Token Counter

Text Input

Tokenizer:

GPT-4+ · Claude 3+ · Gemini 2+ · Llama 4 — all modern LLMs use this tokenizer

0 chars · 0 words

Token Estimate

⚡

Paste text above to count tokens

Cost Reference (input tokens)

Waiting for input…

Token Counter Hakkında

Tokens are the units LLMs work with: roughly 4 characters of English (or about 0.75 words on average), but the relationship is heavily content-dependent. Common English words may be single tokens; rare words, numbers, code, and non-English text fragment into many more tokens. The same paragraph in English vs Turkish can have 1.5–2× different token counts.

This tool counts tokens for the major tokenizer families (cl100k_base / o200k_base for recent OpenAI and Anthropic models; tiktoken-compatible). It shows the breakdown — token count, character count, ratio, and rough cost estimate at typical pricing. Useful for prompt-engineering work, context-window planning, and cost forecasting.

Counts are approximate when crossing tokenizer families. For exact accounting, use the provider's own tokenizer at request time. For planning, the tool is within 5–10% of actual production counts in most cases.

Token sayısı ne zaman önemli

Context-window planning. A model with 200k context window can hold roughly 150k English words or 100k mixed-content tokens once you include the system prompt, history, and headroom for output. Use the counter to size what fits.

Cost forecasting. Multiply average input + output tokens by request volume by per-token price. The counter does the first step; the rate calculator does the rest.

Multilingual cost surprise. A Turkish-language application costs 1.5–2× more in tokens than the equivalent English application. Budget accordingly when localizing.

Token tahmin tuzakları

Assuming 4 chars/token universally. Holds for English prose; far off for code, numbers, URLs, JSON, or non-English text.

Forgetting system prompt and history. They count too. A multi-turn chat consumes tokens from every previous message.

Ignoring output tokens. Output is the expensive direction (3–5× input price for most models).

Sık sorulan sorular

Why are tokens not the same as words?

Tokenizers learn statistical patterns over a training corpus. Common substrings become single tokens regardless of word boundary; rare ones split into multiple tokens. The result is more space-efficient than per-word.

Which tokenizer does this use?

We support OpenAI's cl100k_base and o200k_base by default (used by GPT-4 family and Anthropic's models, approximately). Different providers may differ slightly.

Are token counts consistent across providers?

No — each provider has its own tokenizer. Counts can differ by 5–20% between providers for the same input.

Does whitespace count?

Yes — leading/trailing whitespace and multiple spaces often become tokens. Trim consistently if you want predictable counts.

About the Token Counter

Token Counter Hakkında

Where token counts matter

Token sayısı ne zaman önemli

Token estimation pitfalls

Token tahmin tuzakları

Frequently asked questions

Sık sorulan sorular