Does Speaking to Agents Like Cavemen Save 65% of Tokens? We Test | Hacker News Reader

Does Speaking to Agents Like Cavemen Save 65% of Tokens? We Test

blog.jetbrains.com

46 pointsSandman41 comments

I don’t know about the rest of you, but the bulk of my token usage is not what I type in - it’s that data the AI is processing for me. I’m shocked they even got an 8% reduction.

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Why not Mandarin Chinese? Logographic is pretty compact.

Also, this really should be more precise that it’s talking about neo-caveman. Legit caveman no speak English.

problem is there are thousands of pictograms you have to know with ambiguous sounds

i think korean is the best, just learn the alphabet and you can do pretty crazy compaction by dropping honorifics and abbreviation, you can also sound out foreign words ex. 짱깨 if given the right context LLMs should have no problem understanding

> Why not Mandarin Chinese? Logographic is pretty compact […]

What do you mean? A lot of us already use Hanzi (汉字) or Kanji.

Or are you asking why non-Chinese speakers do not prompt LLMs in Mandarin?

It would take the typical English speaker many years of dedicated study to learn Mandarin Chinese to a level of fluency required to take advantage of any token savings. "Caveman speak" can be adopted instantly, by anyone (in, presumably, ~any language) with no study or time investment required.

https://en.wikipedia.org/wiki/Pidgin

Actually, when you translate Chinese literally word for word, it sounds a lot like caveman English.

你不去，我也不去

You no go, I also no go

今天这里人很多

Today here person very many

下雨就不去

Fall rain then no go

If you translate English literally (a very analytical language) to a very synthetic language (eg. Latin), it also sounds like caveman spreak

Classical latin is not very order constrained because it embeds so much grammar morphologically; it could aound very much like english or not at all like english, depending on the author's choices.

Vulgar latin does correspond to modern romance languages (and english) much more closely.

Pretty seen we will all be speaking tokenese anyways

chinese is less token efficient than english

How so? Do you mean in practice when using mostly english trained models?

Even in chinese trained models too. the amount of tokens it requires to communicate the same ideas is higher. it is just less efficient to tokenize the Chinese language because logographic languages are archaic, ridiculous, and inefficient compared to latin languages.

Heh, deepseek is known to randomly switch to Chinese output for no reason at all. As I was using Claude chat as a poor man’s reviewer, I just pasted it over and Claude interpreted it without skipping a beat and responded in English.

My dad told me he had a German math professor in college that would sometimes have to pause to think while writing out equations on the board and then turn around to a room full of confused students to ask how long he had been speaking German to know how far back in the lecture to restart in English. Again. He was known for doing it. Maybe we can anthropomorphize deepseek similarly??

Using Chinese could lead to a small amount of gains, but it's nowhere near as drastic as you'd think

Putting aside the question of whether English's larger training corpus would be a dimension of quality: content is "tokenized" before the model sees it. The "~4 English chars/token" rule isn't a strategy to optimize around because in practice, many English words become one token, the same way Chinese words do.

"I love you" and "我爱你" both tokenize down to 3 tokens. So no efficiency gains from a Chinese translation. "Telephone" is just one token, and "电话" (Chinese word for telephone that spans 2 characters) also gets condensed into one token. Sometimes there are minor gains, like "经济" being one token but "economy" being two. But it doesn't end up materializing in the gain you'd hope for by increasing meaning-per-character.

You can play around with OpenAI's tokenizer here. Great for getting particular about how to save tokens in prompt and tool definitions https://platform.openai.com/tokenizer

I cannot tell whether you were really serious but this actually could be an interesting study: same task, same procedure, only the language and writing system is different: English vs Chinese vs Korean vs ...

Since the information density is about the same in all (spoken) languages, there shouldn't be much of a difference. But maybe one writing system is better suited for LLMs.

Well 8%-10% saving without measured quality degradation is not nothing, specially now that newer models seem to use more tokens than previous ones.

Also it was tested on reasoning low, i'd have liked to have them tested on higher reasoning levels.

Ask, what is the function of grammar in language? I would argue two major functions. Forcing an encoder to marshal ideas into a coherent structure. Preserving the integrity of that structure when communicated and decoded.

Grammar sounds off? Was the message correctly understood; is there enough left to apply error correction and regenerate the intended message?

I think it's funny this works somewhat, but that's not what the goal is, IMO. The goal is to have the model do this internally in its "thinking" stage. On the very rare occasions in the past where gpt5 leaked its true internal thinking, the "CoT" was itself kinda similar. Instead of the open source "so the user wants me to... but wait... maybe I should... blahblah...", GPT5 was using internal traces like "try x.. no.. try y... no.. from x yes then z yes...". That's probably more token saving, or faster responses, if you can get the model to remain accurate.

Does anyone more familiar with the technology know if these AI models treat abugidas like Thai or abjads like Arabic differently when it comes to token usage?

Specifically abjads since as far as I am aware their difference from alphabetical and abugida scripts is that they infer the vowels instead of explicitly marking them.

I suspect this means that there's just a ~10% inefficiency in token to information mapping.

Isn’t spoken language in general and English in particular famously 50% redundant? Does that not provide the same (if not more) benefit to the model when narrowing the scope of intent?

Why don't we just use a highly specialized domain-specific language that describes the code we want generated? Natural language is too inexact and ambiguous.

You mean like… some sort of code?

As the test shows the real gain is somewhat marginal to most people, but still some ppl claim a wide skills portfolio is required to move faster or save tokens. Mixed feelings, I personally use superpowers as my basic setup - and only them.

What are your thoughts on it?

I use my local GPU. Yes, I realize it's not exactly a superpower, but it's technology that's pretty darn close to one.

Say caveman, cavemen ... unheard[0] cavewoman. No good. Bad.

When Sabrewulf[1] come, some fight, some run ... some run in cave. No good. Bad.

Token cost. Seem smart less token. But already discount. No profit.

Use less token? Price go up.

What money?

You should be bothered by the persistent & popular use of the "caveman" terminology and the myth surrounding it. However, from a dedicated amateur linguistic point of view, the "caveman language" that so many people seem to know by default is kind of remarkable. Perhaps it points to (it doesn't) some deeper, ancient understanding.

[0] this is a joke because the caveman myth comes with a lot of misogynistic baggage and you should have a problem with it. Also because I made you read this. (this is not an explanation of the joke itself, that's up to you)

[1] the sabre-tooth cat is a trope that over-represents its relationship with people. I mean it's extinct, but it was around "back then", right?

If you just want a smaller vocabulary, use French? If your goal is to communicate to an LLM, maybe saving tokens isn't the all in win, unless you like reading assert gronkHitThing(true)

French text is somewhere around 10-30% longer than the corresponding English text. I would guess much of what you save on smaller vocabulary is lost on the lengthened text.

What would matter more is the token length, depends how well your tokenizer was trained on french I guess.

uninstall caveman install ponytail

Funny enough they also tested ponytail https://blog.jetbrains.com/ai/2026/07/ponytail-skill-claude-...

gpt 4 moto razr wen?

Yes all the caveman spoke English like its freaking Flinstone. Like wtf is the caveman dilect, just english words with some randomly skipped? Why not try with actual other human languages and see if there is a token benefit in savings

Sounds like some good research. You should pursue it.

lol - so easy a caveman can do it.

Someone definitely should

Me mechanic not speak English. But he know what me mean when me say "car no go", and we best friends. So me think: why waste time, say lot word when few word do trick?

To see world

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