AI Token Counter

What does the AI Token Counter do?

The AI Token Counter calculates exact subword tokens, character counts, word statistics, and estimated prompt API ingestion costs across a comprehensive 5 × 3 grid of 15 frontier and production Large Language Models (LLMs). It evaluates token counts and prompt pricing for Google Gemini 3.7 Flash & 3.1 Pro, OpenAI GPT-5.6 Sol, Terra & Luna, Claude Sonnet 5, Opus 5 & Haiku 4.5, DeepSeek V4-Pro & V4-Flash, OpenAI o3-mini (Reasoning), Meta Llama 4 Scout, Qwen 2.5 72B, and Mistral Large 3.

Supported Model Architectures (15 Models / 5 × 3 Grid)

Core Concepts

Understanding how modern LLM tokenizers process prompt text:

• Byte-Pair Encoding (BPE): Modern LLMs break text into subword token chunks rather than whole words. Common words like "the" form a single token, while code identifiers, compound words, and non-English scripts segment into multiple subwords.
• Next-Gen Vocabulary Efficiency: Modern tokenizers like OpenAI's o200k_base, Gemini 3, and Qwen 150k BPE compress source code and non-English text far more densely than legacy 100k vocabularies, reducing overall token consumption.
• Reasoning & Multimodal Model Pricing: Calculates prompt ingestion costs based on current official provider API pricing per 1 Million tokens, helping developers budget context window expenditures across frontier reasoning models.
• WebMCP Integration: Enables AI coding assistants, agent browser extensions, and local LLMs to invoke token calculation directly via local Model Context Protocol (MCP) tool calls.

Tool Limitations & Estimation Accuracy

[!IMPORTANT] Estimation Notice: Token counts and estimated costs are calculated using client-side heuristic BPE subword estimators. This tool provides a close approximation (typically within 1–3% of provider metrics) and will not 100% exactly match real-world API token counts. Differences may arise from:

1. Hidden system-level framing tokens (e.g. <|im_start|>, <|im_end|>, role headers).
2. Upstream provider tokenizer revisions and fine-tuned vocabulary patches.
3. White-space and UTF-8 multibyte normalization nuances.

How to use the tool?

1. Paste or Upload Prompt: Paste your prompt, system instructions, source code, or JSON payload into the editor, click Upload, or click Sample.
2. Review Real-Time Metrics: Instantly inspect token totals for Gemini 3.7 Flash, GPT-5.6 Sol, Claude Sonnet 5, and DeepSeek V4-Pro alongside word, character, and line counts.
3. Analyze Cost Estimates: Review the 15-model pricing breakdown table to select the most cost-effective LLM for your workload.
4. Copy or Export CSV: Click Copy CSV to copy the formatted CSV spreadsheet table to your clipboard or Download CSV to save a complete token estimation CSV file.

Related Developer Utilities

If you build AI applications, prompt engineering pipelines, and LLM integrations, explore these related tools:

• JSON Formatter: Format and validate JSON schemas for LLM tool calling.
• YAML to JSON Converter: Convert YAML manifests into JSON for LLM function payloads.
• Base64 Encoder: Encode binary image assets for multimodal vision models.
• Text Diff Tool: Compare prompt revisions and model generation outputs side-by-side.

WebMCP (Model Context Protocol) Integration

This tool natively exposes a WebMCP tool interface for AI agents, allowing AI assistants running in the browser to tokenize text locally.

WebMCP Tool Definition

{
  "name": "ai_count_tokens",
  "description": "Calculates token counts, subword metrics, text stats, and prompt processing costs across modern LLMs including Gemini 3.7 Flash, GPT-5.6 Sol, Claude Sonnet 5, DeepSeek V4-Pro, and Meta Llama 4.",
  "inputSchema": {
    "type": "object",
    "properties": {
      "rawText": {
        "type": "string",
        "description": "The raw text, prompt, code snippet, or document payload to tokenize and analyze."
      },
      "model": {
        "type": "string",
        "default": "all",
        "description": "Optional model filter: 'all', 'gemini-3-7-flash', 'gpt-5-6-sol', 'claude-sonnet-5', 'deepseek-v4-pro', or 'llama-4-scout'."
      }
    },
    "required": ["rawText"]
  }
}

REST API Integration

blueutils.com provides a free REST API endpoint (POST https://blueutils.com/api/ai/token-counter) to programmatically calculate token metrics across multi-model architectures.

API Request Parameters

API Request Payload Examples

cURL

curl -X POST https://blueutils.com/api/ai/token-counter \
  -H "Content-Type: application/json" \
  -d '{
    "rawText": "You are an expert full-stack developer. Build a high-performance REST API."
  }'

Python

import requests

url = "https://blueutils.com/api/ai/token-counter"
payload = {
    "rawText": "You are an expert full-stack developer. Build a high-performance REST API."
}
headers = {"Content-Type": "application/json"}
response = requests.post(url, json=payload, headers=headers)
print(response.json())

Java

import java.net.URI;
import java.net.http.HttpClient;
import java.net.http.HttpRequest;
import java.net.http.HttpResponse;

public class Main {
    public static void main(String[] args) throws Exception {
        String jsonPayload = """
            {
                "rawText": "You are an expert full-stack developer. Build a high-performance REST API."
            }
            """;

        HttpClient client = HttpClient.newHttpClient();
        HttpRequest request = HttpRequest.newBuilder()
            .uri(URI.create("https://blueutils.com/api/ai/token-counter"))
            .header("Content-Type", "application/json")
            .POST(HttpRequest.BodyPublishers.ofString(jsonPayload))
            .build();

        HttpResponse<String> response = client.send(request, HttpResponse.BodyHandlers.ofString());
        System.out.println(response.body());
    }
}

API Response Parameters

API Response Payload Examples

Success Response (HTTP 200 OK)

{
  "isValid": true,
  "textStats": {
    "words": 10,
    "characters": 69,
    "charactersNoSpaces": 60,
    "lines": 1,
    "paragraphs": 1,
    "avgCharsPerToken": "5.75",
    "avgTokensPerWord": "1.20"
  },
  "tokenCounts": {
    "gemini-3-7-flash": 12,
    "gpt-5-6-sol": 12,
    "claude-sonnet-5": 13,
    "deepseek-v4-pro": 12,
    "llama-4-scout": 12
  },
  "models": [
    {
      "id": "gemini-3-7-flash",
      "name": "Google Gemini 3.7 Flash",
      "tokenizer": "Gemini 3 Tokenizer",
      "tokens": 12,
      "pricePerMillion": 1.5,
      "estimatedCost": "0.000018"
    },
    {
      "id": "gpt-5-6-sol",
      "name": "OpenAI GPT-5.6 Sol (Flagship)",
      "tokenizer": "o200k_base",
      "tokens": 12,
      "pricePerMillion": 4.0,
      "estimatedCost": "0.000048"
    }
  ],
  "disclaimer": "Token counts and pricing are client-side approximations based on BPE subword heuristics. Real-world API token counts may vary slightly (typically within 1–3%) depending on upstream provider updates and special control tokens."
}

Validation Failure Response (HTTP 400 Bad Request)

{
  "isValid": false,
  "error": "Invalid input: Text payload cannot be empty."
}

Rate Limit Exceeded Response (HTTP 429 Too Many Requests)

{
  "error": "API rate limit exceeded. Please wait or contact support@blueutils.com."
}

Why use an API for token counting?

• Context Window Budgeting: Validate user prompts against LLM context window limits before sending requests, preventing unexpected truncation errors.
• Cost Allocation & Metering: Accurately calculate token costs for multi-tenant SaaS applications before routing requests to upstream AI model providers.
• Prompt Optimization: Track token reductions across prompt refactoring iterations to trim latency and expenses in high-volume production systems.

Native Usage

How to tokenize text locally in terminal environments:

Python

Using tiktoken:

import tiktoken

enc = tiktoken.get_encoding("o200k_base")
tokens = enc.encode("You are an expert AI software engineer.")
print(f"Token count: {len(tokens)}")

Node.js / JavaScript

Using js-tiktoken:

const { encodingForModel } = require("js-tiktoken");

const enc = encodingForModel("gpt-4o");
const tokens = enc.encode("You are an expert AI software engineer.");
console.log(`Token count: ${tokens.length}`);

Windows (PowerShell)

python -c "import tiktoken; enc = tiktoken.get_encoding('o200k_base'); print('Tokens:', len(enc.encode(open('prompt.txt').read())))"

Linux / Unix (Bash)

python3 -c "import tiktoken; enc = tiktoken.get_encoding('o200k_base'); print('Tokens:', len(enc.encode(open('prompt.txt').read())))"