Token Counter for Prompts and Agent Outputs
Count tokens before you send a prompt. Use this token counter to estimate context size, trim prompts, and reduce OpenClaw model costs.
Quick orientation
When to use this tool
Use the token counter when prompt size is turning into a guess. It gives you a cleaner way to inspect how much context you are actually sending before that prompt becomes a cost or latency problem.
- 1
Before shipping a long system prompt or multi-message workflow
- 2
When a model starts truncating, slowing down, or getting expensive
- 3
When you want to compare prompt drafts and keep only the useful context
Stats
Use the output well
What to look at after the count
The token total matters, but the follow-up decision matters more. These are the numbers that usually tell you whether a prompt is safe to ship as-is.
Prompt weight
0 tokens
Use this number to sanity-check whether the prompt is compact enough for repeated use.
Context pressure
0.0%
If this climbs too high, retrieved context or instructions usually need trimming before output quality drops.
Input-only cost
Free
Helpful for rough budgeting before you even estimate response size or chained tool calls.
Why token counting matters
A prompt can feel concise and still be expensive once memory, tool results, and safety instructions are added. Token counting makes that hidden weight visible.
It is especially useful when you are tuning prompts for assistants that run often or across many users.
- Trim repeated instructions before they multiply across every request
- Check whether retrieved context is helping or just inflating cost
- Spot the difference between a workable prompt and an overstuffed one
Common mistakes
What usually bloats a prompt
Most token problems are not caused by one giant paragraph. They come from small pieces of extra context that keep stacking up.
Related tools
Keep the decision moving
Most tool decisions connect to a second task right away. These are the next pages worth opening if you want fewer surprises later.
API Cost Calculator
Estimate daily, monthly, and yearly OpenClaw API costs before you lock in a model stack.
Regex Tester
Test pattern matching and capture groups live before you rely on regex inside prompts, parsers, or automations.
JSON Validator
Catch malformed JSON and schema mismatches before they break an automation, integration, or deploy.
Learn next
Turn the estimate into a better setup
If the tool solved the immediate question, this is the next place to go for the broader workflow, tradeoffs, and implementation detail.
Learn how to choose and route modelsFAQ
Does a lower token count always mean a better prompt?
No. The goal is not to make prompts tiny. The goal is to remove waste while keeping the context a model genuinely needs.
Should I check tokens before or after prompt testing?
Both. Count early to avoid obvious excess, then count again after testing because the useful version often grows in ways you did not expect.