Tokens Per Dollar Calculator
See how many LLM tokens a dollar buys at a given per-million-token price, plus how many tokens your monthly budget covers — the fastest way to compare model pricing.
How it works
Tokens per dollar = 1,000,000 ÷ price per 1M · Budget tokens = (budget ÷ price) × 1,000,000
Model pricing is quoted per million tokens, which makes two models hard to compare at a glance. Flipping it to tokens per dollar puts every model on one scale: a $3/M model gives about 333,000 tokens per dollar, a $15/M model only 67,000. Because input and output tokens are usually priced differently — output often 3–5× input — run this once for each and weight by your real input:output ratio to get an honest cost per request.
Worked example
A model costs $3 per million tokens. One dollar buys 1,000,000 ÷ 3 ≈ 333,333 tokens, 1,000 tokens cost $0.003, and a $100 monthly budget covers (100 ÷ 3) × 1,000,000 ≈ 33.3 million tokens.
Frequently asked questions
What is a token?
A token is a chunk of text a model processes — roughly 4 characters or about ¾ of a word in English. So 1,000 tokens is about 750 words; 1 million tokens is a few good-sized novels.
Why price input and output separately?
Providers almost always charge more for output (generated) tokens than input (prompt) tokens, often 3–5× more. Enter each price here separately, then blend them by your typical prompt-to-response ratio for a true per-request cost.
How do I estimate tokens from words?
Multiply your word count by about 1.33 to approximate tokens for English text. Code, other languages and heavy punctuation tokenize differently, so treat it as a ballpark.
Related calculators
Educational estimate — real costs and memory depend on your provider, framework, model architecture and pricing, which change often. Verify against your own bills and profiling before committing budget.