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Vextorium Token Counter
x402
Vextorium Token Counter is a paid API for AI agents from api.vextorium.com, paid per call via x402, $0.003/call, status unknown (last checked 2026-10-11).
Counts tokens in text using OpenAI tokenizers (o200k_base for GPT-4o/4.1/5, cl100k_base for GPT-4/3.5) and provides approximate token counts for Claude, Llama 3, and Gemini, plus characters-per-token ratio.
Price / call$0.003
Success—
StatusUnknown
How this service works
Count the tokens of a text with OpenAI tokenizers (o200k for GPT-4o/4.1/5, cl100k for GPT-4/3.5) plus estimates for Claude, Llama and Gemini, with characters per token. Budget LLM calls and split contexts.
Output
Returns exact token count for the requested OpenAI tokenizer, approximate token counts for Claude, Gemini, and Llama 3, word count, character count, characters-per-token ratio, and the token count for the alternate OpenAI encoding (cl100k_base), along with a note clarifying that non-OpenAI counts are estimates.
Example use cases
LLM cost budgeting before API call
“Before I send this 5000-word article to GPT-4o, can you count exactly how many tokens it uses with o200k_base and estimate what it would cost me in tokens?”
Context window fit check
“I need to know if this system prompt plus user message will fit in GPT-4's 128k context window — count the tokens using cl100k_base and tell me how much room is left.”
Cross-model token comparison
“Count the tokens in my text 'Artificial intelligence is transforming industries worldwide by automating complex tasks and enabling new capabilities.' using o200k_base and give me the approximate token counts for Claude, Gemini, and Llama 3 as well.”
Technical details
Endpoint
POSThttps://api.vextorium.com/contar-tokens-pago?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need precise OpenAI token counts (o200k or cl100k) combined with cross-model estimates for Claude, Gemini, and Llama 3 in a single call. Ideal for LLM cost budgeting, context window management, and multi-model planning without setting up tiktoken locally. Prefer this over building your own tokenizer when running in an agent or serverless environment without Python dependencies.
Side effects
- Missing 'texto' field in body returns a validation error
- Invalid 'modelo' enum value returns a schema error
- Empty string input may return zero counts
- Network timeout for very large text inputs
- Payment failure if USDC balance is insufficient for x402 micropayment
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Output
Schema · Response
{
"type": "json",
"example": {
"nota": "Recuento exacto para las codificaciones de OpenAI; el resto son estimaciones (cada modelo tokeniza distinto).",
"tokens": 53,
"palabras": 40,
"caracteres": 282,
"codificacion": "o200k_base",
"estimaciones": {
"claude_aprox": 58,
"gemini_aprox": 50,
"llama3_aprox": 54
},
"caracteres_por_token": 5.32,
"tokens_otra_codificacion": {
"cl100k_base": 56
}
}
}Tags
x402
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