AI Token Counter & Cost Estimator

Token estimation coefficients (approximate — not official figures)

These coefficients are our own heuristic approximations, not official tokenizer data from any provider. Estimation: non-CJK characters ÷ "chars per token", plus CJK characters × "tokens per char". For exact counts use each provider's official tokenizer (e.g. Anthropic's count_tokens API). The pattern they capture: the same Chinese text costs about 0.95 tokens per character on Claude versus about 0.7 on GPT / Gemini, so CJK text consumes more tokens on Claude.

How token estimation and cost work

The tool splits text locally into CJK characters and Latin words, applies per-model tokenizer coefficients to approximate token counts (Claude has no public local tokenizer, so this is an estimate), then multiplies by each model per-million input and output price for per-call and bulk cost. Character and byte counts are exact and nothing is uploaded.

• For an exact count, switch to manual input tokens and paste the usage value the model returned; the cost then becomes exact.
• When a long fixed prefix (system prompt, few-shot examples) is reused, enable cached input to gauge prompt-cache savings.

FAQ

1. Why are token counts estimates rather than exact?

Tokenizers differ per model and Claude has no public local tokenizer; Chinese and code are hardest to estimate. This tool uses a segmented heuristic with a typical error of ±10-20%. For exact numbers use the usage field the model returns or the official counting API, or enter the token count manually so the cost math is exact. Character and byte counts are exact.

2. What does the "cached input" toggle do?

Most models bill cache-hit input tokens at roughly 0.1x. Enabling it prices input at each model cached rate, so you can gauge how much a long fixed prefix saves across repeated calls.

3. My Claude / GPT API bill is far higher than expected — where is the money going?

Usually three things: (1) output tokens cost several times more than input on most models, so long responses — not long prompts — tend to dominate the bill; (2) every turn of a multi-turn conversation resends the entire history, so repeated input charges grow with conversation depth; (3) prompt caching is off, meaning a long system prompt is re-sent at full price each call when a cache hit typically costs about a tenth of that. Paste your real text here to estimate tokens and cost for one call, then multiply by your daily call volume.

4. Why do Chinese characters consume significantly more tokens than English words?

Most LLM tokenizers (like cl100k_base and o200k_base) optimize for English text. A single Chinese ideograph is typically split into 2-3 byte BPE tokens, making Chinese prompts take 1.5x to 2.5x more tokens than English prompts of equal length.

5. What are the best practices for lowering LLM API costs?

Key strategies: (1) Trim redundant System Prompts; (2) Enable Prompt Caching on long static contexts; (3) Cap `max_tokens` parameters to prevent runaway generations; (4) Summarize conversation history periodically.

6. Does counting tokens upload my private code or prompt text anywhere?

Not at all. All character counts, word counts, and token estimates happen strictly inside your local browser memory sandbox without any network traffic.