Why should I use TOON instead of JSON for LLM prompts?

Using TOON format instead of JSON for LLM prompts provides significant benefits in token efficiency, cost savings, and readability.

Token reduction:

TOON eliminates verbose JSON syntax (quotes, braces, brackets, commas) that consume tokens but add little semantic value for AI understanding. A typical 1000-token JSON payload might become 400-700 tokens in TOON format.

Cost savings:

Since OpenAI, Anthropic, and other providers charge per token (both input and output), reducing token count by 30-60% directly reduces API costs by the same percentage. For applications making thousands of API calls daily, this can save hundreds or thousands of dollars monthly.

Context window efficiency:

With limited context windows (even with large models), using fewer tokens for data means more room for instructions, examples, and actual content.

Improved readability:

TOON is easier for humans to read and debug in logs and prompts, with less visual noise than JSON.

Faster parsing:

LLMs process fewer tokens more quickly, leading to slightly faster response times.

When to stick with JSON:

• When interfacing with APIs that require JSON
• When working with tools that specifically expect JSON format
• When you need strict schema validation
• When data contains many special characters that require escaping

TOON excels when you're embedding scraped data, database results, or structured information directly into LLM prompts where token efficiency matters more than formal structure.