GPT-4o mini Token Counter
Estimate token counts for GPT-4o mini — OpenAI's small, low-cost model. 128K context window. Good for high-volume / lower-stakes tasks.
What you'll use this for
A token estimate is the fastest way to know if your prompt fits and what it's likely to cost — without sending it.
Pre-flight checks
Verify a prompt fits before sending.
Cost forecasting
Pair with the cost calculator to estimate spend.
Prompt iteration
See how edits affect token count.
Context budgeting
Plan how much context to leave for output.
How to count tokens
Paste your text
Drop a prompt, document, or transcript into the left editor. Runs locally — nothing leaves the browser.
Read the token count
The breakdown panel shows estimated tokens, character / word / line counts, and the share of GPT-4o mini's context window used.
Watch the fill bar
Green under 80%, amber when crowding, red when over the limit. Trim text or split into chunks accordingly.
Copy summary or jump to cost
Copy a one-line summary, or click through to the matching cost calculator to estimate spend.
Frequently asked questions
~4 chars/token. Off by ±10–20% on real tokenizers depending on language and content. For exact counts use the provider's official tokenizer.
OpenAI's tiktoken averages around 4 characters per token for English text. Other languages and code can differ.
Yes.
No. The estimate covers content tokens only. System prompts, tool definitions, and chat scaffolding add additional tokens.
This tool gives a count, not a tokenization view. For OpenAI use the tiktokenizer playground; for other providers consult their docs.
About token counting
Modern LLMs don't process raw characters or words — they process tokens, sub-word units produced by a byte-pair encoding (BPE) tokenizer trained alongside the model. A token might be a whole word ("the"), a fragment ("token", "ization"), a single character, or even a byte. GPT-4o mini shares the o200k_base tiktoken encoding with GPT-4o.
Why estimates vary
- English prose averages ~4 characters per token.
- Code tends to use more tokens per character — punctuation and indentation each consume tokens.
- Non-Latin scripts (Chinese, Japanese, Arabic) can use 2–3× more tokens than the same idea in English.
- JSON / structured text sits between prose and code — quotes, braces and keys add overhead.
When you need exact counts
- For billing reconciliation, use the API response's
usageobject — it's authoritative. - For local pre-counting, use OpenAI's
tiktokenlibrary or the tiktokenizer web playground. - This tool is for fast estimates — pasting a prompt and seeing how it sits inside the context budget.