toon/benchmarks/results/retrieval-accuracy.md

Benchmarks test LLM comprehension across different input formats using 209 data retrieval questions on 4 models.

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Dataset Catalog

Dataset	Rows	Structure	CSV Support	Eligibility
Uniform employee records	100	uniform	✓	100%
E-commerce orders with nested structures	50	nested	✗	33%
Time-series analytics data	60	uniform	✓	100%
Top 100 GitHub repositories	100	uniform	✓	100%
Semi-uniform event logs	75	semi-uniform	✗	50%
Deeply nested configuration	11	deep	✗	0%
Valid complete dataset (control)	20	uniform	✓	100%
Array truncated: 3 rows removed from end	17	uniform	✓	100%
Extra rows added beyond declared length	23	uniform	✓	100%
Inconsistent field count (missing salary in row 10)	20	uniform	✓	100%
Missing required fields (no email in multiple rows)	20	uniform	✓	100%

Structure classes:

• uniform: All objects have identical fields with primitive values
• semi-uniform: Mix of uniform and non-uniform structures
• nested: Objects with nested structures (nested objects or arrays)
• deep: Highly nested with minimal tabular eligibility

CSV Support: ✓ (supported), ✗ (not supported – would require lossy flattening)

Eligibility: Percentage of arrays that qualify for TOON's tabular format (uniform objects with primitive values)

Efficiency Ranking (Accuracy per 1K Tokens)

Each format's overall performance, balancing accuracy against token cost:

TOON           ████████████████████   26.9  │  73.9% acc  │  2,744 tokens
JSON compact   █████████████████░░░   22.9  │  70.7% acc  │  3,081 tokens
YAML           ██████████████░░░░░░   18.6  │  69.0% acc  │  3,719 tokens
JSON           ███████████░░░░░░░░░   15.3  │  69.7% acc  │  4,545 tokens
XML            ██████████░░░░░░░░░░   13.0  │  67.1% acc  │  5,167 tokens

TOON achieves 73.9% accuracy (vs JSON's 69.7%) while using 39.6% fewer tokens.

Note on CSV: Excluded from ranking as it only supports 109 of 209 questions (flat tabular data only). While CSV is highly token-efficient for simple tabular data, it cannot represent nested structures that other formats handle.

Per-Model Accuracy

Accuracy across 4 LLMs on 209 data retrieval questions:

claude-haiku-4-5-20251001
→ TOON           ████████████░░░░░░░░    59.8% (125/209)
  JSON           ███████████░░░░░░░░░    57.4% (120/209)
  YAML           ███████████░░░░░░░░░    56.0% (117/209)
  XML            ███████████░░░░░░░░░    55.5% (116/209)
  JSON compact   ███████████░░░░░░░░░    55.0% (115/209)
  CSV            ██████████░░░░░░░░░░    50.5% (55/109)

gemini-2.5-flash
→ TOON           ██████████████████░░    87.6% (183/209)
  CSV            █████████████████░░░    86.2% (94/109)
  JSON compact   ████████████████░░░░    82.3% (172/209)
  YAML           ████████████████░░░░    79.4% (166/209)
  XML            ████████████████░░░░    79.4% (166/209)
  JSON           ███████████████░░░░░    77.0% (161/209)

gpt-5-nano
→ TOON           ██████████████████░░    90.9% (190/209)
  JSON compact   ██████████████████░░    90.9% (190/209)
  JSON           ██████████████████░░    89.0% (186/209)
  CSV            ██████████████████░░    89.0% (97/109)
  YAML           █████████████████░░░    87.1% (182/209)
  XML            ████████████████░░░░    80.9% (169/209)

grok-4-fast-non-reasoning
→ TOON           ███████████░░░░░░░░░    57.4% (120/209)
  JSON           ███████████░░░░░░░░░    55.5% (116/209)
  JSON compact   ███████████░░░░░░░░░    54.5% (114/209)
  YAML           ███████████░░░░░░░░░    53.6% (112/209)
  XML            ███████████░░░░░░░░░    52.6% (110/209)
  CSV            ██████████░░░░░░░░░░    52.3% (57/109)

[!TIP] Results Summary TOON achieves 73.9% accuracy (vs JSON's 69.7%) while using 39.6% fewer tokens on these datasets.

Performance by dataset, model, and question type

Performance by Question Type

Question Type	TOON	JSON compact	JSON	CSV	YAML	XML
Field Retrieval	99.6%	99.3%	99.3%	100.0%	98.2%	98.9%
Aggregation	54.4%	47.2%	48.8%	44.0%	47.6%	41.3%
Filtering	56.3%	57.3%	50.5%	49.1%	51.0%	47.9%
Structure Awareness	88.0%	83.0%	83.0%	85.9%	80.0%	80.0%
Structural Validation	70.0%	45.0%	50.0%	80.0%	60.0%	80.0%

Performance by Dataset

Uniform employee records

Format	Accuracy	Tokens	Correct/Total
csv	72.0%	2,352	118/164
toon	73.8%	2,518	121/164
json-compact	69.5%	3,953	114/164
yaml	68.3%	4,982	112/164
json-pretty	68.3%	6,360	112/164
xml	69.5%	7,324	114/164

E-commerce orders with nested structures

Format	Accuracy	Tokens	Correct/Total
toon	81.1%	7,232	133/164
json-compact	76.8%	6,794	126/164
yaml	75.6%	8,347	124/164
json-pretty	76.2%	10,713	125/164
xml	74.4%	12,023	122/164

Time-series analytics data

Format	Accuracy	Tokens	Correct/Total
csv	73.3%	1,406	88/120
toon	72.5%	1,548	87/120
json-compact	71.7%	2,349	86/120
yaml	71.7%	2,949	86/120
json-pretty	68.3%	3,676	82/120
xml	68.3%	4,384	82/120

Top 100 GitHub repositories

Format	Accuracy	Tokens	Correct/Total
toon	62.9%	8,779	83/132
csv	61.4%	8,527	81/132
yaml	59.8%	13,141	79/132
json-compact	55.3%	11,464	73/132
json-pretty	56.1%	15,157	74/132
xml	48.5%	17,105	64/132

Semi-uniform event logs

Format	Accuracy	Tokens	Correct/Total
json-compact	63.3%	4,819	76/120
toon	57.5%	5,799	69/120
json-pretty	59.2%	6,797	71/120
yaml	48.3%	5,827	58/120
xml	46.7%	7,709	56/120

Deeply nested configuration

Format	Accuracy	Tokens	Correct/Total
json-compact	92.2%	574	107/116
toon	95.7%	666	111/116
yaml	91.4%	686	106/116
json-pretty	94.0%	932	109/116
xml	92.2%	1,018	107/116

Valid complete dataset (control)

Format	Accuracy	Tokens	Correct/Total
toon	100.0%	544	4/4
json-compact	100.0%	795	4/4
yaml	100.0%	1,003	4/4
json-pretty	100.0%	1,282	4/4
csv	25.0%	492	1/4
xml	0.0%	1,467	0/4

Array truncated: 3 rows removed from end

Format	Accuracy	Tokens	Correct/Total
csv	100.0%	425	4/4
xml	100.0%	1,251	4/4
toon	0.0%	474	0/4
json-compact	0.0%	681	0/4
json-pretty	0.0%	1,096	0/4
yaml	0.0%	859	0/4

Extra rows added beyond declared length

Format	Accuracy	Tokens	Correct/Total
csv	100.0%	566	4/4
toon	75.0%	621	3/4
xml	100.0%	1,692	4/4
yaml	75.0%	1,157	3/4
json-compact	50.0%	917	2/4
json-pretty	50.0%	1,476	2/4

Inconsistent field count (missing salary in row 10)

Format	Accuracy	Tokens	Correct/Total
csv	75.0%	489	3/4
yaml	100.0%	996	4/4
toon	100.0%	1,019	4/4
json-compact	75.0%	790	3/4
xml	100.0%	1,458	4/4
json-pretty	75.0%	1,274	3/4

Missing required fields (no email in multiple rows)

Format	Accuracy	Tokens	Correct/Total
csv	100.0%	329	4/4
xml	100.0%	1,411	4/4
toon	75.0%	983	3/4
yaml	25.0%	960	1/4
json-pretty	25.0%	1,230	1/4
json-compact	0.0%	755	0/4

Performance by Model

claude-haiku-4-5-20251001

Format	Accuracy	Correct/Total
toon	59.8%	125/209
json-pretty	57.4%	120/209
yaml	56.0%	117/209
xml	55.5%	116/209
json-compact	55.0%	115/209
csv	50.5%	55/109

gemini-2.5-flash

Format	Accuracy	Correct/Total
toon	87.6%	183/209
csv	86.2%	94/109
json-compact	82.3%	172/209
yaml	79.4%	166/209
xml	79.4%	166/209
json-pretty	77.0%	161/209

gpt-5-nano

Format	Accuracy	Correct/Total
toon	90.9%	190/209
json-compact	90.9%	190/209
json-pretty	89.0%	186/209
csv	89.0%	97/109
yaml	87.1%	182/209
xml	80.9%	169/209

grok-4-fast-non-reasoning

Format	Accuracy	Correct/Total
toon	57.4%	120/209
json-pretty	55.5%	116/209
json-compact	54.5%	114/209
yaml	53.6%	112/209
xml	52.6%	110/209
csv	52.3%	57/109

What's Being Measured

This benchmark tests LLM comprehension and data retrieval accuracy across different input formats. Each LLM receives formatted data and must answer questions about it. This does not test the model's ability to generate TOON output – only to read and understand it.

Datasets Tested

Eleven datasets designed to test different structural patterns and validation capabilities:

Primary datasets:

1. Tabular (100 employee records): Uniform objects with identical fields – optimal for TOON's tabular format.
2. Nested (50 e-commerce orders): Complex structures with nested customer objects and item arrays.
3. Analytics (60 days of metrics): Time-series data with dates and numeric values.
4. GitHub (100 repositories): Real-world data from top GitHub repos by stars.
5. Event Logs (75 logs): Semi-uniform data with ~50% flat logs and ~50% with nested error objects.
6. Nested Config (1 configuration): Deeply nested configuration with minimal tabular eligibility.

Structural validation datasets:

7. Control: Valid complete dataset (baseline for validation)
8. Truncated: Array with 3 rows removed from end (tests [N] length detection)
9. Extra rows: Array with 3 additional rows beyond declared length
10. Width mismatch: Inconsistent field count (missing salary in row 10)
11. Missing fields: Systematic field omissions (no email in multiple rows)

Question Types

209 questions are generated dynamically across five categories:

• Field retrieval (33%): Direct value lookups or values that can be read straight off a record (including booleans and simple counts such as array lengths)

  • Example: "What is Alice's salary?" → 75000
  • Example: "How many items are in order ORD-0042?" → 3
  • Example: "What is the customer name for order ORD-0042?" → John Doe

• Aggregation (30%): Dataset-level totals and averages plus single-condition filters (counts, sums, min/max comparisons)

  • Example: "How many employees work in Engineering?" → 17
  • Example: "What is the total revenue across all orders?" → 45123.50
  • Example: "How many employees have salary > 80000?" → 23

• Filtering (23%): Multi-condition queries requiring compound logic (AND constraints across fields)

  • Example: "How many employees in Sales have salary > 80000?" → 5
  • Example: "How many active employees have more than 10 years of experience?" → 8

• Structure awareness (12%): Tests format-native structural affordances (TOON's [N] count and {fields}, CSV's header row)

  • Example: "How many employees are in the dataset?" → 100
  • Example: "List the field names for employees" → id, name, email, department, salary, yearsExperience, active
  • Example: "What is the department of the last employee?" → Sales

• Structural validation (2%): Tests ability to detect incomplete, truncated, or corrupted data using structural metadata

  • Example: "Is this data complete and valid?" → YES (control dataset) or NO (corrupted datasets)
  • Tests TOON's [N] length validation and {fields} consistency checking
  • Demonstrates CSV's lack of structural validation capabilities

Evaluation Process

1. Format conversion: Each dataset is converted to all 6 formats (TOON, JSON compact, JSON, CSV, YAML, XML).
2. Query LLM: Each model receives formatted data + question in a prompt and extracts the answer.
3. Validate deterministically: Answers are validated using type-aware comparison (e.g., 50000 = $50,000, Engineering = engineering, 2025-01-01 = January 1, 2025) without requiring an LLM judge.

Models & Configuration

• Models tested: claude-haiku-4-5-20251001, gemini-2.5-flash, gpt-5-nano, grok-4-fast-non-reasoning
• Token counting: Using gpt-tokenizer with o200k_base encoding (GPT-5 tokenizer)
• Temperature: Not set (models use their defaults)
• Total evaluations: 209 questions × 6 formats × 4 models = 5,016 LLM calls