TOON vs JSON: Cut Your AI Token Costs by 30%

If you're building AI applications, you've probably noticed that token costs add up fast. Every API call counts, and the format you use to structure data matters more than you think.

Enter TOON (Token-Oriented Object Notation), a format designed specifically for LLM contexts. Let's see how it stacks up against JSON and what you can actually save.

What is TOON?

TOON is a lightweight data serialization format optimized for token efficiency. Instead of the verbose syntax of JSON, TOON uses a more compact representation that LLMs can still parse easily.

JSON Example:

jsonCode

TOON Example:

toonCode

Simple difference, but it matters at scale.

The Token Math

Let's look at real numbers. Using GPT-4's tokenizer:

JSON format (95 characters):

→Tokens: ~28
→Includes: {, }, ", ,, extra spacing

TOON format (73 characters):

→Tokens: ~21
→Cleaner syntax, fewer delimiters

Savings: 25% fewer tokens per object

Where TOON Really Shines

Complex Nested Data

When you're working with arrays and nested objects, the savings multiply:

JSON (62 tokens):

jsonCode

TOON (43 tokens):

toonCode

Savings: 29% reduction in tokens

API Response Context

If you're feeding API responses into your prompts, this adds up quickly. A typical API response with 10 objects:

→JSON: ~1,200 tokens
→TOON: ~850 tokens
→You save 350 tokens per request

At $10 per million tokens (GPT-4 pricing), processing 10,000 requests:

→JSON cost: $120
→TOON cost: $85
→Monthly savings: $35 per workflow

When to Use TOON

Perfect for:

→System prompts with structured data
→Few-shot examples in your context
→Tool/function definitions
→Large dataset summaries
→Chain-of-thought reasoning steps

Stick with JSON when:

→You need strict schema validation
→Interfacing with external APIs (they expect JSON)
→Your team isn't familiar with YAML-like formats
→You're using standard JSON parsing libraries

Real-World Impact

Let's say you're building a RAG system that processes 1,000 documents daily, each with metadata in your prompt:

Daily token usage:

→JSON: 450,000 tokens
→TOON: 315,000 tokens

Monthly difference:

→4.05M tokens saved
→At current pricing: $40-80 saved monthly

For a single application. Multiply this across multiple workflows, and you're looking at meaningful cost reduction.

Implementation Tips

→Hybrid approach: Use TOON in prompts, JSON for API contracts
→Document clearly: Make sure your team knows which format to use where
→Test parsing: Verify your LLM correctly interprets TOON structures
→Monitor token usage: Track before/after metrics to confirm savings

Bottom Line

TOON isn't revolutionary, but it's practical. If you're watching token budgets (and you should be), switching internal data formats from JSON to TOON can cut costs by 25-35% without changing functionality.

The format is readable, LLMs handle it naturally, and the savings compound over time.

Worth testing in your next project. Tool to convert JSON a TOON