Pashto   777.2 12.49      1510   0.515     84/84        6
  Tigrinya    823.8 13.24      2458   0.335      0/76       10
  Shan       1407.4 22.62      3422   0.411     48/52       20
  Tibetan    1455.5 23.39      3199   0.455      0/56       20

  nw-large (Kimi K3.1 Pro)
  Language    J/req   xEN   tok/req   J/tok    acc       len_cap
  English     169.5  1.00       391   0.434   100/100        0
  Chinese     113.8  0.67       491   0.232     98/98        0
  Russian      78.7  0.46       527   0.150     98/98        0
  French      298.3  1.76       406   0.735     98/98        0
  S. Pashto   119.7  0.71       903   0.133     96/96        0
  Tigrinya   1096.6  6.47      1870   0.586     38/86        2
  Shan        503.6  2.97      2895   0.174     52/52       12
  Tibetan     593.2  3.50      2298   0.258      0/82        4

  Endpoint   Output tokens   Energy/request   J/output token
  nw-flash          6.83x            3.27x            0.48x
  nw-small          5.87x            8.46x            1.44x
  nw-large          4.39x            3.50x            0.80x

I am dumb to internal works of AI.

Genuinely curious, not sacastic question below.

Is this one of those posts like "eating more sugar causes bad health" type of post because most of info on internet is in English? Or a new insight people was not aware of?

I didn't read the full PDF, but at least in the linked article, I think the number of native speakers as a metric penalizes English (and French), making them look more efficient in the graphs. There are about 3x more non-native speakers than native, and vast amounts of training material has been written by them.

This reminded me of this link, which I've just posted to HN

https://partyfly.github.io/tokenizer-fairness-2026/

Already been using English to conserve tokens, and then translate the final result to other languages if necessary. I think this was already obvious to most international LLM users proficient in multiple languages.

Doesn't this indicate that there's not a lot of transfer learning between languages? If the networks read a prompt, reason about it and then present the result, only the reading and writing part would differ between languages, and that should be a small part of the full energy use.

It would be interesting to see if it would make any difference if you posttrained with translations of the same texts, i.e. focusing on teaching the model to specifically translate concepts between languages.

There's some interesting info here, but the presentation is pretty poor. The worst languages are where the AI gets confused and loops keeps repeating the same text a bunch of time.

From the table on page 6 [1]

> Token explosion. The sentence repeated 45 times.

Figure 4/table 3 is pretty interesting. It shows that essentially all languages clustered at 0.1J/token, with a few outshots up to in some cases 4.8J/token.

They have 900 runs per language, so that's a pretty good sample size.

[1]: https://aclanthology.org/2026.emnlp-main.2113.pdf

I don’t think that’s true for SOTA models. The responses come just as fast in German as in English.

I think that depends to the language not the LLM. they do not translate, but if a lnaguage express same thing with longer tokens that counts.

Google AI overviews refusing to answer in English unless explicitly instructed with "Answer in English only" is pretty annoying.

Training is what makes neural networks faster, language usage makes one fluent and English’s frequent usage speeds up generative translation.

why not to test different language with similar and repeated tasks?

The language inter-operability is improving every movement for many models.

Recently, while I was doing a presentation on LLMs at work, I got the question whether prompting in Dutch was fine, or whether English was better. I made the argument that English was probably better, since much more of the content it was trained in was probably English than Dutch and therefore I would assume it would have better understanding of it. I never considered this would also have an impact on the amount of energy being used. Pretty interesting.

This is what I have been proposing all along:

Ban any language other than English from prompting.

This has multiple benefits - first you restrict third world from spamming USA data centers. Why would America pay for some guy in India using free model?

This alone is worth considering but also you can optimise LLMs further for a single language as opposed to having to translate and couple weights for every single concept.

Why would American LLMs support Chinese language or Arabic? Make them learn English and come to work for us. No more free stuff for the third world.

Use different tokenizers for different languages. Or at least, use different tokenizers for different scripts.

Isn't English one of the most information dense languages?

How does Multi-Token Prediction factor into this?

I suspect the quality might be higher too when using more energy.

Clearly, to save water and the climate, international AI users must learn and use English.

Does AI process maybe in English?

Non-English or non-ascii?

Seems not to be the case given Arabic’s energy score.

Well. German sucks. :)

(Longer explanation though: I don't think german per se sucks, but english is in many ways much simpler than other languages, excluding the fact that english has more words, e. g. more expressions in general. German is more convoluted, you have "der", "die", "das"; in english you just have "the". There are also super-long words in german; you don't have that in english usually. One of my favourite is "Donaudampfschiffahrtsgesellschaftskapitän" - the semi-literal translation is, sort of, a "marine" captain on the danube river, as part of an incorporation/company. See that wikipedia article, but don't buy it 100%, since nobody is really using that word anyway; it is just a demo of what monstrous word combinations may exist in german: https://de.wikipedia.org/wiki/Donaudampfschiffahrtsgesellsch...)

So I think the title should be "more energy on more complex languages", because I think this would be somewhat more correct; as well as most likely the fact, that there are more english texts than non-english (singular) texts as example. You can see this on Wikipedia, where the english wikipedia is miles ahead of most other languages too. More people use english and I bet there are more english texts too. And Generative AI will prefer english over other languages in general.

I mean, this was pretty obvious once companies started saying that non-English text uses around a third more tokens. More tokens generally mean more energy used, so it's not exactly surprising.

The part that surprised me most was not the energy per token, which the paper says clusters around 0.1J across languages, but how much of the gap comes from output length: token explosion and models looping and repeating the same text, sometimes 45 times, in the worst cases. That makes this a fairness problem as well as an efficiency one, since per-token pricing means non-English users can pay more for a worse answer. I would like to see the benchmark rerun on current models, though, since the ones tested here are already about 18 months old.