Bangla token counter
Paste Bangla text: see how many tokens it really uses compared with English, and what that does to your AI API bill.
On our samples, Bangla needs 5.6× the tokens of English with GPT-4’s tokenizer, and 1.4× with GPT-4o’s.
Counting runs in your browser — your text is not sent anywhere.
Bangla vs English: measured token counts
| Text | GPT-4o (o200k) Bangla / English | GPT-4 (cl100k) Bangla / English |
|---|---|---|
| Customer supportআপনার অর্ডারটি আজ বিকেলে পাঠানো হয়েছে। দুই থেকে তিন কর্মদিব… | 41 / 30 1.4× | 164 / 30 5.5× |
| Newsদেশের বিশ্ববিদ্যালয়গুলোতে কৃত্রিম বুদ্ধিমত্তা নিয়ে গবেষণা … | 38 / 26 1.5× | 164 / 26 6.3× |
| Farming adviceধানের পাতায় বাদামি দাগ দেখা দিলে প্রথমে আক্রান্ত পাতা তুলে … | 42 / 32 1.3× | 166 / 32 5.2× |
Why Bangla uses more tokens
Tokenizers are built from mostly-English text, so common English words become a single token while Bangla words are split into many small pieces — sometimes one per letter or vowel sign. More tokens means higher API bills, slower answers and less room in the model’s context window.
Running an open model (Llama, Qwen, Gemma) on your own AI Computer avoids per-token bills entirely: you pay by the minute, however many tokens you process. See what it needs with Can I run this model?
Counts are exact for OpenAI’s o200k (GPT-4o family) and cl100k (GPT-4 / GPT-3.5) tokenizers. Other models (Llama, Qwen, Gemini, Claude) use their own tokenizers, so their counts differ.
Frequently asked questions
How many tokens is a Bangla word?
It depends on the tokenizer. On our samples a Bangla text needs about 5.6× the tokens of the same English text with GPT-4 (cl100k) and about 1.4× with GPT-4o (o200k).
Is my text uploaded?
No. The tokenizer runs entirely in your browser.
Try it on a real AI Computer
JupyterLab opens in about a minute. Pay in taka with bKash, billed by the minute — stop whenever you like.