Resource
Estimate Token Usage Before You Run It
Know how many tokens a job will consume before you send it — input plus an assumed response, costed per call and at scale.
Reduce Token Usage to Cut Cost
Measure first, then trim. This counts a padded, over-polite prompt so you can see the tokens the filler is costing — before you cut it.
Overview
You cannot cut what you have not measured. This loads a deliberately verbose, please-and-thank-you prompt and counts it, so the cost of padding is a number rather than a feeling. Measurement is this tool's job; the actual trimming — removing redundancy and noise without changing meaning — is the Prompt Cleaner's. Used together, the loop is tight: count here, clean there, count again, and watch the per-1,000-calls figure drop.
How to use this resource
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Count the padded prompt
See what the filler and politeness are costing in tokens.
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Trim with the Cleaner
Remove redundancy and noise without changing meaning.
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Re-count
Measure again and watch the per-1,000-calls figure fall.
Why This Works
- You cannot reduce what you have not measured — counting comes first
- The cost line makes padding a number, not a hunch
- Counting here and trimming in the Cleaner closes a tight loop
Best for
- Quantifying the cost of verbose prompts
- High-volume jobs where every token scales
- Pairing measurement with the Prompt Cleaner
Not for
- Doing the trimming itself — that's the Prompt Cleaner
- Restructuring a messy prompt — that's the Prompt Formatter
FAQ
How do I see what padding and politeness cost in my prompt
The report turns padding into a number: it counts the verbose prompt at roughly 176 tokens (range 158-193) and shows a per-1,000-calls cost line where token spend becomes real. Token Counter produces this browser estimate; the figures are character-based estimates, not tokenizer output, so treat them as approximate and verify pricing before relying on a number.
Does the token counter trim my prompt or just measure it
Only measures. Its job is counting; the actual trimming, removing redundancy and noise without changing meaning, is the Prompt Cleaner's. The loop is count here, clean there, count again, and watch the per-1,000-calls figure drop. It also isn't a fit check, the report notes for "will it fit?" the Context Window Estimator is the right tool.
Are the per-model token counts here exact for billing
No, they're estimates. The report shows the same text as roughly GPT-5 176, Claude Opus and Sonnet 184, Gemini Pro 176, but notes these are character-based estimates that tokenize differently per provider, which is why it gives a range rather than a single number. Pricing is approximate as of June 2026, so verify current rates before budgeting.
More resources from Token Counter
Resource
Token Counter for AI Prompts
Paste a prompt, get an honest token estimate — a range, not a fake-precise number — plus the cost across GPT, Claude, and Gemini.
Resource
Calculate AI API Cost for a Prompt
Turn a prompt into a dollar figure: input cost, output cost, combined per call, and the number that actually matters — cost per 1,000 calls.
Resources that pair well
Resource
Estimate Token Budget — Plan Before You Paste
Token budget planning for real workloads: how much of the window a transcript actually consumes, what is left for the answer, and how much headroom remains.
Resource
Message Too Long — the Fix That Doesn't Butcher Content
The "message too long" error has a structural fix: split at paragraph boundaries into sequenced chunks with wait rules, instead of pasting fragments and hoping.
Resource
Prompt Cleanup Examples (Before & After)
A set of before-and-after examples showing exactly what prompt cleanup removes — and what it deliberately leaves alone.
Related tools
Tool
Token Counter
Estimate how many tokens a prompt is and what it costs — honest ranges across GPT, Claude, and Gemini, with per-call and per-1,000-call pricing.
Workflows that use this resource
Workflow
AI Cost Optimization Workflow
Cut what an AI feature costs without dumbing it down — price the prompt as it runs today, see where the tokens go, trim the waste, and re-measure to prove the saving holds at scale.
Guides for this resource
Guide
How to count tokens in a prompt before you send it
Counting a prompt's tokens before you send it tells you whether it fits the model, what it will cost, and whether the end might get cut off. Here's how to check and trim.