LLM Token Calculator

Each tokenizer family builds its vocabulary differently, so the same sentence splits into a different number of chunks depending on which one processes it. This calculator approximates that with a fixed characters-per-token ratio per tokenizer — 4.0 for GPT-4's cl100k_base down to 3.2 for Llama's SentencePiece — so switching the tokenizer at a fixed character count directly rescales the estimated input tokens; a smaller ratio means more tokens for the same text.

The calculator only takes one price input (per 1M input tokens) and multiplies it by 3 to approximate the output rate, reflecting that most commercial LLM APIs do charge noticeably more for generated tokens than for tokens they merely read. It's a simplification, not a lookup of your specific provider's real output price — if your provider's output-to-input price ratio differs from 3x, the outputCost figure here will be off by that same factor.

It divides your estimated total tokens per request (input plus output) by a fixed 128,000-token context window and expresses that as a percentage — it does not check your actual model's context limit, which may be smaller (like 8K or 32K) or larger. Use it as a rough proxy for how close a single request is to hitting a typical modern context ceiling, and adjust manually if your model's real window differs from 128K.

The ratio scales output tokens directly off input tokens, so it should reflect how much your task typically expands or shrinks text: around 0.3 for summarization (output much shorter than input), close to 1 for question-answering, and 2 or higher for open-ended generation where the model writes substantially more than it reads. Getting this wrong skews both outputCost and totalTokensPerRequest since output tokens are derived entirely from this multiplier rather than measured independently.