LLM Token Counter

Programmatic Access

LLM Token Counter is also callable as a free HTTP JSON API at https://aidevhub.io/api/token-counter/ — GET with query parameters or POST with a JSON body, no authentication, CORS enabled, 50 requests/day per IP. Responses return { ok, tool, result, meta }.

curl -s "https://aidevhub.io/api/token-counter/?text=Hello%20world"

Machine-readable contract: /api/tool/token-counter.json · All API endpoints: /agents/ · LLM site index: /llms.txt

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

How do I count tokens for GPT and Claude prompts with an offline tokenizer?

Paste your prompt into the counter above. It computes character and word counts, then estimates tokens per model family using the characters-per-token ratios below — all in the browser, with no API call and no network dependency after the page loads. This matters when prompts contain confidential material, or when you need counts inside an offline or air-gapped workflow.

Characters-per-token ratios by model family

Ratios apply to typical English prose; data as of August 09, 2026. A practical consequence: the same prompt usually produces more tokens on Claude (3.5 chars/token) than on GPT-family models (4.0 chars/token).

How accurate is a ratio-based token estimate?

A characters-per-token ratio is an estimate, not a byte-pair-encoding run. It is closest for plain English text and drifts for source code, dense punctuation, non-Latin scripts, and unusual whitespace, where real tokenizers split text differently. Use ratio estimates for sizing prompts, comparing models, and budgeting; use the provider's own tokenizer when you need billing-exact counts.

When do I need an exact count instead?

1. Enforcing hard context-window limits, where an overshoot causes a request to be rejected or silently truncated.
2. Metering or invoicing customers on token usage.
3. For OpenAI models, run the open-source tiktoken library locally; for Anthropic models, call the count-tokens endpoint of the API.
4. For everything before that point — drafting, comparing, budgeting — a client-side estimate is faster and keeps the prompt on your machine.