OpenAI Models Pricing

Comprehensive OpenAI pricing solution with automated data updates and Python library for cost calculation.

This project provides two integrated components:

1. Pricing Data Provider - Automatically scrapes and publishes OpenAI pricing data via JSON API
2. Python Library (openai-pricing-api) - Clean API for calculating costs and tracking usage

🎯 Features

Pricing Data Provider

• Daily automatic price updates via GitHub Actions
• JSON API for integration into your projects
• Web interface for browsing prices
• Price history for the last 90 days
• Search and filter models

Python Library

• Simple and clean API for cost calculation
• Support for all OpenAI model types (GPT, DALL-E, Whisper, Sora)
• Automatic pricing updates with local caching
• Credit-based billing support
• Variance tracking (estimated vs actual costs)
• Mixed usage calculation across multiple models
• Zero dependencies (only Pydantic required)

Demo

Site available at: https://bes-dev.github.io/openai-pricing-api/

📦 Quick Start - Python Library

Install the library:

pip install openai-pricing-api

Calculate costs:

from openai_pricing_api import PricingCalculator

calculator = PricingCalculator()

# Calculate cost for GPT-4o
cost = calculator.calculate_token_cost(
    "gpt-4o",
    input_tokens=1000,
    output_tokens=500
)
print(f"Cost: ${cost:.4f}")  # Cost: $0.0125

# Calculate cost for DALL-E-3
cost = calculator.calculate_image_cost(
    "dall-e-3",
    count=5,
    size="1024x1024",
    quality="hd"
)
print(f"Cost: ${cost:.4f}")  # Cost: $0.4000

# Mixed usage (multiple models)
usage = {
    "analyze": {"model": "gpt-4o", "input_tokens": 1000, "output_tokens": 500},
    "generate": {"model": "dall-e-3", "count": 5, "size": "1024x1024"}
}
total = calculator.calculate_mixed_usage(usage)
print(f"Total: ${total:.4f}")

For complete library documentation, see Python Library Usage section.

📁 Project Structure

openai-pricing-api/
├── .github/workflows/
│   ├── update-pricing.yml         # Daily pricing updates
│   ├── publish-pypi.yml           # PyPI publishing (WIP)
│   └── tests.yml                  # Library tests (WIP)
├── github_pages/                   # GitHub Pages site
│   ├── index.html                 # Web interface
│   ├── script.js                  # Frontend JavaScript
│   ├── styles.css                 # Styling
│   ├── api.json                   # Simplified API (generated)
│   ├── pricing.json               # Full data (generated)
│   └── history.json               # Price history (generated)
├── scripts/
│   └── fetch_openai_pricing.py    # Price scraping script
├── src/openai_pricing/             # Python library source
│   ├── __init__.py
│   ├── calculator.py              # Main calculator class
│   ├── pricing.py                 # Pricing data provider
│   └── models.py                  # Pydantic data models
├── examples/
│   ├── basic_usage.py             # Library usage examples
│   └── README.md                  # Examples documentation
├── pyproject.toml                 # Python package configuration
└── requirements.txt               # Scraper dependencies

🚀 Quick Start

Option 1: Use Python Library (Recommended)

pip install openai-pricing-api

See Python Library Usage for full documentation.

Option 2: Fork Repository for Custom Data Provider

1. Fork the Repository

Fork this repository to your GitHub account.

2. Enable GitHub Pages

Important: You must enable GitHub Pages before the workflow can deploy.

1. Go to your repository on GitHub
2. Click Settings (top menu)
3. Scroll down to Pages (left sidebar)
4. Under Source, select GitHub Actions from the dropdown
5. Click Save (if available)

Note: If you don't see the "GitHub Actions" option:

• Make sure your repository is public (or you have GitHub Pro for private repos)
• The workflow must run at least once to create the deployment
• You may need to wait a few seconds and refresh the page

3. Enable GitHub Actions

1. Go to the Actions tab
2. If prompted, click "I understand my workflows, go ahead and enable them"
3. The workflow will run automatically on push or on schedule (daily at 12:00 UTC)

4. Run Workflow Manually

1. Go to Actions → Update OpenAI Pricing
2. Click "Run workflow" dropdown (right side)
3. Select branch (usually main or master)
4. Click green "Run workflow" button
5. Wait for completion (~2-3 minutes)
6. If it fails with "Pages not enabled", go back to step 2 and enable Pages first

5. Check the Result

After the workflow completes successfully:

• Open https://bes-dev.github.io/openai-pricing-api/
• It may take 1-2 minutes for the site to become available
• Check the Actions tab for the deployment URL in the workflow summary

API Usage

Simple API (Recommended)

curl https://bes-dev.github.io/openai-pricing-api/api.json

Response:

{
  "models": {
    "gpt-4o": {
      "model": "gpt-4o",
      "pricing_type": "per_1m_tokens",
      "input": 2.5,
      "output": 10.0,
      "timestamp": "2025-01-27T12:00:00Z"
    }
  },
  "timestamp": "2025-01-27T12:00:00Z",
  "models_count": 20,
  "source": "openai_official_pricing_page"
}

JavaScript Example

fetch('https://bes-dev.github.io/openai-pricing-api/api.json')
  .then(res => res.json())
  .then(data => {
    console.log('Models:', data.models);
    console.log('Last updated:', data.timestamp);
  });

Python Example

import requests

url = 'https://bes-dev.github.io/openai-pricing-api/api.json'
data = requests.get(url).json()

# Filter by category
for model_name, model_data in data['models'].items():
    if model_data.get('category') == 'image_generation_token':
        print(f"{model_name} ({model_data['category']}):")
        print(f"  Input: ${model_data.get('input', 0)}/1M tokens")
        print(f"  Output: ${model_data.get('output', 0)}/1M tokens")
        print()

# Or show all models with their categories
for model_name, model_data in data['models'].items():
    category = model_data.get('category', 'unknown')
    pricing_type = model_data.get('pricing_type', 'unknown')
    print(f"{model_name}: {category} ({pricing_type})")

Available Endpoints

• /api.json - Simplified data (recommended)
• /pricing.json - Full data with all details
• /history.json - Price change history for the last 90 days

Local Testing

Install Dependencies

pip install -r requirements.txt
playwright install chromium

Run the Script

python scripts/fetch_openai_pricing.py

This will create files in the github_pages/ directory:

• pricing.json
• api.json
• history.json

View Results

Open github_pages/index.html in your browser.

Update Schedule

GitHub Actions workflow runs:

• Daily at 12:00 UTC (automatically)
• On push to master/main (automatically)
• Manually via GitHub Actions web interface

Configuration

Change Schedule

Edit .github/workflows/update-pricing.yml:

schedule:
  - cron: '0 12 * * *'  # Daily at 12:00 UTC

Examples:

• '0 */6 * * *' - Every 6 hours
• '0 0 * * *' - Daily at midnight UTC
• '0 12 * * 1' - Every Monday at 12:00 UTC

Change Pricing URL

Edit scripts/fetch_openai_pricing.py:

PRICING_URL = "https://platform.openai.com/docs/pricing"  # API docs (recommended)
# OR
PRICING_URL = "https://openai.com/api/pricing/"  # Marketing page (limited data)

Note: The API docs URL (platform.openai.com) contains more comprehensive pricing data (60+ models) compared to the marketing page.

Data Structure

pricing.json

Full model data:

{
  "gpt-4o": {
    "model": "gpt-4o",
    "pricing_type": "per_1m_tokens",
    "category": "language_model",
    "input": 2.5,
    "output": 10.0,
    "timestamp": "2025-01-27T12:00:00Z"
  },
  "gpt-image-1": {
    "model": "gpt-image-1",
    "pricing_type": "per_1m_tokens",
    "category": "image_generation_token",
    "input": 10.0,
    "output": 40.0,
    "timestamp": "2025-01-27T12:00:00Z"
  },
  "dall-e-3": {
    "model": "dall-e-3",
    "pricing_type": "per_image",
    "category": "image_generation",
    "price": 0.04,
    "timestamp": "2025-01-27T12:00:00Z"
  }
}

history.json

Price change history:

[
  {
    "date": "2025-01-27",
    "timestamp": "2025-01-27T12:00:00Z",
    "models": { ... },
    "models_count": 20
  },
  {
    "date": "2025-01-26",
    "timestamp": "2025-01-26T12:00:00Z",
    "models": { ... },
    "models_count": 19
  }
]

Data Fields

Each model in the JSON has the following fields:

• model - Model name
• pricing_type - How the model is billed (per_1m_tokens, per_image, per_second, etc.)
• category - Model category (see below)
• input - Input price (for token-based models)
• output - Output price (for token-based models)
• cached_input - Cached input price (if available)
• price - Fixed price (for non-token models)
• timestamp - When the data was last updated

Pricing Types

• per_1m_tokens - Price per 1 million tokens (language, image-gen, embeddings)
• per_image - Price per image (DALL-E)
• per_second - Price per second (Sora video generation)
• per_minute - Price per minute (Whisper audio transcription)
• per_1k_chars - Price per 1K characters (TTS)

Categories

• language_model - GPT-5, GPT-4, GPT-3.5, davinci, babbage
• reasoning - o1-pro, o3-pro, o3-deep-research
• image_generation_token - gpt-image-1 (token-based image generation)
• image_generation - DALL-E (fixed price per image)
• video_generation - Sora models
• audio_transcription - Whisper models
• text_to_speech - TTS models
• embeddings - text-embedding models
• computer_use - Computer use models
• storage - Storage pricing
• other - Other models

---

📚 Python Library Usage

The openai-pricing-api Python library provides a clean API for calculating costs across all OpenAI model types.

Installation

# From PyPI
pip install openai-pricing-api

# From source (for development)
git clone https://github.com/bes-dev/openai-pricing-api.git
cd openai-pricing-api
pip install -e .

Basic Usage

from openai_pricing_api import PricingCalculator

calculator = PricingCalculator()

API Reference

calculate_token_cost(model, input_tokens=0, output_tokens=0, cached_tokens=0)

Calculate cost for token-based models (GPT-4, GPT-3.5, embeddings, etc.).

# GPT-4o
cost = calculator.calculate_token_cost(
    "gpt-4o",
    input_tokens=1000,
    output_tokens=500
)
print(f"${cost:.4f}")  # $0.0125

# With cached tokens
cost = calculator.calculate_token_cost(
    "gpt-4o",
    input_tokens=500,
    output_tokens=500,
    cached_tokens=500  # 90% cheaper
)

calculate_image_cost(model, count=1, size="1024x1024", quality="standard")

Calculate cost for image generation (DALL-E, gpt-image-1).

# DALL-E-3
cost = calculator.calculate_image_cost(
    "dall-e-3",
    count=5,
    size="1024x1024",
    quality="hd"
)
print(f"${cost:.4f}")  # $0.4000

# gpt-image-1
cost = calculator.calculate_image_cost(
    "gpt-image-1",
    count=10,
    size="1024x1024",
    quality="low"
)

calculate_video_cost(model, duration_seconds)

Calculate cost for video generation (Sora).

cost = calculator.calculate_video_cost(
    "sora-2",
    duration_seconds=30
)

calculate_mixed_usage(usage)

Calculate total cost from multiple models and stages.

usage = {
    "analysis": {
        "model": "gpt-4o",
        "input_tokens": 1000,
        "output_tokens": 500
    },
    "generation": {
        "model": "gpt-4o",
        "input_tokens": 2000,
        "output_tokens": 800
    },
    "images": {
        "model": "dall-e-3",
        "count": 5,
        "size": "1024x1024",
        "quality": "standard"
    }
}

total = calculator.calculate_mixed_usage(usage)
print(f"Total: ${total:.4f}")

estimate_credits(items, overhead, per_item, currency="credits")

Estimate cost in credits or custom units.

estimate = calculator.estimate_credits(
    items=10,           # Number of items
    overhead=3,         # Fixed overhead
    per_item=2,         # Cost per item
    currency="credits"
)

print(f"Total: {estimate.total} {estimate.currency}")  # Total: 23 credits
print(f"Breakdown: {estimate.overhead} + ({estimate.items} × {estimate.per_item})")

calculate_actual_cost(estimated, usage)

Calculate actual cost and compare with estimate (variance tracking).

# Charge user 23 credits (estimated)
estimated_usd = 23 * 0.05  # Convert to USD

# After generation, calculate actual cost
actual = calculator.calculate_actual_cost(
    estimated=estimated_usd,
    usage={
        "stage1": {"model": "gpt-4o", "input_tokens": 1000, "output_tokens": 500},
        "stage2": {"model": "dall-e-3", "count": 5}
    }
)

print(f"Estimated: ${actual.estimated:.4f}")
print(f"Actual: ${actual.actual:.4f}")
print(f"Variance: {actual.variance_percent:+.1f}%")
print(f"Is over budget: {actual.is_over_budget}")
print(f"Savings/Loss: ${actual.savings:.4f}")

get_model_pricing(model)

Get pricing information for a specific model.

pricing = calculator.get_model_pricing("gpt-4o")

if pricing:
    print(f"Model: {pricing.model}")
    print(f"Type: {pricing.pricing_type}")
    print(f"Input: ${pricing.input_price}/1M tokens")
    print(f"Output: ${pricing.output_price}/1M tokens")
    if pricing.cached_input_price:
        print(f"Cached: ${pricing.cached_input_price}/1M tokens")

get_available_models()

Get list of all available models.

models = calculator.get_available_models()
print(f"Available models: {len(models)}")
print(models[:5])  # ['gpt-4o', 'gpt-5', 'dall-e-3', ...]

refresh_pricing()

Force refresh pricing data from API.

success = calculator.refresh_pricing()
if success:
    print("Pricing data refreshed successfully")

Configuration

Custom API URL

calculator = PricingCalculator(
    api_url="https://your-custom-api.com/pricing.json"
)

Custom Cache Location

from pathlib import Path

calculator = PricingCalculator(
    cache_file=Path("/custom/path/pricing_cache.json")
)

Custom Cache Duration

from datetime import timedelta

calculator = PricingCalculator(
    cache_duration=timedelta(hours=24)  # Cache for 24 hours
)

Data Models

CostEstimate

class CostEstimate:
    items: int              # Number of items
    overhead: float         # Fixed overhead
    per_item: float         # Cost per item
    total: float            # Total cost
    currency: str           # Currency unit

ActualCost

class ActualCost:
    estimated: float        # Estimated cost
    actual: float           # Actual cost
    variance_percent: float # Variance percentage
    is_over_budget: bool    # True if over budget
    savings: float          # Savings (negative if over)
    details: dict           # Detailed breakdown

ModelPricing

class ModelPricing:
    model: str                    # Model identifier
    pricing_type: str             # Type of pricing
    input_price: Optional[float]  # Input price per 1M tokens
    output_price: Optional[float] # Output price per 1M tokens
    cached_input_price: Optional[float]
    image_pricing: Optional[dict] # Image pricing by size/quality
    video_price_per_second: Optional[float]
    source: str                   # Data source

Examples

Complete examples are available in the examples/ directory:

python examples/basic_usage.py

This will demonstrate:

1. Token-based cost calculation
2. Image generation cost
3. Mixed usage calculation
4. Credit-based billing
5. Variance tracking
6. Model information retrieval

Error Handling

The library raises ValueError for invalid inputs:

try:
    cost = calculator.calculate_token_cost("invalid-model", input_tokens=1000)
except ValueError as e:
    print(f"Error: {e}")  # Error: Model not found: invalid-model

Caching

The library automatically caches pricing data:

• Location: ~/.openai_pricing_api/pricing_cache.json
• Duration: 12 hours (configurable)
• Fallback: Uses cached data if API is unavailable
• Update: Automatically refreshes expired cache

To force refresh:

calculator.refresh_pricing()

Integration with tiktoken

For accurate token counting:

import tiktoken
from openai_pricing_api import PricingCalculator

# Count tokens
encoding = tiktoken.encoding_for_model("gpt-4o")
input_tokens = len(encoding.encode("Your input text"))
output_tokens = len(encoding.encode("Model response"))

# Calculate cost
calculator = PricingCalculator()
cost = calculator.calculate_token_cost(
    "gpt-4o",
    input_tokens=input_tokens,
    output_tokens=output_tokens
)

---

How to Calculate Costs (Manual Method)

This section explains how to use the pricing data to calculate the cost of using different OpenAI models.

Understanding Tokens

What is a token? A token is the basic unit of text processing in OpenAI models. It can be a word, subword, punctuation mark, or symbol.

Rule of thumb:

• 1 token ≈ 4 characters of English text
• 1,000 tokens ≈ 750 English words
• 100 tokens ≈ 75 words

Example: The sentence "Hello, how are you today?" contains approximately 6-7 tokens.

Cost Calculation Formulas

1. Language Models (per_1m_tokens)

Models: GPT-4, GPT-3.5, o1, embeddings, etc.

Formula:

Total Cost = (Input Tokens / 1,000,000 × Input Price) + (Output Tokens / 1,000,000 × Output Price)

Example with GPT-4o:

• Input price: $2.50 / 1M tokens
• Output price: $10.00 / 1M tokens
• Your request: 500 input tokens, 1,500 output tokens

Cost = (500 / 1,000,000 × $2.50) + (1,500 / 1,000,000 × $10.00)
     = $0.00125 + $0.015
     = $0.01625 (≈ $0.016)

With cached input:

Total Cost = (Cached Input / 1,000,000 × Cached Price) + (New Input / 1,000,000 × Input Price) + (Output / 1,000,000 × Output Price)

2. Image Generation - Token-based (image_generation_token)

Models: gpt-image-1, gpt-image-1-mini

These models have two pricing components:

A) Text tokens (for your prompt):

Text Cost = (Input Tokens / 1,000,000 × Input Price) + (Output Tokens / 1,000,000 × Output Price)

B) Image generation (per image by resolution and quality):

Image Cost = Number of Images × Price per Image (from image_pricing)

Example with gpt-image-1:

• Input: $10.00 / 1M tokens
• Output: $40.00 / 1M tokens
• Image (low quality, 1024x1024): $0.011 / image

Generate 1 image with prompt "A beautiful sunset over mountains" (≈10 tokens input, ≈50 tokens output):

Text Cost = (10 / 1,000,000 × $10.00) + (50 / 1,000,000 × $40.00)
          = $0.0001 + $0.002 = $0.0021

Image Cost = 1 × $0.011 = $0.011

Total Cost = $0.0021 + $0.011 = $0.0131 (≈ $0.013)

Quality comparison (1 image, 1024x1024):

• Low quality: $0.011 per image
• Medium quality: $0.063 per image (5.7× more expensive)
• High quality: $0.25 per image (23× more expensive)

Resolution comparison (low quality):

• 1024x1024: $0.011
• 1024x1536: $0.016 (45% more expensive)
• 1536x1024: $0.016 (45% more expensive)

3. Image Generation - Fixed Price (image_generation)

Models: DALL-E 3, DALL-E 2

Formula:

Total Cost = Number of Images × Price per Resolution

Example with DALL-E 3:

• 1024x1024: $0.12 per image
• 1024x1536: $0.12 per image

Cost for 5 images (1024x1024) = 5 × $0.12 = $0.60

4. Audio Transcription (audio_transcription)

Models: Whisper

Formula:

Total Cost = Audio Duration (minutes) × Price per Minute

Example:

• Price: $0.006 / minute
• Audio: 15 minutes

Cost = 15 × $0.006 = $0.09

5. Text-to-Speech (text_to_speech)

Models: TTS

Formula:

Total Cost = (Characters / 1,000) × Price per 1K Characters

Example:

• Price: $0.015 / 1K characters
• Text: 5,000 characters

Cost = (5,000 / 1,000) × $0.015 = $0.075

6. Video Generation (video_generation)

Models: Sora

Formula:

Total Cost = Duration (seconds) × Price per Second

Example:

• Price: $0.05 / second
• Video: 30 seconds

Cost = 30 × $0.05 = $1.50

Practical Tips

1. Use the tiktoken library to count tokens accurately before making API calls:

   import tiktoken
   
   encoding = tiktoken.encoding_for_model("gpt-4o")
   tokens = encoding.encode("Your text here")
   token_count = len(tokens)
   

2. Monitor your usage in the OpenAI dashboard to track actual token consumption.

3. Optimize costs:

   • Use lower-quality image generation when high quality isn't needed
   • Use smaller models (e.g., GPT-3.5 instead of GPT-4) for simpler tasks
   • Cache frequently used prompts to benefit from cached input pricing
   • Keep prompts concise to reduce input token count

4. Estimate before production:

   • Test with small samples to measure actual token usage
   • Account for system messages and API formatting overhead
   • Add 10-20% buffer for unexpected token usage

5. Image token consumption varies:

   • Low quality: ~85 tokens per image
   • Medium quality: ~300-400 tokens per image
   • High quality: ~765 tokens per image

Cost Comparison Example

Generate 100 images with text prompt (1024x1024):

Model	Quality	Text Cost	Image Cost	Total Cost
gpt-image-1-mini	Low	$0.11	$0.50	$0.61
gpt-image-1	Low	$0.21	$1.10	$1.31
gpt-image-1-mini	Medium	$0.11	$1.50	$1.61
DALL-E 2	Standard	$0	$1.60	$1.60
gpt-image-1-mini	High	$0.11	$5.20	$5.31
gpt-image-1	Medium	$0.21	$6.30	$6.51
DALL-E 3	Standard	$0	$12.00	$12.00
gpt-image-1	High	$0.21	$25.00	$25.21

Assumes 1,000 input tokens and 5,000 output tokens for text processing.

Automated Cost Calculator

For automated cost calculation, use the openai-pricing-api Python library:

pip install openai-pricing-api

See the Python Library Usage section for complete documentation.

Or run the examples:

python examples/basic_usage.py

Additional Resources

• Official Pricing Page: https://openai.com/api/pricing/
• OpenAI Documentation: https://platform.openai.com/docs/
• Tokenizer Tool: https://platform.openai.com/tokenizer
• tiktoken Library: https://github.com/openai/tiktoken

Notes

• Data is scraped from the official OpenAI pricing page
• Always verify current prices on openai.com/api/pricing
• Script uses Playwright for dynamic content loading
• GitHub Actions is free for public repositories

License

Apache License 2.0 - see LICENSE