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

Selecting the right data format can quietly decide whether your AI system stays affordable or slowly drains your budget. I learned this the hard way while sending large datasets to LLM APIs. The same data, formatted differently, produced wildly different token counts. This comparison between TOON, JSON, and YAML comes directly from those real deployment lessons.

Quick Comparison Table

Same Data in Three Formats

I often test formats by pushing identical payloads through the same pipeline. This example mirrors a real ecommerce catalog I once sent to an LLM for product summarization.

JSON Format

{
  "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 Format

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 Format

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

In production, JSON caused my prompt sizes to balloon. YAML helped slightly, but TOON cut token usage enough to avoid hitting model limits entirely.

Token Count Analysis from Real Usage

Before switching formats, I validated token counts using the same model encoding used by the API.

import tiktoken

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

json_text = '{"products":[{"id":1,"name":"Wireless Mouse","price":29.99}]}'
toon_text = 'products [1] {id, name, price}\n1, Wireless Mouse, 29.99'

print(count_tokens(json_text))
print(count_tokens(toon_text))

One early mistake was feeding invalid JSON into token counters, which skewed results. Running payloads through https://jsonformatterspro.com caught hidden JSON errors before testing.

Practical Problems I Faced with Each Format

TOON in Real Projects

• Early tooling gaps required custom converters
• Inconsistent arrays caused broken tables
• Indentation errors changed hierarchy meaning
• Once stabilized, token savings were immediate and measurable

JSON in Real Projects

• Unexpected token errors from trailing commas
• Large nested objects exploded token counts
• Validation errors broke downstream parsing
• Debugging became easier with strict parsers and formatters

YAML in Real Projects

• Indentation mistakes caused silent logic errors
• Tabs versus spaces issues in CI pipelines
• Parsing failures varied across languages
• Human readability remained its strongest advantage

When Each Format Makes Sense

Use TOON When

• Sending structured data to LLM APIs
• Optimizing prompt cost and size
• Working with uniform datasets
• Operating under strict token budgets

Use JSON When

• Building APIs between services
• Needing strict schema validation
• Relying on mature tooling
• Storing machine generated data

Use YAML When

• Writing configuration by hand
• Needing comments inside data
• Managing deployment files
• Optimizing for human editing

Conclusion

Each format has a place. TOON is built for modern AI workflows where token efficiency matters. JSON remains unbeatable for interoperability and tooling. YAML shines when humans need to read and edit data. Choosing the right one is less about preference and more about understanding the real costs and failure points you will encounter in production.