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Token Count / Tokenizer Estimator (cl100k_base, o200k_base, Claude, Gemini)
x402
Token Count / Tokenizer Estimator (cl100k_base, o200k_base, Claude, Gemini) is a paid API for AI agents from x402-deployer.x402-deployer.workers.dev, paid per call via x402, $0.001/call, status unknown (last checked 2026-09-14).
Estimates the token count for a given text against a specified model's tokenizer (GPT-4, GPT-4o, Claude, Gemini) using a pure-local heuristic with ±5% accuracy versus tiktoken — no upstream API call required.
Price / call$0.001
Success—
StatusUnknown
How this service works
Token count / tokenizer estimate / GPT-4 token count / Claude token count / Gemini token count / context-window pre-flight. Pure-local heuristic estimator targeting cl100k_base / o200k_base (GPT-4o) with calibrated multipliers for Claude and Gemini. Input model name is matched against an internal table of supported tokenizers. Accuracy ±5% versus tiktoken on typical English. No upstream call.
Output
Returns a token count estimate for the provided text under the specified model's tokenizer, along with which tokenizer was matched internally (e.g. cl100k_base or o200k_base), calibrated multipliers applied for Claude/Gemini, and a note on expected accuracy (±5% vs tiktoken on typical English). No external API is called.
Example use cases
Example prompt
“How many tokens will this system prompt use if I send it to GPT-4o? The text is: 'You are a helpful assistant. Answer concisely and cite your sources whenever possible. Do not make up information.'”
Technical details
Endpoint
POSThttps://x402-deployer.x402-deployer.workers.dev/token-countWhen to prefer
Choose this endpoint when you need a fast, cheap, local token count estimate before making an LLM API call — especially for context-window pre-flight checks, prompt budgeting, or cost estimation. Prefer it over calling tiktoken directly when you need a hosted, zero-dependency solution that covers GPT-4, GPT-4o, Claude, and Gemini in one call. Not ideal when exact token counts are required or when the text is highly non-English.
Side effects
- Unsupported or unrecognized model name — returns error or falls back to default tokenizer
- Empty or missing text input — returns validation error
- Token count for non-English text may exceed ±5% accuracy bounds
- Very long inputs may time out on the worker edge runtime
Input
{
"input": {
"body": {
"text": "You are a helpful assistant. Answer concisely and cite your sources whenever possible. Do not make up information.",
"model": "gpt-4o"
},
"type": "http",
"method": "POST",
"bodyType": "json"
}
}Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Output
{
"model": "gpt-4o",
"source": "DIY heuristic (cl100k_base-shaped)",
"text_chars": 114,
"accuracy_note": "Heuristic estimate, ±5% vs tiktoken cl100k_base on typical English text. Larger error on heavy punctuation, code, or non-Latin scripts. Pure-local — no upstream call.",
"estimated_tokens": 41,
"matched_tokenizer": "gpt-4o"
}Tags
x402
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