Tokens → cost
Paste a prompt and see what it actually costs: tokens, how much of the context window it eats, and the bill per request, day and month. Prices are editable presets, because provider rates change more often than any tool can keep up with.
- Handles
- prose · code · CJK · emoji
- Shows
- tokens · window · cost
- Privacy
- never sent anywhere
Your prompt
What it costs
waiting for text
What a token actually is
Not a word, not a character
~4 characters · ~0.75 words
Models split text into sub-word chunks. Common words are one token; rarer ones break
apart. unbelievable might be three tokens while the is one.
A useful rule: 750 words ≈ 1,000 tokens.
Spaces and punctuation count
every character is billed
Leading spaces are usually part of the following token, and newlines, brackets and indentation all cost. This is why minifying JSON before sending it genuinely reduces the bill.
Where the money actually goes
Output costs far more than input
Most providers charge three to five times more per output token. A 200-token question
producing a 2,000-token answer is dominated by the answer. If a bill surprises you,
check response length before prompt length — and cap it with
max_tokens.
Chat history is re-sent every turn
Models are stateless. Turn 20 of a conversation re-sends turns 1–19 as input, so cost grows roughly with the square of the conversation length. Summarise or truncate old turns rather than letting history grow forever.
The system prompt is paid for every call
A 2,000-token system prompt on a million calls a month is two billion input tokens. At $2.50/M that is $5,000 for text you wrote once. Trim it, or check whether your provider offers prompt caching.
Non-English costs more for the same meaning
Tokenisers are trained mostly on English. Chinese and Japanese approach one token per character; some Indic and African scripts fare worse still. The same sentence can cost two to three times more — a real fairness problem, not just a billing quirk.
The window holds both sides
A 128k window is not 128k of input. Prompt plus reply must fit together. Filling 120k with context leaves room for a very short answer, and most clients will silently drop your oldest messages rather than fail loudly.
Bigger is not always dearer
A small fast model that answers in one call can beat a large one that needs three attempts. Compare cost per solved task, not per token — and try the cheap model first.
Rules of thumb worth memorising
| Content | Rough cost | Notes |
|---|---|---|
English prose | ~4 chars / token | The baseline every published figure assumes. |
Source code | ~3 chars / token | Punctuation and indentation split aggressively. |
JSON | ~3 chars / token | Braces, quotes and colons are each a token. Minify before sending. |
Chinese / Japanese | ~1–1.5 chars / token | Up to four times more expensive per character than English. |
Emoji | 2–3 tokens each | Encoded as several byte-level pieces. |
1 page of text | ~500 tokens | About 375 words at normal density. |
A 300-page book | ~150,000 tokens | Fits in a 200k window — but leaves little room to answer. |
Worked scenarios
Support chatbot, 10k conversations/day
800-token system prompt + 150-token question + 300-token answer, five turns average. History re-sending roughly triples input. Around 15M input and 15M output tokens a day — on a mid-tier model, a four-figure monthly bill. Trimming the system prompt to 300 tokens saves about a third of the input cost on its own.
Document summariser, 500 PDFs/day
20-page documents are roughly 10,000 tokens each, with 500-token summaries. That is 5M input and 250k output daily. Here input dominates — the opposite of chat — so a cheaper model with a large window usually wins outright.
Code review on every pull request
A 500-line diff is around 8,000 tokens; add repository context and you are near 20,000 per review. At 100 reviews a day that is 2M input tokens daily. Send only the diff, not whole files, and the figure drops by an order of magnitude.