ctoken 1.1.1

A lightweight library to count and estimate costs for OpenAI API tokens

ctoken

A high-performance Python library for estimating costs of OpenAI API calls. Track your API expenses precisely with support for all OpenAI models and APIs.

Install

pip install ctoken

Features

• Accurate cost estimation for all OpenAI models
• Compatible with all OpenAI APIs (Chat Completions and Responses)
• Detailed cost breakdown (prompt, completion, cached tokens)
• Works with streaming responses
• External pricing data with no hardcoded values
• Dynamic data refresh from remote sources
• Mock response support for testing
• High-performance token counting with efficient caching
• Smart model detection for accurate pricing
• Robust error handling through unified exception type

Quick Start

from openai import OpenAI
from ctoken import ctoken

client = OpenAI(api_key="YOUR_API_KEY")

# Make your API call
response = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Explain quantum computing"}]
)

# Get the cost estimate and token counts
result = ctoken(response)
print(f"Tokens: {result['total_tokens']} | Cost: ${result['total_cost']}")

Real-world Examples

1. Chat Completion API

from openai import OpenAI
from ctoken import ctoken

client = OpenAI(api_key="sk-...")
resp = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "Hi there!"}],
)

# Get token counts and cost breakdown
result = ctoken(resp)
print(f"Token counts: {result['prompt_tokens']} prompt, {result['completion_tokens']} completion")
print(f"Total cost: ${result['total_cost']}")
# Complete result:
# {'prompt_tokens'       : 3,
#  'completion_tokens'   : 12,
#  'total_tokens'        : 15,
#  'cached_tokens'       : 0,
#  'prompt_cost_uncached': 0.00000150,
#  'prompt_cost_cached'  : 0.00000000,
#  'completion_cost'     : 0.00000600,
#  'total_cost'          : 0.00000750}

2. Responses API

from openai import OpenAI
from ctoken import ctoken

client = OpenAI(api_key="sk-...")
resp = client.responses.create(
    model="gpt-4.1-mini",
    input=[{"role": "user", "content": "Hi there!"}],
)

# Get detailed cost breakdown
print(ctoken(resp))
# {'prompt_cost_uncached': 0.00000120,
#  'prompt_cost_cached'  : 0.00000000,
#  'completion_cost'     : 0.00001920,
#  'total_cost'          : 0.00002040}

3. Streaming Responses

from openai import OpenAI
from ctoken import ctoken

client = OpenAI(api_key="sk-...")
stream = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Write a poem about AI"}],
    stream=True
)

# Collect the stream and use it
response_chunks = []
for chunk in stream:
    response_chunks.append(chunk)
    # Use the chunk for whatever you need
    # ...

# Get cost at the end of the stream
cost = ctoken(response_chunks)
print(f"Streaming API call cost: ${cost['total_cost']}")

4. Batch Estimation

from openai import OpenAI
from ctoken import ctoken
import pandas as pd

client = OpenAI(api_key="sk-...")
responses = []
total_cost = 0

# Collect multiple responses
questions = ["What is AI?", "How does quantum computing work?", "Explain neural networks"]
for question in questions:
    resp = client.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": question}],
    )
    responses.append(resp)
    
    # Accumulate costs
    cost_estimate = ctoken(resp)
    total_cost += cost_estimate['total_cost']

# Create a report
df = pd.DataFrame([{
    'prompt': responses[i].choices[0].message.content[:50] + "...",
    'tokens': responses[i].usage.total_tokens,
    'cost': ctoken(responses[i])['total_cost']
} for i in range(len(responses))])

print(f"Total batch cost: ${total_cost:.8f}")
print(df)

5. Refresh Pricing Data

from ctoken import refresh_pricing

# Force-reload the current pricing data (cache TTL is 24h)
refresh_pricing()

6. Pricing Data Sources

The library contains pricing data in a bundled Python dictionary format. It also supports fetching pricing from:

1. Local file: data/gpt_pricing_data.csv if available
2. Remote source: Official GitHub repository
3. Bundled fallback data: A default dataset included with the package

This approach ensures pricing data remains up-to-date without requiring library updates but also provides fast access through the bundled dictionary.

7. Error Handling

from ctoken import ctoken, CostEstimateError

try:
    cost = ctoken(response)
    print(f"Cost: ${cost['total_cost']}")
except CostEstimateError as e:
    print(f"Error estimating cost: {e}")
    # Handle gracefully

API Reference

from ctoken import ctoken, estimate_cost, refresh_pricing, CostEstimateError

# Main function for cost estimation
ctoken(response) → dict[str, Any]
    """
    Accepts a ChatCompletion, streamed chunks, or Response object.
    Returns a dict with:
        prompt_tokens        : int   # Number of prompt tokens
        completion_tokens    : int   # Number of completion tokens
        total_tokens         : int   # Total tokens used
        cached_tokens        : int   # Number of cached tokens
        prompt_cost_uncached : float # Cost of non-cached prompt tokens
        prompt_cost_cached  : float # Cost of cached prompt tokens (often reduced rate)
        completion_cost      : float # Cost of model-generated tokens
        total_cost           : float # Total cost of the API call
    """

# Alias for backward compatibility
estimate_cost = ctoken

# Force reload pricing data
refresh_pricing() → None
    """Force-reload the remote pricing CSV (cache TTL is 24h)."""

# Exception for errors
CostEstimateError
    """Unified exception for recoverable input, parsing, or pricing errors."""

Production Usage Tips

Cost Monitoring in Flask Application

from flask import Flask, request, jsonify
from openai import OpenAI
from ctoken import ctoken
import logging

app = Flask(__name__)
client = OpenAI(api_key="sk-...")

# Configure cost logging
cost_logger = logging.getLogger("api_costs")
handler = logging.FileHandler("api_costs.log")
handler.setFormatter(logging.Formatter('%(asctime)s - %(message)s'))
cost_logger.addHandler(handler)
cost_logger.setLevel(logging.INFO)

@app.route("/ask", methods=["POST"])
def ask_ai():
    user_query = request.json.get("query", "")
    user_id = request.json.get("user_id", "anonymous")
    
    response = client.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": user_query}]
    )
    
    # Log the cost with user ID
    result = ctoken(response)
    cost_logger.info(
        f"User: {user_id} | Query: {user_query[:30]}... | "
        f"Tokens: {result['total_tokens']} | Cost: ${result['total_cost']}"
    )
    
    return jsonify({
        "answer": response.choices[0].message.content,
        "tokens": {
            "prompt": result['prompt_tokens'],
            "completion": result['completion_tokens'],
            "total": result['total_tokens']
        },
        "cost": result['total_cost']
    })

if __name__ == "__main__":
    app.run(debug=True)

Budget Management

from openai import OpenAI
from ctoken import ctoken
import os
from datetime import datetime

client = OpenAI(api_key="sk-...")
DAILY_BUDGET = 1.0  # $1 per day

# Track daily spending
today = datetime.now().strftime("%Y-%m-%d")
spent_today = 0.0

def make_api_call(query):
    global spent_today
    
    # Check budget before making the call
    if spent_today >= DAILY_BUDGET:
        return {"error": "Daily budget exceeded"}
    
    # Make the API call
    response = client.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": query}]
    )
    
    # Calculate and add to spending
    cost = ctoken(response)
    spent_today += cost['total_cost']
    
    return {
        "response": response.choices[0].message.content,
        "cost": cost['total_cost'],
        "remaining_budget": DAILY_BUDGET - spent_today
    }

Performance Improvements

This library has been optimized for performance with:

• Efficient caching of pricing data
• Improved error handling with detailed messages
• Smart model name parsing for versioned models
• Optimized token calculations using Python's Decimal for financial accuracy
• Reduced memory usage with streamlined data structures
• Enhanced attribute access for safe navigation of nested objects

License

MIT

Links

• GitHub Repository
• PyPI Package
• OpenAI Pricing Page

Project Structure

The project is organized as follows:

ctoken/
├── ctoken/                # Main package
│   ├── __init__.py        # Package initialization
│   ├── core.py            # Core functionality
│   ├── estimate.py        # Cost estimation
│   ├── parser.py          # Token parsing
│   ├── pricing.py         # Pricing data handling
│   └── data/              # Package data files
│       └── gpt_pricing_data.csv   # Pricing data
├── data/                  # External data files
│   └── openai_text_tokens_pricing.csv  # Latest scraped pricing
├── examples/              # Example code
│   ├── basic_usage.py     # Basic token counting
│   ├── cost_estimation.py # Cost estimation examples
│   ├── demo_ctoken_usage.py  # Complete usage demo
│   └── demo_cost_estimation.py  # Detailed cost estimations
├── scripts/               # Utility scripts
│   ├── openai_pricing_scraper.py  # Scraper for OpenAI pricing
│   └── verify_pricing_data.py     # Verify pricing data
├── tests/                 # Test suite
│   ├── __init__.py
│   ├── test_api_cost_estimation.py
│   ├── test_ctoken.py
│   └── test_model_pricing.py
├── .gitignore
├── MANIFEST.in
├── pyproject.toml
├── pytest.ini
├── README.md
├── requirements-dev.txt
└── setup.py

Directory Overview

• ctoken/: Main package source code
• data/: External data files including scraped pricing information
• examples/: Example code showing how to use the package
• scripts/: Utility scripts for maintenance tasks
• tests/: Test suite for the package