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PennyRail LLM Token Counter
x402
PennyRail LLM Token Counter is a paid API for AI agents from pennyrail.vercel.app, paid per call via x402, $0.001/call, status unknown (last checked 2026-09-14).
Counts the number of LLM tokens in a given text input using a machine-readable settlement service
Price / call$0.001
Success—
StatusUnknown
How this service works
Machine-readable settlement service
Output
Returns a token count (and possibly additional token metrics) for the provided text input, computed according to LLM tokenization rules.
Example use cases
Prompt length guard before LLM call
“Before I send this system prompt to GPT-4, count how many tokens it uses so I know if it fits within the 8k context window.”
Estimate API cost for a document
“I have this 3-page contract text I want to send to an LLM — can you count the tokens first so I can estimate how much it'll cost?”
Validate chatbot message size
“My chatbot just received a user message — count the tokens in it before I append it to the conversation history to make sure we don't exceed the limit.”
Technical details
Endpoint
POSThttps://pennyrail.vercel.app/api/p/nano/text.token-count--llm-token-countWhen to prefer
Choose this endpoint when you need a quick, paid-per-call token count without hosting your own tokenizer library, especially in serverless or agent contexts where installing tiktoken or similar is impractical. Best for low-volume spot checks or when you need a neutral third-party token count billed via USDC microtransaction.
Side effects
- Missing or malformed 'input' object returns a validation error
- Empty text input may return zero or an error
- Payment failure (402) if USDC microtransaction is not completed
- Oversized input may be rejected or time out
- Ambiguous tokenizer not specified may return counts for a default model only
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object |
Output
Schema · Response
{
"type": "object",
"additionalProperties": true
}Tags
x402
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