detecting locale...

Every model has a native tongue.
The question is whether yours matches.

See which models make your language cheaper, longer, or harder to fit in context. Different models spend tokens differently across languages.

compare language fit
# token counter chars/token fertility vs english efficiency (rtc)
detecting your language...
benchmark command

The benchmark shown here has been generated with the following command:

$ uv run mothertoken benchmark run \
--languages eng_Latn,fra_Latn,spa_Latn,por_Latn,deu_Latn,arb_Arab,cmn_Hans,jpn_Jpan,tha_Thai,hin_Deva,kor_Hang,tur_Latn,ukr_Cyrl,vie_Latn,swh_Latn \
--models gpt-4o,gpt-4,qwen3,mistral,qwen2.5,deepseek-v3,gpt-oss,gpt2,gpt-3,codex,codex-edit,opt,tinyllama,pythia,bert-base-uncased,roberta-base,xlm-roberta-base,distilbert-base-uncased \
--output src/mothertoken/data/default_benchmark.json