Token Counting and LLM API Pricing

Models do not read characters or words directly. A tokenizer splits text into tokens, which are common character sequences learned from training data. A common rule of thumb for English is about four characters, or three-quarters of a word, per token. That puts 1,000 words at roughly 1,300 to 1,400 tokens. Code, numbers, unusual names and many non-English languages use more tokens per word. Each model family has its own tokenizer, so the same text can produce different counts with different providers. Prices are usually quoted per million tokens, with separate input and output rates. Output tokens cost more because the model generates them one at a time, while it can process input tokens in parallel. Output is commonly priced at several times the input rate, often somewhere between four and eight times, though ratios vary by model. Here is a worked example with made-up prices: input at $3 per million tokens and output at $15 per million. A task sends 6,000 input tokens (system prompt, instructions and a document) and gets back 800 output tokens. - Input: 6,000 x $3 / 1,000,000 = $0.018 - Output: 800 x $15 / 1,000,000 = $0.012 - Total: $0.03 per task At 10,000 tasks a day, that comes to about $300 a day. Real bills often surprise people, for predictable reasons: - Chat applications resend the whole conversation on every turn, so input grows with each message. - Reasoning models generate internal thinking tokens that are billed as output, even when they are not shown in full. - Tool definitions, retrieved documents and images all count as input. - Retries and failed calls cost money too. Three misconceptions are common: that tokens equal words, that filling a model's context window is free, and that the model with the lowest per-token price is always cheapest per task. A stronger model that finishes in fewer attempts can cost less overall.