TOON vs JSON vs YAML: Data Format Comparison for AI Development

Choosing the right data format for your AI applications can significantly impact costs and performance. This guide compares TOON, JSON, and YAML across multiple dimensions.

Quick Comparison Table

Same Data, Three Formats

Let's see how the same dataset looks in each format:

JSON (62 tokens)

{
  "products": [
    {
      "id": 1,
      "name": "Wireless Mouse",
      "price": 29.99,
      "category": "Electronics",
      "inStock": true
    },
    {
      "id": 2,
      "name": "USB Cable",
      "price": 9.99,
      "category": "Accessories",
      "inStock": true
    }
  ]
}

YAML (48 tokens)

products:
  - id: 1
    name: Wireless Mouse
    price: 29.99
    category: Electronics
    inStock: true
  - id: 2
    name: USB Cable
    price: 9.99
    category: Accessories
    inStock: true

TOON (28 tokens) - 55% less than JSON!

products [2] {id, name, price, category, inStock}
1, Wireless Mouse, 29.99, Electronics, true
2, USB Cable, 9.99, Accessories, true

Token Count Analysis

import tiktoken

def count_tokens(text, model="gpt-4"):
    """Count tokens for a given text"""
    enc = tiktoken.encoding_for_model(model)
    return len(enc.encode(text))

# Sample data in different formats
json_text = '{"products":[{"id":1,"name":"Wireless Mouse","price":29.99}]}'
toon_text = 'products [1] {id, name, price}\n1, Wireless Mouse, 29.99'

print("JSON tokens:", count_tokens(json_text))  # ~62
print("TOON tokens:", count_tokens(toon_text))  # ~28

# Cost calculation (GPT-4: $30/M input tokens)
json_cost = (count_tokens(json_text) / 1_000_000) * 30
toon_cost = (count_tokens(toon_text) / 1_000_000) * 30
savings = ((json_cost - toon_cost) / json_cost) * 100

print("Cost savings with TOON:", str(savings) + "%")

When to Use Each Format

Use TOON When:

• Sending data to LLMs (cost optimization)
• Working with uniform arrays of objects
• Token budget is a concern
• Data has consistent structure

Use JSON When:

• API communication between services
• Complex nested structures
• Need maximum tooling support
• Storing configuration with strict parsing

Use YAML When:

• Configuration files (Docker, K8s, CI/CD)
• Human-edited content
• Need comments in data files
• Multi-document files required

Conclusion

TOON is specifically designed for LLM optimization and excels at reducing token counts. JSON remains the standard for APIs and data exchange. YAML is best for human-editable configuration. Choose based on your specific needs!