Free JSON to TOON converter.
Convert JSON to compact TOON format for LLM prompts, or decode TOON back to JSON. TOON reduces token usage while preserving the full JSON data model. Everything runs locally in your browser.
Token-efficient, LLM-friendly JSON encoding
TOON (Token-Oriented Object Notation) is a compact encoding of the JSON data model designed for AI prompts. It combines YAML-like indentation for nested objects with CSV-style tables for uniform arrays, reducing token counts by ~40% compared to formatted JSON.
- Encode JSON to TOON to reduce LLM token usage.
- Decode TOON back to JSON to verify round-trip accuracy.
- Explicit array lengths
[N]and field headers{fields}for LLM guardrails. - All conversion happens locally, not on a server.
Important: This is a data formatting tool for LLM workflows. For production systems, use the official @toon-format/toon library in your backend or pipelines.
JSON ⇄ TOON Converter
Convert JSON data to compact TOON format for LLM prompts.
Runs locally. Your data never leaves your browser.
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How it works
Convert between JSON and TOON in two modes.
Switch between encode and decode modes. In encode mode, paste JSON and get compact TOON. In decode mode, paste TOON and verify the JSON round-trip.
1. Encode JSON to TOON
Paste any JSON object or array. The encoder automatically detects uniform arrays and converts them to compact tabular format, reducing token usage by ~40% for typical datasets.
2. Use TOON in LLM prompts
Wrap the TOON output in a ```toon code block and include it in your system messages, few-shot examples, or data payloads for faster, cheaper inference.
3. Decode TOON back to JSON
Switch to decode mode to parse TOON strings back into JSON. This verifies lossless round-trips and helps debug TOON syntax when working with model outputs.
TOON best practices
Use TOON effectively in LLM workflows.
TOON excels with uniform arrays of objects. These guidelines help you maximize token savings and LLM parsing accuracy.
Wrap TOON in code blocks for prompts
When sending TOON to models, use ```toon code blocks in your prompts. This signals the format and helps models parse the structure more reliably.
Best for uniform arrays
TOON achieves maximum token savings with uniform arrays of objects (same fields across all items). For deeply nested or highly variable structures, compact JSON may be more efficient.
Verify round-trips before production
Always test decode mode to ensure your data survives a round-trip. TOON is lossless and deterministic, but complex nested structures should be validated before relying on them in pipelines.
Use tab delimiters for extra savings
The official library supports tab-delimited mode for even lower token counts. Check the TOON spec for advanced encoding options.
FAQ
JSON to TOON converter questions, answered.
Learn what this tool does (and doesn't) do when working with TOON format.
Is TOON conversion lossless?
Yes. TOON encodes the same data model as JSON: objects, arrays, strings, numbers, booleans, and null. Every JSON payload can be encoded to TOON and decoded back without losing information.
Are conversions sent to a server?
No. Encoding and decoding both happen entirely in your browser using the official @toon-format/toon library. We don't send data, secrets, or payloads to any server.
How much do I save compared to JSON?
TOON typically saves ~40% tokens on datasets with uniform arrays of objects. For deeply nested or non-uniform structures, savings are smaller. Check the benchmarks for detailed comparisons.
Should I use this for production pipelines?
This tool is great for debugging, demos, and local experimentation. For production, integrate the official library directly in your backend or data pipelines for automated encoding/decoding.
JSON ⇄ TOON Converter
Cut LLM token costs with compact encoding
Test how your data transforms into TOON, verify round-trips, and see exactly how many tokens you save before updating your prompts.
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