How does it estimate output tokens?

It approximates input tokens from text length, then applies a task-specific ratio — summarization shrinks output, translation stays close to one-to-one, and open-ended generation expands well beyond the input. The result is a low-to-high range, not an exact count.

Why a range instead of one number?

Output length is inherently variable; the same prompt can produce a short or long answer. A range sets realistic budget bounds rather than implying false precision.

How is the token count approximated?

It uses the common rule of thumb of roughly four characters per token for English text. That is close enough for budgeting, but real tokenization varies by model and language, so treat it as an estimate.

Are the prices current?

The model prices are illustrative output-token rates for planning. Always confirm against the provider's current pricing page before relying on a number for billing.

Does it send my prompt anywhere?

No. The estimate is computed entirely in your browser with no API calls, so you can paste private prompts safely and pay nothing to estimate.

Output Token Estimator

Output token estimator

You pay for the tokens a model generates, but you do not know the output length until after the call. This tool flips that around: it estimates the likely output token range before you send, from your prompt’s length and the kind of task, then shows the per-call output cost across a few common models so you can budget up front.

How it works

The tool approximates your prompt’s input tokens using the familiar four- characters-per-token rule, then applies a ratio tuned to the task type. Summarization compresses, so output is a fraction of input. Translation and question-answering stay closer to the input size or smaller. Open-ended generation expands well beyond the prompt. It reports a low-to-high range to reflect real variability, then multiplies the midpoint by an illustrative per-token output price for the selected model.

Tips and notes

Budget with the high end. For cost ceilings, plan against the top of the range rather than the midpoint so you are not surprised by long completions.
Cap with max_tokens. The surest way to control output cost is to set a max_tokens limit; use this estimate to choose a sensible cap.
Re-check provider pricing. The model rates here are for planning; confirm the live numbers before using them for billing.
Account for language. Non-English text often uses more tokens per character, so estimates skew low for those prompts.