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%.

LayerFlow Team6 min read
LLM Token Calculator: How to Estimate and Cut Token Costs — LayerFlow blog illustration

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

  1. Count input tokens: system prompt + context + user text.
  2. Estimate output tokens from your desired answer length.
  3. Apply the model's price: $/1M input and $/1M output.
  4. 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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