Count the tokens of a text with OpenAI tokenizers (o200k for GPT-4o/4.1/5, cl100
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.
Answeringour last check, 2026-10-04
1 of 1checks answered this week
315 msmedian answer time
$0.003listed price per call
$0.003price it asked us
Paid test badge: not yet. The checks above are free: we call the tool without paying and read the payment request it sends back. The Verified badge needs paid calls whose answers match the promised output, and nobody can buy a badge.
Endpoint
POST https://api.vextorium.com/v1/llm-token-count
| Category | Market data |
|---|---|
| Provider host | api.vextorium.com |
| Networks | eip155:10, eip155:5042, eip155:8453, solana:5eykt4UsFv8P8NJdTREpY1vzqKqZKvdp |
| Payment schemes | exact |
| Self-reported calls, 30 days | 1 from 1 payers (the provider's figure, not ours) |
Our checks, last 30 days
| Day | Result | HTTP | Asked | Time |
|---|---|---|---|---|
| 2026-10-04 | valid payment request | 402 | $0.003 | 315 ms |
Example input (from the provider)
{
"body": {
"text": "Artificial intelligence agents are transforming the digital economy. AI agents pay for data with micropayments. The x402 protocol lets agents pay per call without accounts. Many companies already offer pay-per-use APIs for agents. USDC micropayments make this business model viable."
},
"bodyType": "json",
"method": "POST",
"type": "http"
}
Promised output schema (from the provider)
{
"$schema": "https://json-schema.org/draft/2020-12/schema",
"properties": {
"input": {
"additionalProperties": false,
"properties": {
"body": {
"properties": {
"model": {
"enum": [
"o200k_base",
"cl100k_base"
],
"type": "string"
},
"text": {
"type": "string"
}
},
"required": [
"text"
]
},
"bodyType": {
"enum": [
"json",
"form-data",
"text"
],
"type": "string"
},
"method": {
"enum": [
"POST"
],
"type": "string"
},
"type": {
"const": "http",
"type": "string"
}
},
"required": [
"type",
"method",
"bodyType",
"body"
],
"type": "object"
},
"output": {
"properties": {
"example": {
"properties": {
"characters": {
"type": [
"number",
"null"
]
},
"characters_per_token": {
"type": [
"number",
"null"
]
},
"encoding": {
"type": [
"string",
"null"
]
},
"estimates": {
"properties": {
"claude_approx": {
"type": [
"number",
"null"
]
},
"gemini_approx": {
"type": [
"number",
"null"
]
},
"llama3_approx": {
"type": [
"number",
"null"
]
}
},
"type": [
"object",
"null"
]
},
"note": {
"type": [
"string",
"null"
]
},
"tokens": {
"type": [
"number",
"null"
]
},
"tokens_other_encoding": {
"properties": {
"cl100k_base": {
"type": [
"number",
"null"
]
}
},
"type": [
"object",
"null"
]
},
"words": {
"type": [
"number",
"null"
]
}
},
"type": [
"object",
"null"
]
},
"type": {
"type": "string"
}
},
"required": [
"type"
],
"type": "object"
}
},
"required": [
"input"
],
"type": "object"
}