OpenAI o1 Token Counter
Estimate token counts for OpenAI o1 — the reasoning model. 200K context window. Note: o1 also generates "reasoning tokens" that aren't visible but count toward output billing.
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 o1's 200K context window used. Reasoning tokens are not counted here.
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. OpenAI o1 uses the o200k_base tiktoken encoding, the same as GPT-4o.
About o1 reasoning tokens
o1 performs multi-step internal reasoning before producing visible output. Those reasoning tokens are not returned in the response but are billed as output tokens. A short prompt can generate hundreds or thousands of hidden reasoning tokens. This counter only estimates the visible input — budget extra for hidden reasoning when planning cost.
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.