LLM Token Calculator: How to Estimate and Cut Token Costs
How to estimate LLM token costs: token-per-word ratios, a practical token calculator workflow, and ways to cut token spend by 30-60%.
Estimating LLM token usage before you call an API prevents budget surprises and helps you size prompts. A rough token calculator is simple: English text runs about 4 characters per token, or roughly 1.3-1.5 tokens per word.
The rule of thumb
- ~4 characters per token for English.
- ~1.3-1.5 tokens per word.
- ~100 tokens ≈ 75 words.
- Code and JSON tokenize denser than prose.
Calculating cost
- Count input tokens: system prompt + context + user text.
- Estimate output tokens from your desired answer length.
- Apply the model's price: $/1M input and $/1M output.
- Total = (input/1M × in-price) + (output/1M × out-price).
Worked example
Hidden token eaters
- Re-sending the same context on every call instead of caching.
- Huge system prompts restated per request.
- Output token ceilings set too high — models overgenerate.
- Agent loops that accumulate full conversation history.
Cutting token costs
- Trim context to only what the task needs.
- Use prompt caching for stable prefixes.
- Lower max output tokens.
- Route simple tasks to cheaper models.
- Summarize conversation history in long agents.
FAQ
How many tokens is 1,000 words?+
About 1,300-1,500 tokens, since English averages roughly 1.3-1.5 tokens per word.
Do token calculators work for code?+
Approximately. Code and JSON tokenize denser; measure real usage with provider tokenizers for accuracy.
What is the fastest way to cut token costs?+
Trim context, cache stable prefixes, and route simple requests to cheaper models. These three cut spend fastest.
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