How LLM Token Pricing Works
API providers price Large Language Models based on tokens—the fundamental sub-word chunks used by neural tokenizers (such as Byte-Pair Encoding or SentencePiece). Pricing is structured into two separate rates:
- Input Tokens (Prompt Context): The text sent to the model, including system instructions, conversation history, and user prompts.
- Output Tokens (Completion Response): The text generated by the model in response. Output tokens typically cost 3x to 5x more per token than input tokens due to sequential autoregressive processing.
Word to Token Conversion Rules
In standard English text, 1 token is equivalent to roughly 0.75 words (or 4 characters). Conversely, 1,000 words equals approximately 1,333 tokens. Our built-in text estimator applies this rule automatically to let you estimate costs directly from raw text or draft prompts.
Frequently Asked Questions (FAQ)
How many words are in 1,000 tokens?
As a rule of thumb in English, 1,000 tokens equal approximately 750 words (roughly 0.75 words per token or 1.33 tokens per word). For code, non-English languages, or complex text, token counts can vary.
Why are output tokens more expensive than input tokens?
Output token generation requires sequential autoregressive inference, where each token generated must run through the entire neural network layers sequentially. Input prompt processing can be parallelized and KV-cached, making prompt tokens computationally cheaper to process.
Are the pricing numbers in this calculator exact?
Prices displayed are approximate rates based on official provider documentation (OpenAI, Anthropic, Google, DeepSeek) as of August 2026. Because API pricing changes frequently, users should verify current rates directly with their provider.
How do I calculate monthly LLM API costs for an app?
Multiply average input token cost plus average output token cost per request by your estimated daily active requests, then multiply by 30 days to project your monthly LLM API expenditure.