TOON vs JSON / YAML / XML / TOML / CSV: The Ultimate Comparison

Choosing the right data serialization format can dramatically impact your application's performance, readability, and cost. This comprehensive guide compares TOON against the most popular data formats: JSON, YAML, XML, and TOML.

Quick Comparison Table

Format Deep Dive

JSON (JavaScript Object Notation)

Best for: APIs, web applications, configuration files

JSON is the de facto standard for data interchange on the web. It's lightweight, easy to parse, and supported by virtually every programming language. However, when it comes to LLM applications, JSON's verbosity becomes a significant drawback. See our detailed JSON vs TOON comparison.

Key limitations for LLMs:

• Repeated key names in arrays waste tokens
• Quoted strings and escape characters add overhead
• Curly braces and commas increase token count
• No built-in mechanism to reduce redundancy

YAML (YAML Ain't Markup Language)

Best for: Configuration files, CI/CD pipelines, data serialization

YAML prioritizes human readability with its indentation-based syntax. It's highly popular for configuration files and DevOps tools. While more readable than JSON, it's actually less efficient for LLMs.

Key limitations for LLMs:

• Significant whitespace contributes to token count
• Indentation-based structure adds many space/tab tokens
• Still repeats keys for each array item
• Complex documents can be ambiguous to parse

XML (eXtensible Markup Language)

Best for: Document markup, legacy systems, enterprise applications

XML was the dominant data format before JSON's rise. It's incredibly flexible and supports rich document structures with attributes and namespaces. However, it's the most verbose format of all.

Key limitations for LLMs:

• Opening and closing tags double the verbosity
• Highest token count among all formats
• Angle brackets and tag names waste space
• Not optimized for data serialization

TOML (Tom's Obvious, Minimal Language)

Best for: Configuration files, application settings

TOML was designed as a more readable alternative to INI files. It excels at configuration but struggles with deeply nested data structures and arrays of objects.

Key limitations for LLMs:

• Table headers repeat for array items
• Not designed for complex data structures
• Can become verbose with nested objects
• Limited support for arrays of complex objects

CSV (Comma-Separated Values)

Best for: Spreadsheets, simple data export/import, flat datasets

CSV is the simplest format for tabular data. It's extremely compact and widely supported. However, it lacks support for nested data structures, types, and metadata, making it unsuitable for complex LLM tasks.

Key limitations for LLMs:

• Cannot represent nested objects or arrays
• No type safety (everything is a string)
• Parsing issues with commas in values
• Lack of standard schema

TOON (Token-Oriented Object Notation)

Best for: LLM prompts, AI applications, token-sensitive contexts

TOON was specifically designed to address the limitations of traditional formats in LLM contexts. It combines the best aspects of tabular data representation with the flexibility of object notation. Learn more about what TOON is, the TOON specification, or how it compares to TONL.

Key advantages for LLMs:

• Keys defined once for entire arrays (like CSV headers)
• No quotes needed for simple strings
• Minimal syntax characters (just commas and colons)
• Tabular representation reduces redundancy by 30-60%
• Humans can easily read and edit it
• LLMs understand it as well as JSON

Practical Example

Here's the same data represented in all five formats:

[
  {
    "id": 1,
    "name": "Alice Johnson",
    "role": "Admin",
    "department": "Engineering"
  },
  {
    "id": 2,
    "name": "Bob Smith",
    "role": "User",
    "department": "Sales"
  },
  {
    "id": 3,
    "name": "Carol Davis",
    "role": "Manager",
    "department": "Marketing"
  }
]

- id: 1
  name: Alice Johnson
  role: Admin
  department: Engineering
- id: 2
  name: Bob Smith
  role: User
  department: Sales
- id: 3
  name: Carol Davis
  role: Manager
  department: Marketing

<?xml version="1.0"?>
<users>
  <user>
    <id>1</id>
    <name>Alice Johnson</name>
    <role>Admin</role>
    <department>Engineering</department>
  </user>
  <user>
    <id>2</id>
    <name>Bob Smith</name>
    <role>User</role>
    <department>Sales</department>
  </user>
  <user>
    <id>3</id>
    <name>Carol Davis</name>
    <role>Manager</role>
    <department>Marketing</department>
  </user>
</users>

[[users]]
id = 1
name = "Alice Johnson"
role = "Admin"
department = "Engineering"

[[users]]
id = 2
name = "Bob Smith"
role = "User"
department = "Sales"

[[users]]
id = 3
name = "Carol Davis"
role = "Manager"
department = "Marketing"

id,name,role,department
1,Alice Johnson,Admin,Engineering
2,Bob Smith,User,Sales
3,Carol Davis,Manager,Marketing

[3]{id,name,role,department}:
  1,Alice Johnson,Admin,Engineering
  2,Bob Smith,User,Sales
  3,Carol Davis,Manager,Marketing

When to Use Each Format

Use JSON when:

• Building RESTful APIs (industry standard)
• Working with web applications and JavaScript
• You need universal tool support
• Data volume is small and optimization isn't critical

Use YAML when:

• Writing configuration files for humans to edit
• Defining CI/CD pipelines (GitHub Actions, GitLab CI)
• Creating Kubernetes manifests
• Token count doesn't matter

Use XML when:

• Working with legacy enterprise systems
• You need complex document structures with attributes
• Industry standards require it (SOAP, RSS, etc.)
• Schema validation is critical

Use TOML when:

• Writing simple configuration files
• Data structure is mostly flat
• You want something more readable than JSON for configs
• Working with Rust projects (Cargo.toml)

Use CSV when:

• Data is strictly tabular and flat
• Exporting/importing to spreadsheets
• Simple data storage without nesting
• Legacy data exchange

Use TOON when:

• Sending data to LLM APIs (GPT, Claude, Gemini)
• You're hitting context window limits
• Reducing API costs is a priority. See our cost optimization guide.
• Working with tabular or array-heavy data
• You need both efficiency and readability

Cost Impact Analysis

For a typical dataset of 1,000 user records with 4 fields each:

• XML: ~65,000 tokens → $0.65 per request (GPT-4 Turbo input)
• JSON: ~42,000 tokens → $0.42 per request
• YAML: ~38,000 tokens → $0.38 per request
• TOML: ~35,000 tokens → $0.35 per request
• CSV: ~20,000 tokens → $0.20 per request (but no nesting)
• TOON: ~18,000 tokens → $0.18 per request ✨

Monthly savings at 10,000 requests: TOON saves $2,400 compared to JSON, or $4,700 compared to XML!

Final Verdict

Each format has its place in software development:

• JSON remains king for traditional APIs and web development
• YAML excels at human-edited configuration files
• XML is necessary for legacy systems and complex documents
• TOML is perfect for simple, flat configuration files
• CSV is great for flat data and spreadsheets
• TOON is the clear winner for LLM interactions and token-sensitive applications

If you're building AI applications, working with LLM APIs, or dealing with context window limitations, TOON offers unmatched efficiency without sacrificing readability. Try our free JSON to TOON converter to see the difference yourself! For advanced features, check out TONL or read our comparison guide.