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docs: overhaul retrieval accuracy benchmark
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Johann Schopplich
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benchmarks/results/accuracy/models/gemini-2.5-flash
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benchmarks/results/accuracy/models/gemini-2.5-flash
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benchmarks/results/accuracy/models/gpt-5-nano
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benchmarks/results/accuracy/models/gpt-5-nano
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### Retrieval Accuracy
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Accuracy across **3 LLMs** on **159 data retrieval questions**:
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```
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gpt-5-nano
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toon ████████████████████ 99.4% (158/159)
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yaml ███████████████████░ 95.0% (151/159)
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csv ██████████████████░░ 92.5% (147/159)
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json ██████████████████░░ 92.5% (147/159)
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xml ██████████████████░░ 91.2% (145/159)
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claude-haiku-4-5
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toon ███████████████░░░░░ 75.5% (120/159)
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xml ███████████████░░░░░ 75.5% (120/159)
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csv ███████████████░░░░░ 75.5% (120/159)
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json ███████████████░░░░░ 75.5% (120/159)
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yaml ███████████████░░░░░ 74.2% (118/159)
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gemini-2.5-flash
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xml ██████████████████░░ 91.8% (146/159)
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csv █████████████████░░░ 86.2% (137/159)
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toon █████████████████░░░ 84.9% (135/159)
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json ████████████████░░░░ 81.8% (130/159)
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yaml ████████████████░░░░ 78.6% (125/159)
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```
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**Advantage:** TOON achieves **86.6% accuracy** (vs JSON's 83.2%) while using **46.3% fewer tokens**.
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<details>
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<summary><strong>Performance by dataset and model</strong></summary>
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#### Performance by Dataset
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##### Uniform employee records (TOON optimal format)
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| Format | Accuracy | Tokens | Correct/Total |
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| ------ | -------- | ------ | ------------- |
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| `toon` | 87.4% | 2.483 | 152/174 |
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| `csv` | 82.8% | 2.337 | 144/174 |
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| `yaml` | 83.9% | 4.969 | 146/174 |
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| `json` | 83.9% | 6.347 | 146/174 |
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| `xml` | 88.5% | 7.314 | 154/174 |
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##### E-commerce orders with nested structures
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| Format | Accuracy | Tokens | Correct/Total |
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| ------ | -------- | ------ | ------------- |
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| `toon` | 90.9% | 5.967 | 120/132 |
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| `csv` | 93.9% | 6.735 | 124/132 |
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| `yaml` | 87.1% | 7.328 | 115/132 |
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| `json` | 87.9% | 9.694 | 116/132 |
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| `xml` | 93.2% | 10.992 | 123/132 |
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##### Time-series analytics data
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| Format | Accuracy | Tokens | Correct/Total |
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| ------ | -------- | ------ | ------------- |
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| `csv` | 89.7% | 1.393 | 78/87 |
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| `toon` | 88.5% | 1.515 | 77/87 |
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| `yaml` | 83.9% | 2.938 | 73/87 |
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| `json` | 88.5% | 3.665 | 77/87 |
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| `xml` | 85.1% | 4.376 | 74/87 |
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##### Top 100 GitHub repositories
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| Format | Accuracy | Tokens | Correct/Total |
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| ------ | -------- | ------ | ------------- |
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| `toon` | 76.2% | 8.745 | 64/84 |
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| `csv` | 69.0% | 8.513 | 58/84 |
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| `yaml` | 71.4% | 13.129 | 60/84 |
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| `json` | 69.0% | 15.145 | 58/84 |
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| `xml` | 71.4% | 17.095 | 60/84 |
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#### Performance by Model
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##### gpt-5-nano
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| Format | Accuracy | Correct/Total |
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| ------ | -------- | ------------- |
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| `toon` | 99.4% | 158/159 |
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| `yaml` | 95.0% | 151/159 |
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| `csv` | 92.5% | 147/159 |
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| `json` | 92.5% | 147/159 |
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| `xml` | 91.2% | 145/159 |
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##### claude-haiku-4-5
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| Format | Accuracy | Correct/Total |
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| ------ | -------- | ------------- |
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| `toon` | 75.5% | 120/159 |
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| `xml` | 75.5% | 120/159 |
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| `csv` | 75.5% | 120/159 |
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| `json` | 75.5% | 120/159 |
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| `yaml` | 74.2% | 118/159 |
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##### gemini-2.5-flash
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| Format | Accuracy | Correct/Total |
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| ------ | -------- | ------------- |
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| `xml` | 91.8% | 146/159 |
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| `csv` | 86.2% | 137/159 |
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| `toon` | 84.9% | 135/159 |
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| `json` | 81.8% | 130/159 |
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| `yaml` | 78.6% | 125/159 |
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</details>
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<details>
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<summary><strong>How the benchmark works</strong></summary>
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#### What's Being Measured
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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 model's ability to generate TOON output).
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#### Datasets Tested
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Four datasets designed to test different structural patterns:
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1. **Tabular** (100 employee records): Uniform objects with identical fields – optimal for TOON's tabular format.
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2. **Nested** (50 e-commerce orders): Complex structures with nested customer objects and item arrays.
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3. **Analytics** (60 days of metrics): Time-series data with dates and numeric values.
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4. **GitHub** (100 repositories): Real-world data from top GitHub repos by stars.
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#### Question Types
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159 questions are generated dynamically across three categories:
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- **Field retrieval (50%)**: Direct value lookups
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- Example: "What is Alice's salary?" → `75000`
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- Example: "What is the customer name for order ORD-0042?" → `John Doe`
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- **Aggregation (25%)**: Counting and summation tasks
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- Example: "How many employees work in Engineering?" → `17`
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- Example: "What is the total revenue across all orders?" → `45123.50`
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- **Filtering (25%)**: Conditional queries
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- Example: "How many employees in Sales have salary > 80000?" → `5`
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- Example: "How many orders have total > 400?" → `12`
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#### Evaluation Process
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1. **Format conversion:** Each dataset is converted to all 5 formats (TOON, JSON, YAML, CSV, XML).
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2. **Query LLM**: Each model receives formatted data + question in a prompt and extracts the answer.
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4. **Validate with LLM-as-judge**: `gpt-5-nano` validates if the answer is semantically correct (e.g., `50000` = `$50,000`, `Engineering` = `engineering`, `2025-01-01` = `January 1, 2025`).
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#### Models & Configuration
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- **Models tested**: `gpt-5-nano`, `claude-haiku-4-5`, `gemini-2.5-flash`
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- **Token counting**: Using `gpt-tokenizer` with `o200k_base` encoding (GPT-5 tokenizer)
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- **Temperature**: 0 (for non-reasoning models)
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- **Total evaluations**: 159 questions × 5 formats × 3 models = 2,385 LLM calls
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</details>
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{
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"formatResults": [
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{
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"format": "toon",
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"accuracy": 0.8658280922431866,
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"totalTokens": 4678,
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"averageLatency": 5321,
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"correctCount": 413,
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"totalCount": 477
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},
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{
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"format": "xml",
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"accuracy": 0.8616352201257862,
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"totalTokens": 9944,
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"averageLatency": 6035,
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"correctCount": 411,
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"totalCount": 477
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},
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{
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"format": "csv",
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"accuracy": 0.8469601677148847,
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"totalTokens": 4745,
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"averageLatency": 6551,
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"correctCount": 404,
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"totalCount": 477
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},
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{
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"format": "json",
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"accuracy": 0.8322851153039832,
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"totalTokens": 8713,
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"averageLatency": 7981,
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"correctCount": 397,
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"totalCount": 477
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},
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{
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"format": "yaml",
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"accuracy": 0.8259958071278826,
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"totalTokens": 7091,
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"averageLatency": 5561,
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"correctCount": 394,
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"totalCount": 477
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}
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],
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"questions": 159,
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"models": [
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"gpt-5-nano",
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"claude-haiku-4-5",
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"gemini-2.5-flash"
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],
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"datasets": [
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{
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"name": "tabular",
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"description": "Uniform employee records (TOON optimal format)"
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},
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{
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"name": "nested",
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"description": "E-commerce orders with nested structures"
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},
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{
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"name": "analytics",
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"description": "Time-series analytics data"
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},
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{
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"name": "github",
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"description": "Top 100 GitHub repositories"
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}
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],
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"tokenCounts": {
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"json-tabular": 6347,
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"json-nested": 9694,
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"json-analytics": 3665,
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"json-github": 15145,
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"toon-tabular": 2483,
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"toon-nested": 5967,
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"toon-analytics": 1515,
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"toon-github": 8745,
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"csv-tabular": 2337,
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"csv-nested": 6735,
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"csv-analytics": 1393,
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"csv-github": 8513,
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"xml-tabular": 7314,
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"xml-nested": 10992,
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"xml-analytics": 4376,
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"xml-github": 17095,
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"yaml-tabular": 4969,
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"yaml-nested": 7328,
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"yaml-analytics": 2938,
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"yaml-github": 13129
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},
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"timestamp": "2025-10-28T07:39:09.360Z"
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}
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