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?

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.

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

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)

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

gpt 4 moto razr wen?

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.

https://groups.google.com/g/alt.nerd.obsessive/c/EGuKIN_4dME...

  alt.nerd.obsessive FAQ v1.4

  In the episode where they were filming the Radioactive Man movie [1995], the comic book store guy tells Bart that he can find out the star of the RM movie. He promptly posts a message to alt.nerd.obsessive which states "Need know star RM pic". This information is relayed through the nerd world until it reaches a nerd hiding under the table at a meeting of movie moguls casting the film. He relays back the answer immediately (Rainier Wolfcastle).

https://en.wikipedia.org/wiki/Radioactive_Man_(The_Simpsons_...

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.

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.