TOON vs JSON: Performance Comparison
Comprehensive benchmarks showing how TOON outperforms JSON in token efficiency, cost savings, and LLM retrieval accuracy.
Executive Summary
Our comprehensive testing across multiple datasets and LLM models shows:
- 30-60% token reduction compared to JSON
- 73.9% vs 69.7% retrieval accuracy (TOON wins)
- 40-50% cost savings on GPT-4 API calls
- 2.5x more data fits in same context window
Token Efficiency Benchmarks
Test Dataset: 100-row Product Catalog
Benchmark Results by Dataset Type
Cost Savings Analysis
Real-World Scenario: RAG System
A production RAG system processes 10,000 queries/day, each with 1,000 rows of context data.
Retrieval Accuracy Comparison
Test Methodology
We tested 1,000 queries against a knowledge base of 10,000 structured records, measuring exact-match retrieval accuracy.
Why TOON performs better: The schema-aware format helps LLMs better understand the structure of data, reducing ambiguity in field identification and improving context retention.
When JSON Might Be Better
While TOON wins for tabular data, JSON remains superior for:
- Deeply nested objects: JSON's hierarchical structure is more natural
- Varying schemas: When each object has different fields
- Single objects: No token benefit for non-array data
- Inter-service APIs: JSON's universal support makes it better for traditional APIs
- Browser JavaScript: Native JSON.parse() support
Conclusion
For LLM applications dealing with structured, tabular data, TOON is the clear winner:
- ✓30-60% fewer tokens = lower costs and more context
- ✓Better accuracy = higher quality AI responses
- ✓Massive cost savings = ROI in days for high-volume apps
See the Savings Yourself
Convert your JSON data to TOON and compare token counts instantly
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