Token Counter & Estimator

Tokens are the fundamental unit of LLM pricing. Every API call consumes tokens — both for the input you send and the output the model generates. Understanding how words translate to tokens, and how tokens translate to costs, is essential for budgeting any AI application. This calculator helps you model the full pipeline: from words to tokens to daily and monthly costs, across different models and usage patterns.

Words to Tokens: The Conversion Factor

For standard English text, one word averages approximately 1.3 tokens. This ratio varies by content type: conversational text averages 1.2, technical documentation 1.4, source code 1.5-2.0, and non-Latin scripts (Chinese, Japanese, Korean) can use 2-3 tokens per word or character. The tokenizer splits words into sub-word units — common words like "the" are single tokens, while rare or compound words get split into multiple tokens. Understanding your specific tokens-per-word ratio is key to accurate cost projections.

Input vs Output Token Economics

LLM APIs charge separately for input tokens (your prompt) and output tokens (the model's response). Output tokens typically cost 3-5x more than input tokens because generating each output token requires sequential computation — each new token depends on all previous ones. This pricing structure means that response length is one of the most powerful cost levers. A prompt that generates a 500-token response costs significantly less than one generating a 2000-token response, even with the same input. To plan your AI content strategy efficiently, Semrush helps you identify high-value content topics to focus your AI resources on the highest-impact use cases.

Practical Cost Optimization

The most effective cost optimizations target the largest line items. First, audit your prompts — most can be shortened 30-50% by removing redundant instructions and compressing context. Second, set max_tokens on every API call to prevent unnecessarily long responses. Third, implement prompt caching for system prompts that repeat across queries — many providers offer significant discounts for cached input tokens. Fourth, use structured output formats (JSON, XML) to reduce boilerplate in responses. Finally, route simple queries to budget models — not every query needs a flagship model. For data-driven content planning that maximizes token efficiency, try Semrush's keyword and content tools to focus AI spend on the topics that drive the most value.