How can output length be predicted before generation?

It cannot be predicted exactly, but task type is a strong signal: summaries compress input, Q&A produces short answers, code and essays expand. The tool combines the task profile with cues in your prompt (explicit length requests, input size) to estimate a realistic range rather than a single number.

Why not just use max_tokens for budgeting?

max_tokens is a ceiling, not a forecast. Budgeting at the ceiling overstates cost — models usually stop well short. Estimating the likely length gives a far more accurate cost projection while still letting you set a safety cap.

How accurate is the estimate?

It is a heuristic that captures the typical shape of each task type and adjusts for explicit length instructions in your prompt. Real output varies with the model and content, so treat the range as a planning aid and measure your actual completions to refine it.

Is my prompt sent anywhere?

No. The prediction runs entirely in your browser and your prompt never leaves the page.

Completion Length Predictor

Completion length predictor

Output tokens usually cost more than input tokens, and they are the part you cannot see until after you pay for them. Always budgeting at max_tokens overstates your bill; ignoring output entirely understates it. This predictor reads your prompt and task type to estimate a realistic output length and cost, so your projections sit close to reality.

How it works

Each task type carries a characteristic output profile: summaries shrink the input, Q&A answers are short, code and long-form writing expand well beyond the prompt. The tool starts from the task profile, then adjusts using signals in your prompt — input length, and any explicit length instruction like “in one paragraph” or “list ten items.” It returns a low, expected, and high token estimate and prices the expected completion against your model’s output rate. Everything runs in your browser.

Tips and notes

The single biggest accuracy gain is telling the model how long to be — “answer in two sentences” both shortens output and makes it predictable. Use the high estimate to set a sensible max_tokens cap and the expected estimate for cost budgeting; they serve different purposes. Output cost often dominates total cost on generative tasks, so this estimate matters more than input counting for chat and code workloads. Measure your real completions over a few hundred calls and adjust your assumptions — nothing beats your own data for refining the forecast.