Words ↔ Tokens Calculator
Estimate LLM tokens from words, characters or pages — per-language ratios for English, code, Hindi, Chinese.
Ratios reflect cl100k/Llama-3-class BPE tokenizers. Hindi's ~2.9 tokens/word (vs English 1.33) is the 'tokenizer tax' — the same prompt costs Indian-language users 2×+ the API spend; newer 128K+ vocabs narrow the gap.
Formula
About Words ↔ Tokens Calculator
Every API bill, context-limit error and RAG chunk size traces back to one estimate: how many tokens is this text? This calculator converts words, characters or pages using measured ratios per content type — English's famous four-characters-per-token, code's whitespace-heavy 2.4×, and the steep multipliers for Hindi and other underrepresented scripts that effectively price non-English users out of the same context window. Use it for budgeting prompts, sizing chunks, or explaining to finance why the Hindi chatbot costs double.
How to use Words ↔ Tokens Calculator
- 1Enter your values into Words ↔ Tokens Calculator — sensible, domain-typical defaults are pre-filled so you see a real result immediately.
- 2The result recomputes live using the formula shown on the page; there is no button to press.
- 3Adjust any input to compare scenarios, then read the worked example to see the substituted numbers.
Why use Words ↔ Tokens Calculator?
- ✓Computes Words ↔ Tokens instantly in your browser — no sign-up, no upload, no server round-trip.
- ✓100% free and unlimited, with the exact formula shown: tokens ≈ words × ratio(content) — English ≈ 1.33 (the famous '¾ words per token'), code/JSON/Hindi cost far more.
- ✓Runs entirely client-side, so every value you enter stays private on your device.
- ✓Live recompute as you type, with a worked example and authoritative references for trust.
Frequently asked questions
Why is the English rule of thumb 0.75 words per token?+
BPE tokenizers allocate their vocabulary by frequency, and English dominated training corpora — common words get whole tokens, rarer ones split into 2–3 pieces. Averaged over prose, ~1.33 tokens per word (4 characters per token) has held across GPT/Llama-class tokenizers.
Why does code tokenize so expensively?+
Indentation, brackets, operators and camelCase identifiers fragment heavily: each newline+indent run, each symbol is often its own token. Real-world Python averages ~2–2.5 tokens per 'word'. This is also why code models benefit from whitespace-aware tokenizers.
How accurate are these estimates?+
Within ±15% for typical text of each class — good enough for budgeting and chunking. For billing-critical exactness, run the actual tokenizer (tiktoken, HF tokenizers); ratios also drift slightly between tokenizer generations (Llama-3's 128K vocab is ~10% more efficient than cl100k on multilingual text).
What's the tokenizer tax on Indian languages?+
Older 32–50K vocabs encode Devanagari nearly byte-by-byte: 3–5× the tokens of equivalent English, meaning higher cost, slower generation and less effective context. Newer 100K+ multilingual vocabs (GPT-4o, Llama 3, Qwen) cut this to ~1.8–2.9× — better, not yet fair.
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