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LLM Token Counter
Estimate prompt token consumption before dispatching requests to OpenAI, Anthropic, Meta, or Google models. Budget context windows accurately.
Estimated Tokens
0
for GPT-4o / ChatGPT Word Count
0
Whitespace separated Characters
0
0 no spaces Tokens / Word
0.00
Density index Model Family:
Tokenizer: OpenAI (o200k / cl100k)
Why token count matters more than word count
Context windows and API quotas are strictly bounded by tokens. When optimizing system prompts or stuffing retrieved context into LLM calls, managing your token envelope is the primary lever for cost and latency control.
Rule 1
~4 Characters / Token
In standard English prose, 1 token is roughly 4 characters or ~0.75 words.
Rule 2
Code is Denser
Code, symbols, and non-Latin characters generate significantly more tokens per character.
Rule 3
Trim Context First
Pasted files and retrieved documents make up 90% of token weight. Strip boilerplate first.