I actually tested this with GPT-4o's tokenizer, and the result was the opposite — Korean keywords average 2-3 tokens vs 1 for English. A fibonacci program in Han takes 88 tokens vs 54 in Python.

The reason comes down to how LLM tokenizers work. They use BPE (Byte Pair Encoding), which starts with raw bytes and repeatedly merges the most frequent pairs into single tokens. Since training data is predominantly English, words like `function` and `return` appear billions of times and get merged into single tokens.

Korean text appears far less frequently, so the tokenizer doesn't learn to merge Hangul syllables — it falls back to splitting each character into 2-3 byte-level tokens instead.

It's a tokenizer training bias, not a property of Hangul itself. If a tokenizer were trained on a Korean-heavy corpus, `함수` could absolutely become a single token too.

So no efficiency benefit today. But it was a fun exploration, and Korean speakers can read the code like natural language. It could also be a fun way for people learning Korean to practice reading Hangul in a different context — every keyword is a real Korean word with meaning.

The main benefit of Korean actually comes from the fact that the language itself fits perfectly into a standard 27 alphabet keys and laid out in such a way that lets you type ridiculously fast. The consonant letters are always situated in the left half and the vowels are in the right half of the keyboard. This means it is extremely easy to train muscle memory because you’re mostly alternating keystrokes on your left hand and right hand.

Anecdotally I feel like when I’m typing in English, each half of my brain needs to coordinate more compared to when I’m typing in Korean, the right brain only need to remember the consonant positions for my left hand and my left brain only need to remember the vowel positions.

One distinction though: Han uses actual Korean words, not transliterations. 함수 means "function" in Korean, 만약 means "if" — they're real words Korean speakers already know.

Your example uses transliterations like 펀크 and 아래이 which would look odd to a Korean reader. That difference matters for readability.

Error messages, REPL, LSP hover docs are all in Korean. You can't get that from #define 만약 if.