What you are testing for is fundamentally different than character level text manipulation.
A major optimization in modern LLMs is tokenization. This optimization is based on the assumption that we do not care about character level details, so we can combine adjacent characters into tokens, then train and run the main AI model on smaller strings built out of a much larger dictionary of tokens. Given this architecture, it is impressive that AIs can perform character level operations at all. They essentially need to reverse engineer the tokenization process.
However, morphemes are semantically meaningful, so a quality tokenizer will tokenize at the morpheme level, instead of the word level. [0]. This is of particuarly obvious importance in Japanese, as the lack of spaces between words means that the naive "tokenize on whitespace" approach is simply not possible.
We can explore the tokenizer of various models here: https://huggingface.co/spaces/Xenova/the-tokenizer-playgroun...
Looking at the words in your example, we see the tokenization of the Gemma model (closely related to Gemini) is:
un-belie-vably
dec-entral-ization
bio-degradable
mis-understanding
anti-dis-establishment-arian-ism
пере-писы-ваться
pere-pis-y-vat-'-s-ya
до-сто-примеча-тельность
do-stop-rime-chat-el-'-nost-'
пре-по-дава-тель-ница
бе-зо-т-вет-ственности
bezotvetstvennosti
же-лез-нодоро-жный
z-hele-zn-odoro-zh-ny-y
食べ-させ-られた-くな-かった
tab-es-aser-are-tak-unak-atta)
図書館
tos-ho-kan
情報-技術
j-ō-h-ō- gij-utsu
国際-関係
kok-us-ai- kan-kei
面白-くな-さ-そうだ
Further, the training data that is likely to be relevent in this type of query probably isolates the individual morphemes while talking about a bunch of words that the use them; so it is a much shorter path for the AI to associate these close but not quite morphene tokens with the actual sequence of tokens that corresponds to what we think of as a morphene.
[0] Morpheme level tokenization is itself a non-trivial problem. However, has been pretty well solved long before the current generation of AI.