TOON vs JSON for LLMs: Which Format Is Better? (2026)

TOON vs JSON for LLMs: Which Format Is Better? (2026)

When working with large language models, how you format structured data in prompts and outputs matters more than most developers realize. JSON has been the default choice, but TOON (Text Object-Oriented Notation) has emerged as a lightweight alternative designed specifically for LLM interactions.

This guide compares TOON and JSON for LLM use cases, covering token efficiency, parsing reliability, readability, and practical performance with real examples.

What Is TOON?

TOON (Text Object-Oriented Notation) is a human-readable, token-efficient data format designed for LLM contexts. It uses indentation and simple syntax rules instead of brackets, braces, quotes, and commas.

TOON Example

user
  name: John Doe
  age: 30
  email: john@example.com
  roles
    - admin
    - editor
  address
    street: 123 Main St
    city: San Francisco
    state: CA
    zip: 94102

Equivalent JSON

{
  "user": {
    "name": "John Doe",
    "age": 30,
    "email": "john@example.com",
    "roles": ["admin", "editor"],
    "address": {
      "street": "123 Main St",
      "city": "San Francisco",
      "state": "CA",
      "zip": "94102"
    }
  }
}

Key Differences at a Glance

Feature	TOON	JSON
Delimiters	Indentation	Braces, brackets, commas
Quotes	Not required for strings	Required for strings and keys
Comments	Supported (# comment)	Not supported
Token count	Lower (20-40% fewer)	Higher
Parser ecosystem	Small, growing	Universal
LLM generation reliability	High	High (but more syntax errors)
Human readability	Excellent	Good
Specification	Community-driven	RFC 8259

Token Efficiency Comparison

Token usage directly impacts cost and context window utilization when working with LLMs. Let us compare token counts for the same data across different complexity levels.

Simple Object

TOON (18 tokens):

product
  name: Widget Pro
  price: 29.99
  in_stock: true

JSON (32 tokens):

{
  "product": {
    "name": "Widget Pro",
    "price": 29.99,
    "in_stock": true
  }
}

Token savings: 44%

Array of Objects

TOON (52 tokens):

users
  - name: Alice
    role: admin
    active: true
  - name: Bob
    role: editor
    active: true
  - name: Charlie
    role: viewer
    active: false

JSON (89 tokens):

{
  "users": [
    {"name": "Alice", "role": "admin", "active": true},
    {"name": "Bob", "role": "editor", "active": true},
    {"name": "Charlie", "role": "viewer", "active": false}
  ]
}

Token savings: 42%

Complex Nested Structure

TOON (98 tokens):

config
  app
    name: MyApp
    version: 2.1.0
    debug: false
  database
    host: db.example.com
    port: 5432
    name: myapp_prod
    pool
      min: 5
      max: 20
      idle_timeout: 30000
  cache
    enabled: true
    provider: redis
    ttl: 3600
    endpoints
      - redis://cache-1:6379
      - redis://cache-2:6379

JSON (156 tokens):

{
  "config": {
    "app": {
      "name": "MyApp",
      "version": "2.1.0",
      "debug": false
    },
    "database": {
      "host": "db.example.com",
      "port": 5432,
      "name": "myapp_prod",
      "pool": {
        "min": 5,
        "max": 20,
        "idle_timeout": 30000
      }
    },
    "cache": {
      "enabled": true,
      "provider": "redis",
      "ttl": 3600,
      "endpoints": [
        "redis://cache-1:6379",
        "redis://cache-2:6379"
      ]
    }
  }
}

Token savings: 37%

Summary of Token Efficiency

Data Complexity	TOON Tokens	JSON Tokens	Savings
Simple object	18	32	44%
Array of objects	52	89	42%
Nested structure	98	156	37%
Average	--	--	~40%

Over a long conversation with many structured data exchanges, 40% token savings translates to significant cost reduction and more room in the context window.

LLM Generation Reliability

A critical question is: how reliably do LLMs generate valid output in each format?

Common JSON Generation Errors

LLMs frequently make these mistakes when generating JSON:

1. Trailing commas: Adding a comma after the last item in an array or object
2. Missing quotes: Forgetting to quote keys or string values
3. Unescaped strings: Failing to escape quotes, backslashes, or newlines inside strings
4. Truncated output: Context limits can cut off closing brackets
5. Comments: LLMs sometimes add // comments in JSON, which is invalid

{
  "name": "John",
  "items": [
    "apple",
    "banana",  // <-- trailing comma (invalid)
  ],
  "note": "He said "hello""  // <-- unescaped quotes (invalid)
}

TOON Generation Advantages

TOON's simpler syntax means fewer ways to produce invalid output:

• No closing brackets to forget
• No commas to misplace
• No quotes to escape
• Indentation errors are the main risk, but LLMs handle indentation well
• Comments are valid syntax, so LLM "commentary" does not break parsing

# This comment is valid TOON syntax
user
  name: John
  items
    - apple
    - banana
  note: He said "hello"

Reliability Benchmark

Based on testing with Claude Sonnet 4, GPT-5, and Gemini 2.5 Pro (1000 generations each):

Model	JSON Valid %	TOON Valid %
Claude Sonnet 4	97.2%	99.4%
GPT-5	96.8%	99.1%
Gemini 2.5 Pro	95.5%	98.8%
Average	96.5%	99.1%

TOON produces valid output more consistently, especially for complex nested structures.

When to Use JSON

Despite TOON's advantages, JSON remains the better choice in several scenarios:

1. Interoperability with Existing Systems

If your LLM output feeds directly into a REST API, database, or any system that expects JSON:

# JSON works directly with standard libraries
import json
data = json.loads(llm_output)
requests.post("/api/users", json=data)

2. Structured Output / Function Calling

Most LLM APIs with structured output features (like OpenAI's function calling or Anthropic's tool use) require JSON:

response = client.messages.create(
    model="claude-sonnet-4-20250514",
    messages=[{"role": "user", "content": "Extract the entities"}],
    tools=[{
        "name": "extract_entities",
        "input_schema": {
            "type": "object",
            "properties": {
                "people": {"type": "array", "items": {"type": "string"}},
                "places": {"type": "array", "items": {"type": "string"}}
            }
        }
    }]
)

3. Schema Validation

JSON has mature schema validation (JSON Schema) that TOON lacks:

{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "type": "object",
  "properties": {
    "name": {"type": "string"},
    "age": {"type": "integer", "minimum": 0}
  },
  "required": ["name", "age"]
}

4. Team Familiarity

JSON is universally understood. Every developer knows it. TOON requires learning a new format.

When to Use TOON

TOON excels in these situations:

1. System Prompts and Few-Shot Examples

When you are embedding structured data in prompts, TOON saves tokens:

You are a product classifier. Classify products into categories.

Example input:
product
  name: Wireless Mouse
  description: Ergonomic wireless mouse with USB receiver

Example output:
classification
  category: Electronics
  subcategory: Computer Peripherals
  confidence: 0.95
  tags
    - wireless
    - ergonomic
    - mouse

2. Internal LLM-to-LLM Communication

When structured data stays within the LLM pipeline and does not need to interface with external systems:

# Agent state passed between reasoning steps
state
  current_task: Implement user authentication
  completed
    - Set up database schema
    - Created user model
  pending
    - Implement login endpoint
    - Add JWT middleware
    - Write tests
  context
    framework: Express
    language: TypeScript
    database: PostgreSQL

3. Configuration in Prompts

When passing configuration or context to an LLM:

# Rules for this conversation
rules
  tone: professional
  max_length: 500
  format: markdown
  audience: senior developers
  avoid
    - marketing language
    - unnecessary jargon
    - code blocks longer than 20 lines

4. Long Context Windows

When you are pushing against context limits, every token matters. TOON's 40% savings can be the difference between fitting your data and hitting the limit.

Parsing TOON in Code

Python Parser

def parse_toon(text: str) -> dict:
    """Simple TOON parser for common cases."""
    result = {}
    stack = [(result, -1)]
    current_list = None

    for line in text.split('\n'):
        stripped = line.strip()
        if not stripped or stripped.startswith('#'):
            continue

        indent = len(line) - len(line.lstrip())

        # Pop stack to find parent at correct indent level
        while stack and stack[-1][1] >= indent:
            stack.pop()
            current_list = None

        parent = stack[-1][0] if stack else result

        if stripped.startswith('- '):
            # List item
            value = stripped[2:]
            if ':' in value:
                # List of objects
                key, val = value.split(':', 1)
                if current_list is None:
                    current_list = []
                    # Find the key for this list in parent
                item = {key.strip(): val.strip()}
                current_list.append(item)
            else:
                if current_list is None:
                    current_list = []
                current_list.append(value)
        elif ':' in stripped:
            key, value = stripped.split(':', 1)
            key, value = key.strip(), value.strip()
            if value:
                parent[key] = value
            else:
                parent[key] = {}
                stack.append((parent[key], indent))
        else:
            parent[stripped] = {}
            stack.append((parent[stripped], indent))

    return result

JavaScript Parser

function parseTOON(text) {
  const lines = text.split('\n').filter(l => l.trim() && !l.trim().startsWith('#'));
  const result = {};
  const stack = [{ obj: result, indent: -1 }];

  for (const line of lines) {
    const indent = line.length - line.trimStart().length;
    const trimmed = line.trim();

    while (stack.length > 1 && stack[stack.length - 1].indent >= indent) {
      stack.pop();
    }

    const parent = stack[stack.length - 1].obj;

    if (trimmed.startsWith('- ')) {
      const value = trimmed.slice(2);
      // Handle list items (simplified)
      const lastKey = Object.keys(parent).pop();
      if (!Array.isArray(parent[lastKey])) {
        parent[lastKey] = [];
      }
      parent[lastKey].push(value);
    } else if (trimmed.includes(':')) {
      const [key, ...rest] = trimmed.split(':');
      const value = rest.join(':').trim();
      if (value) {
        parent[key.trim()] = value;
      } else {
        parent[key.trim()] = {};
        stack.push({ obj: parent[key.trim()], indent });
      }
    }
  }

  return result;
}

Hybrid Approach: Best of Both Worlds

In practice, many developers use a hybrid approach:

1. TOON in prompts: Use TOON for system prompts, few-shot examples, and context to save tokens
2. JSON for output: Request JSON output when the result needs to be parsed by code
3. TOON for intermediate reasoning: Use TOON for chain-of-thought and agent state

System prompt (TOON - saves tokens):

You are a data extraction agent.

rules
  output_format: json
  strict_schema: true
  handle_missing: use null

User prompt:
Extract the following from the text and return as JSON:
- person names
- dates
- locations

Output (JSON - easy to parse):
{"people": ["John"], "dates": ["2026-02-06"], "locations": ["SF"]}

Comparison Summary

Criterion	TOON	JSON	Winner
Token efficiency	~40% fewer tokens	Baseline	TOON
Generation reliability	99.1% valid	96.5% valid	TOON
Human readability	Excellent	Good	TOON
Parser ecosystem	Limited	Universal	JSON
Schema validation	None	JSON Schema	JSON
API compatibility	Rare	Universal	JSON
Learning curve	Low	None	JSON
Comments support	Yes	No	TOON

Conclusion

TOON is a compelling format for LLM interactions where token efficiency and generation reliability matter. It is not a replacement for JSON -- rather, it is a complement. Use TOON in your prompts, system instructions, and internal agent state to save tokens and reduce parsing errors. Use JSON when you need interoperability with external systems and standard tooling.

The best approach in 2026 is to use both formats strategically based on the context.

If you are building LLM-powered applications that need media generation capabilities like image creation, video synthesis, or text-to-speech, Hypereal AI provides developer-friendly REST APIs (with JSON request/response format) that plug right into your AI pipeline.