Benchmarks
Retrieval Accuracy
Benchmarks test LLM comprehension using 309 data retrieval questions on gpt-5-nano (Azure OpenAI).
Dataset Catalog
| Dataset | Rows | Structure | Description |
|---|---|---|---|
| Unified benchmark | 5 | mixed | Users, config, logs, metadata - mixed structures |
Structure: Mixed uniform tables + nested objects
Structure: Mixed uniform tables + nested objects
Questions: 309 total (field retrieval, aggregation, filtering, structure awareness)
Efficiency Ranking (Accuracy per 10K Tokens)
Each format ranked by efficiency (accuracy percentage per 10,000 tokens):
ZON ████████████████████ 1430.6 acc%/10K │ 99.0% acc │ 692 tokens 👑
CSV ███████████████████░ 1386.5 acc%/10K │ 99.0% acc │ 714 tokens
JSON compact ████████████████░░░░ 1143.4 acc%/10K │ 91.7% acc │ 802 tokens
TOON ████████████████░░░░ 1132.7 acc%/10K │ 99.0% acc │ 874 tokens
JSON ██████████░░░░░░░░░░ 744.6 acc%/10K │ 96.8% acc │ 1,300 tokens
Efficiency score = (Accuracy % / Tokens) x 10,000. Higher is better.
ZON achieves 99.0% accuracy while using 20.8% fewer tokens than TOON and 13.7% fewer than Minified JSON.
Per-Model Comparison
Accuracy on the unified dataset with gpt-5-nano:
gpt-5-nano (Azure OpenAI)
→ ZON ████████████████████ 99.0% (306/309) │ 692 tokens
TOON ████████████████████ 99.0% (306/309) │ 874 tokens
CSV ████████████████████ 99.0% (306/309) │ 714 tokens
JSON ███████████████████░ 96.8% (299/309) │ 1,300 tokens
JSON compact ██████████████████░░ 91.7% (283/309) │ 802 tokens
ZON matches TOON's 99.0% accuracy while using 20.8% fewer tokens.
⚡️ Token Efficiency (vs Compact JSON)
| Tokenizer | ZON Savings | vs TOON | vs CSV |
|---|---|---|---|
| GPT-4o | -23.8% 👑 | -36.1% | -12.9% |
| Claude 3.5 | -21.3% 👑 | -26.0% | -9.9% |
| Llama 3 | -16.5% 👑 | -26.6% | -9.2% |
Note: ZON is the only human-readable format that consistently beats CSV in token count while maintaining full structural fidelity.
Token Efficiency Benchmark
Tokenizers: GPT-4o (o200k), Claude 3.5 (Anthropic), Llama 3 (Meta)
Dataset: Unified benchmark dataset, Large Complex Nested Dataset
BYTE SIZES:
CSV: 1,384 bytes
ZON: 1,399 bytes
TOON: 1,665 bytes
JSON (compact): 1,854 bytes
YAML: 2,033 bytes
JSON (formatted): 2,842 bytes
XML: 3,235 bytes
Unified Dataset
GPT-4o (o200k):
ZON █████████░░░░░░░░░░░ 513 tokens 👑
CSV ██████████░░░░░░░░░░ 534 tokens (+4.1%)
JSON (cmp) ███████████░░░░░░░░░ 589 tokens (+12.9%)
TOON ███████████░░░░░░░░░ 614 tokens (+19.7%)
YAML █████████████░░░░░░░ 728 tokens (+41.9%)
JSON format ████████████████████ 939 tokens (+45.4%)
XML ████████████████████ 1,093 tokens (+113.1%)
Claude 3.5 (Anthropic):
CSV ██████████░░░░░░░░░░ 544 tokens 👑
ZON ██████████░░░░░░░░░░ 548 tokens (+0.7%)
TOON ██████████░░░░░░░░░░ 570 tokens (+4.0%)
JSON (cmp) ███████████░░░░░░░░░ 596 tokens (+8.1%)
YAML ████████████░░░░░░░░ 641 tokens (+17.0%)
JSON format ████████████████████ 914 tokens (+40.0%)
XML ████████████████████ 1,104 tokens (+101.5%)
Llama 3 (Meta):
ZON ██████████░░░░░░░░░░ 696 tokens 👑
CSV ██████████░░░░░░░░░░ 728 tokens (+4.6%)
JSON (cmp) ███████████░░░░░░░░░ 760 tokens (+8.4%)
TOON ███████████░░░░░░░░░ 784 tokens (+12.6%)
YAML █████████████░░░░░░░ 894 tokens (+28.4%)
JSON format ████████████████████ 1,225 tokens (+43.1%)
XML ████████████████████ 1,392 tokens (+100.0%)
Large Complex Nested Dataset
gpt-4o (o200k):
ZON █████████░░░░░░░░░░░ 143,661 tokens 👑
CSV ██████████░░░░░░░░░░ 164,919 tokens (+14.8%)
JSON (cmp) ███████████░░░░░░░░░ 188,604 tokens (+23.8%)
TOON █████████████░░░░░░░ 224,940 tokens (+56.6%)
YAML █████████████░░░░░░░ 224,938 tokens (+56.6%)
JSON format ████████████████████ 284,132 tokens (+97.8%)
XML ████████████████████ 335,239 tokens (+133.4%)
claude 3.5 (anthropic):
ZON █████████░░░░░░░░░░░ 145,652 tokens 👑
CSV ██████████░░░░░░░░░░ 161,701 tokens (+11.0%)
JSON (cmp) ███████████░░░░░░░░░ 185,136 tokens (+21.3%)
TOON ████████████░░░░░░░░ 196,893 tokens (+35.2%)
YAML ████████████░░░░░░░░ 196,892 tokens (+35.2%)
JSON format ████████████████████ 274,149 tokens (+88.2%)
XML ████████████████████ 327,274 tokens (+124.7%)
llama 3 (meta):
ZON ██████████░░░░░░░░░░ 230,838 tokens 👑
CSV ███████████░░░░░░░░░ 254,181 tokens (+10.1%)
JSON (cmp) ████████████░░░░░░░░ 276,405 tokens (+16.5%)
TOON █████████████░░░░░░░ 314,824 tokens (+36.4%)
YAML █████████████░░░░░░░ 314,820 tokens (+36.4%)
JSON format ████████████████████ 407,488 tokens (+76.5%)
XML ████████████████████ 480,125 tokens (+108.0%)
Overall Summary:
GPT-4o (o200k):
ZON Wins: 2/2 datasets
Total tokens across all datasets:
ZON: 147,267
CSV: 165,647 (+12.5%)
JSON (cmp): 189,193 (+28.4%)
TOON: 225,510 (+53.1%)
ZON vs TOON: -34.7% fewer tokens
ZON vs JSON: -22.2% fewer tokens
Claude 3.5 (Anthropic):
ZON Wins: 1/2 datasets
Total tokens across all datasets:
ZON: 149,281
CSV: 162,245 (+8.7%)
JSON (cmp): 185,732 (+24.4%)
TOON: 197,463 (+32.3%)
ZON vs TOON: -24.4% fewer tokens
ZON vs JSON: -19.6% fewer tokens
Llama 3 (Meta):
ZON Wins: 2/2 datasets
Total tokens across all datasets:
ZON: 234,623
CSV: 254,909 (+8.7%)
JSON (cmp): 277,165 (+18.1%)
TOON: 315,608 (+34.5%)
ZON vs TOON: -25.7% fewer tokens
ZON vs JSON: -15.3% fewer tokens
Key Insights:
-
ZON wins on all Llama 3 and GPT-4o tests (best token efficiency across both datasets).
-
Claude shows CSV has slight edge (0.2%) on simple tabular data, but ZON dominates on complex nested data.
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Average savings: 25-35% vs TOON, 15-28% vs JSON across all tokenizers.
-
ZON wins on all Llama 3 and GPT-4o tests (best token efficiency across both datasets).
-
ZON is 2nd on Claude (CSV wins by only 0.2%, ZON still beats TOON by 4.6%).
-
ZON consistently outperforms TOON on every tokenizer (from 4.6% up to 34.8% savings).
Key Insight: ZON is the only format that wins or nearly wins across all models & datasets.