LLM Token Counter

Agent Invocation

REST API

curl "https://aidevhub.io/api/token-counter/?text=Hello+world"

const res = await fetch("https://aidevhub.io/api/token-counter//api/token-counter/", {
  method: "GET",
  headers: { "Content-Type": "application/json" },
  body: JSON.stringify({
  "text": "Hello, world!"
}),
});
const data = await res.json();
console.log(data);

import requests

res = requests.post(
    "https://aidevhub.io/api/token-counter//api/token-counter/",
    json={"text":"Hello, world!"},
)
print(res.json())

{
  "text_length": 13,
  "tokens": {
    "gpt-4o": {
      "model": "gpt-4o",
      "tokens": 4,
      "method": "estimate",
      "cost": {
        "input": 0.00001,
        "output": 0.00004
      }
    }
  },
  "model_count": 32
}

How to Use LLM Token Counter

1. 1

   Paste text to count

   Paste any content: prompt text, article, code snippet, or conversation. The tool counts tokens using the actual tokenizer for each model.

2. 2

   Select your target model

   Choose from GPT, Claude, Gemini, Llama, and others. Token counts vary by model due to different tokenizers.

3. 3

   See token estimate and cost breakdown

   Get total token count, approximate cost at current pricing, and cost per 1M tokens. Useful for budgeting API calls.

4. 4

   Count multiple texts in batch

   Paste multiple prompts or messages separated by '---'. Get individual counts and total across all inputs.

5. 5

   Test text reduction strategies

   Use the counter to compare token usage before and after shortening. Remove redundant words or summarize to optimize API costs.

Frequently Asked Questions