TOON vs JSON for LLMs: Which Format Is Better? (2026)
Comparing data serialization formats for LLM prompting and output
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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:
- Trailing commas: Adding a comma after the last item in an array or object
- Missing quotes: Forgetting to quote keys or string values
- Unescaped strings: Failing to escape quotes, backslashes, or newlines inside strings
- Truncated output: Context limits can cut off closing brackets
- 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:
- TOON in prompts: Use TOON for system prompts, few-shot examples, and context to save tokens
- JSON for output: Request JSON output when the result needs to be parsed by code
- 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.
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