☀️ AI Morning Minute: Token Efficiency
Using AI at scale isn’t just a technical problem. It’s a math problem. And tokens are the unit of currency.
Every time you send a message to an AI, you’re spending tokens. Every word the model sends back costs tokens too. For a single conversation, that’s invisible. For a company running millions of queries a month, token costs can exceed what they spent on software licenses. Token efficiency is the practice of getting the same result with fewer tokens, and it’s becoming one of the most important skills in AI product development.
What it means
Token efficiency is how much useful output an AI produces relative to how many tokens (the chunks of text, roughly three-quarters of a word each, that AI models use to read and write) it consumes to get there. An efficient prompt gets a complete, accurate answer in 200 output tokens. An inefficient one gets the same answer after 800. That gap is real money.
Flagship models currently charge $2 to $3 per million input tokens and $10 to $15 per million output tokens, a four to five times multiplier on outputs. A customer support bot handling a million conversations a month at current rates can cost over $3,000 monthly just in tokens, and that’s before any infrastructure costs.
Why it matters
Wasted tokens hit your bill and your speed. Output tokens are generated one at a time, so longer responses mean slower responses. A prompt that asks the model to explain its reasoning when you only need the answer is paying for words you don’t want and waiting longer to get them.
Prompt design directly controls token spend. Asking “Could you please provide me with a comprehensive overview of my scheduled appointments for today?” costs about 18 tokens. Asking “What’s on my calendar today?” costs 8. Same intent, half the tokens. At a million requests a day, that difference is not small.
As reasoning models become more common, token efficiency gets more complex. A reasoning model might use thousands of internal tokens to think through a problem before giving you its answer. That thinking costs money too, even if you never see it. Knowing when to use a reasoning model versus a standard model is now part of managing token spend.
Simple example
A developer building a chatbot sets the system prompt to 800 words of detailed instructions. Fine in testing. But in production with 500,000 users a day, that prompt runs before every single message. Cut it to 200 words and you’ve trimmed 600 tokens from every request.
At standard pricing, that’s thousands of dollars a month saved without touching the model or the product.