How does the live count work?

The meter estimates tokens locally from your text using a character-and-word heuristic (about 1 token per 4 characters), then multiplies by the model's input price. It updates instantly as you type because nothing leaves your browser.

Is the token estimate exact?

It is a close approximation, typically within a few percent. For an exact, billable count use a provider token-counting tool. The live meter is built for fast, cost-aware drafting.

Why include a completion length?

Output tokens are usually 3–5× more expensive than input, so the prompt cost alone understates a call. The completion estimate lets the meter show realistic total cost per request.

Are the prices up to date?

They are editable presets based on published list prices and clearly labelled as estimates. Confirm the current rate in your provider dashboard before budgeting.

Is my prompt sent anywhere?

No. The meter runs entirely in your browser. Nothing you type is uploaded, stored or logged.

What is the Live Prompt Cost Meter?

As you type your prompt, a real-time meter shows the running token count and dollar cost for your chosen model and estimated completion length, making cost-conscious prompt crafting effortless. It runs free in your browser on Gera Tools, with nothing uploaded.

Live Prompt Cost Meter

Name: Live Prompt Cost Meter
Creator: Gera Tools
License: https://creativecommons.org/licenses/by/4.0/

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Live prompt cost meter

Write prompts with a price tag in view. As you type, this meter shows a running token count and dollar cost for your selected model and expected completion length — so you feel the cost of a verbose system prompt before it ships to production.

How it works

The meter estimates tokens locally from your text, blending a character-based rule (approximately 1 token per 4 characters) with a word count for stability across short and long inputs. It then prices the call:

prompt_cost = (prompt_tokens / 1,000,000) x input_price
output_cost = (expected_completion / 1,000,000) x output_price
total       = prompt_cost + output_cost

Because output is priced several times higher than input on most models, the completion-length field often dominates the total — a useful reminder that capping max_tokens is often the cheapest single optimization you can make.

Why tracking cost while writing matters

The biggest prompt cost leaks are invisible until the bill arrives. Common culprits include:

Bloated system prompts — a 2,000-token system prompt paid on every call across 10,000 daily requests compounds quickly
Redundant context — including entire documents when a relevant excerpt would serve
Open-ended completions — letting the model generate 4,000 tokens when 400 is sufficient for the task

The live meter makes these visible as you draft, so you fix them before they scale.

What affects the cost most

Output tokens are priced 3 to 5 times higher than input tokens on most frontier models. A short prompt requesting a long answer costs more than a long prompt requesting a short one. For classification, extraction, or yes/no tasks, constraining the output with a direct instruction can cut per-call cost dramatically.

Tips

Trim the system prompt — it is paid on every single call; the meter makes that cost visible.
Set a realistic completion length — estimate your typical answer size so the total reflects real usage.
Verify before launch — for exact, billable counts, confirm with a provider-backed tokenizer; this meter is a fast drafting aid, not a billing guarantee.

How prompt length scales with daily cost

The cost impact of a single token in a system prompt is easy to underestimate until you multiply it out. For a system prompt that is 500 tokens longer than it needs to be, at a typical input price, across 10,000 calls per day, the monthly excess can easily reach tens of dollars or more depending on the model. The live meter makes this relationship visible: you can see the marginal cost of each paragraph you add and make an informed decision about whether it earns its place in every call.