Choosing the right data format for your LLM prompts can have a massive impact on both cost and performance. While JSON is the industry standard for APIs, is it the best choice for prompting AI models? Let's compare JSON and TOON head-to-head.

The Contenders

JSON

Pros:

Universally understood by developers and machines.
Native support in almost every programming language.
Unambiguous syntax.

Cons:

High token overhead due to syntax characters.
Harder for LLMs to "repair" if malformed.
Verbose for repetitive data structures.

TOON

Pros:

Extremely token-efficient (30-60% savings).
Human-readable and cleaner to look at.
Optimized for LLM tokenizers.

Cons:

Newer format, less ecosystem support.
Requires conversion step for legacy systems.

Performance Benchmarks

We ran a series of benchmarks using OpenAI's gpt-4-turbo and Anthropic's claude-3-opus to compare the two formats.

1. Token Efficiency

We took a dataset of 100 e-commerce products containing ID, name, price, description, and category.

JSON: 4,200 tokens
TOON: 1,850 tokens

Winner: TOON (56% reduction)

2. Model Understanding

We asked the models to perform reasoning tasks on the data (e.g., "Find the average price of electronics").

JSON: 99% accuracy
TOON: 98.5% accuracy

Winner: Tie. Modern LLMs are smart enough to understand TOON just as well as JSON, despite it being a newer format. The slight difference is within the margin of error.

3. Generation Speed

When asking the model to output data in these formats:

JSON: Slower, as the model has to generate all the syntax characters.
TOON: Faster, as there are fewer tokens to generate.

Winner: TOON. Generating TOON output is faster and cheaper because there is less "syntax work" for the model to do.

When to Use Which?

Stick with JSON if:

You are integrating with strict legacy systems that only accept JSON.
The data volume is very small, so optimization isn't worth the effort.
You need absolute schema validation guarantees (though TOON has schemas too).

Switch to TOON if:

You are processing large datasets with LLMs.
You are hitting context window limits.
You want to reduce your monthly API bill.
You need faster response times from the model.

Final Verdict

For traditional software engineering, JSON remains king. But for the specific domain of LLM interaction and prompting, TOON is the superior choice. It respects the constraints of the medium (token limits and costs) while maintaining the structure needed for complex data. Learn more about optimizing API costs or see how TOON compares to TONL.

JSON vs TOON for Large Language Models