Due to Byte-Pair Encoding (BPE) character splits, Arabic text generates 2.1× more tokens than equivalent English text. 1,000 Arabic words consume ~2,799 tokens in Gemini 2.0 Flash-Lite.
Relative to English baseline (1.0×).
Price to send 1,000 words of Arabic text into context.
Discounted price for repeated Arabic system context.
| Word Count Scale | Arabic Tokens | Arabic Input Cost | English Equivalent Cost | Tokenization Penalty |
|---|---|---|---|---|
| 1,000 words (Short Article) | 2,799 | $0.00021 | $0.00010 | +$0.00011 |
| 10,000 words (Whitepaper / Report) | 27,993 | $0.002099 | $0.001 | +$0.0011 |
| 50,000 words (Book / Corpus) | 139,965 | $0.0105 | $0.004999 | +$0.005499 |
| 100,000 words (Enterprise Repository) | 279,930 | $0.021 | $0.009998 | +$0.011 |
Short Arabic sentences often generate more tokens than long English sentences.
Strip non-essential tashkeel (diacritics) from input context where semantic disambiguation is not required to save 20-30% tokens.
Most LLM tokenizers are primarily trained on English-heavy web datasets. Non-Latin characters in Arabic (Arabic Abjad) split across multiple Byte-Pair Encoding (BPE) sub-word or multi-byte UTF-8 tokens, requiring approximately 2.1× more tokens to encode the exact same semantic meaning as English.
Strip non-essential tashkeel (diacritics) from input context where semantic disambiguation is not required to save 20-30% tokens. In addition, enabling prompt caching on static Arabic instructions or documentation saves 75–90% on input token rates.
Yes. Gemini 2.0 Flash-Lite has strong multilingual comprehension and generation capabilities in Arabic (العربية). The difference is purely computational and financial due to sub-word token splits.