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chore(benchmarks): finalize structure-awareness run

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Johann Schopplich
2025-11-07 10:33:46 +01:00
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@@ -180,7 +180,7 @@ Datasets with flat tabular structures where CSV is applicable.
```
<details>
<summary><strong>View detailed examples</strong></summary>
<summary><strong>Show detailed examples</strong></summary>
#### 📈 Time-series analytics data
@@ -317,10 +317,10 @@ repositories[3]{id,name,repo,description,createdAt,updatedAt,pushedAt,stars,watc
<!-- automd:file src="./benchmarks/results/retrieval-accuracy.md" -->
Benchmarks test LLM comprehension across different input formats using 201 data retrieval questions on 4 models.
Benchmarks test LLM comprehension across different input formats using 204 data retrieval questions on 4 models.
<details>
<summary><strong>View Dataset Catalog</strong></summary>
<summary><strong>Show Dataset Catalog</strong></summary>
#### Dataset Catalog
@@ -350,58 +350,67 @@ Benchmarks test LLM comprehension across different input formats using 201 data
Each format's overall performance, balancing accuracy against token cost:
```
TOON ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ 15.6 │ 68.7% acc │ 4,389 tokens
CSV ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ 15.3 │ 62.3% acc │ 4,080 tokens
JSON compact ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓░░░ 13.5 │ 67.2% acc │ 4,982 tokens
YAML ▓▓▓▓▓▓▓▓▓▓▓▓▓▓░░░░░░ 11.2 │ 66.7% acc │ 5,976 tokens
JSON ▓▓▓▓▓▓▓▓▓▓▓▓░░░░░░░░ 9.0 │ 65.7% acc │ 7,260 tokens
XML ▓▓▓▓▓▓▓▓▓▓░░░░░░░░░░ 8.1 │ 66.8% acc │ 8,251 tokens
TOON ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ 17.2 │ 75.5% acc │ 4,389 tokens
CSV ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓░ 16.6 │ 67.8% acc │ 4,080 tokens
JSON compact ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓░░░ 14.7 │ 73.3% acc │ 4,982 tokens
YAML ▓▓▓▓▓▓▓▓▓▓▓▓▓▓░░░░░░ 12.1 │ 72.4% acc │ 5,976 tokens
JSON ▓▓▓▓▓▓▓▓▓▓▓▓░░░░░░░░ 10.0 │ 72.4% acc │ 7,260 tokens
XML ▓▓▓▓▓▓▓▓▓▓░░░░░░░░░░ 8.4 │ 69.0% acc │ 8,251 tokens
```
TOON achieves **68.7%** accuracy (vs JSON's 65.7%) while using **39.5% fewer tokens**.
TOON achieves **75.5%** accuracy (vs JSON's 72.4%) while using **39.5% fewer tokens**.
#### Per-Model Accuracy
Accuracy across 4 LLMs on 201 data retrieval questions:
Accuracy across 4 LLMs on 204 data retrieval questions:
```
gpt-5-nano
→ TOON ██████████████████░░ 88.6% (178/201)
JSON compact ██████████████████░░ 88.1% (177/201)
CSV ██████████████████░░ 88.0% (88/100)
YAML █████████████████░░░ 84.6% (170/201)
XML ████████████████░░░░ 81.6% (164/201)
JSON ████████████████░░░░ 80.1% (161/201)
claude-haiku-4-5-20251001
YAML ██████████░░░░░░░░░░ 52.2% (105/201)
→ TOON ██████████░░░░░░░░░░ 50.7% (102/201)
JSON ██████████░░░░░░░░░░ 50.2% (101/201)
JSON compact ██████████░░░░░░░░░░ 49.8% (100/201)
XML ██████████░░░░░░░░░░ 49.3% (99/201)
CSV ████████░░░░░░░░░░░░ 39.0% (39/100)
→ TOON ████████████░░░░░░░░ 62.3% (127/204)
JSON ███████████░░░░░░░░░ 56.9% (116/204)
YAML ███████████░░░░░░░░░ 55.9% (114/204)
JSON compact ███████████░░░░░░░░░ 54.9% (112/204)
XML ███████████░░░░░░░░░ 54.9% (112/204)
CSV █████████░░░░░░░░░░░ 47.1% (49/104)
gemini-2.5-flash
XML █████████████████░░░ 86.1% (173/201)
→ TOON █████████████████░░░ 84.1% (169/201)
CSV ████████████████░░░░ 82.0% (82/100)
JSON compact ████████████████░░░░ 81.1% (163/201)
YAML ████████████████░░░░ 81.1% (163/201)
JSON ████████████████░░░░ 81.1% (163/201)
→ TOON ██████████████████░░ 91.2% (186/204)
YAML ██████████████████░░ 89.7% (183/204)
JSON compact ██████████████████░░ 87.7% (179/204)
JSON ██████████████████░░ 87.7% (179/204)
XML █████████████████░░░ 87.3% (178/204)
CSV █████████████████░░░ 85.6% (89/104)
gpt-5-nano
JSON compact ███████████████████░ 93.6% (191/204)
CSV ██████████████████░░ 90.4% (94/104)
JSON ██████████████████░░ 89.7% (183/204)
→ TOON ██████████████████░░ 89.2% (182/204)
YAML ██████████████████░░ 89.2% (182/204)
XML ████████████████░░░░ 81.4% (166/204)
grok-4-fast-non-reasoning
→ TOON ██████████░░░░░░░░░░ 51.2% (103/201)
JSON ██████████░░░░░░░░░░ 51.2% (103/201)
XML ██████████░░░░░░░░░░ 50.2% (101/201)
JSON compact ██████████░░░░░░░░░░ 49.8% (100/201)
YAML ██████████░░░░░░░░░░ 48.8% (98/201)
CSV ████████░░░░░░░░░░░░ 40.0% (40/100)
→ TOON ████████████░░░░░░░░ 59.3% (121/204)
JSON compact ███████████░░░░░░░░░ 56.9% (116/204)
JSON ███████████░░░░░░░░░ 55.4% (113/204)
YAML ███████████░░░░░░░░░ 54.9% (112/204)
XML ██████████░░░░░░░░░░ 52.5% (107/204)
CSV ██████████░░░░░░░░░░ 48.1% (50/104)
```
**Key tradeoff:** TOON achieves **68.7% accuracy** (vs JSON's 65.7%) while using **39.5% fewer tokens** on these datasets.
**Key tradeoff:** TOON achieves **75.5% accuracy** (vs JSON's 72.4%) while using **39.5% fewer tokens** on these datasets.
<details>
<summary><strong>Performance by dataset and model</strong></summary>
<summary><strong>Performance by dataset, model, and question type</strong></summary>
#### Performance by Question Type
| Question Type | TOON | JSON compact | JSON | YAML | XML | CSV |
| ------------- | ---- | ---- | ---- | ---- | ---- | ---- |
| Field Retrieval | 100.0% | 98.9% | 99.6% | 99.3% | 98.5% | 100.0% |
| Aggregation | 56.3% | 52.4% | 53.2% | 53.2% | 47.2% | 40.5% |
| Filtering | 58.9% | 58.3% | 54.2% | 53.1% | 50.5% | 49.1% |
| Structure Awareness | 89.0% | 85.0% | 82.0% | 85.0% | 79.0% | 84.4% |
#### Performance by Dataset
@@ -409,110 +418,110 @@ grok-4-fast-non-reasoning
| Format | Accuracy | Tokens | Correct/Total |
| ------ | -------- | ------ | ------------- |
| `toon` | 65.6% | 2,483 | 105/160 |
| `csv` | 62.5% | 2,337 | 100/160 |
| `json-compact` | 66.3% | 3,943 | 106/160 |
| `yaml` | 63.7% | 4,969 | 102/160 |
| `xml` | 67.5% | 7,314 | 108/160 |
| `json-pretty` | 62.5% | 6,347 | 100/160 |
| `csv` | 70.7% | 2,337 | 116/164 |
| `toon` | 72.0% | 2,483 | 118/164 |
| `json-compact` | 71.3% | 3,943 | 117/164 |
| `yaml` | 70.1% | 4,969 | 115/164 |
| `json-pretty` | 72.6% | 6,347 | 119/164 |
| `xml` | 70.7% | 7,314 | 116/164 |
##### E-commerce orders with nested structures
| Format | Accuracy | Tokens | Correct/Total |
| ------ | -------- | ------ | ------------- |
| `toon` | 75.6% | 7,197 | 121/160 |
| `json-compact` | 70.6% | 6,784 | 113/160 |
| `yaml` | 71.9% | 8,334 | 115/160 |
| `json-pretty` | 68.8% | 10,700 | 110/160 |
| `xml` | 71.9% | 12,013 | 115/160 |
| `toon` | 83.5% | 7,197 | 137/164 |
| `json-compact` | 79.3% | 6,784 | 130/164 |
| `yaml` | 78.7% | 8,334 | 129/164 |
| `json-pretty` | 78.7% | 10,700 | 129/164 |
| `xml` | 73.8% | 12,013 | 121/164 |
##### Time-series analytics data
| Format | Accuracy | Tokens | Correct/Total |
| ------ | -------- | ------ | ------------- |
| `csv` | 63.8% | 1,391 | 74/116 |
| `toon` | 66.4% | 1,513 | 77/116 |
| `json-compact` | 61.2% | 2,339 | 71/116 |
| `yaml` | 65.5% | 2,936 | 76/116 |
| `json-pretty` | 64.7% | 3,663 | 75/116 |
| `xml` | 65.5% | 4,374 | 76/116 |
| `toon` | 75.8% | 1,513 | 91/120 |
| `csv` | 72.5% | 1,391 | 87/120 |
| `json-compact` | 70.0% | 2,339 | 84/120 |
| `yaml` | 70.0% | 2,936 | 84/120 |
| `json-pretty` | 71.7% | 3,663 | 86/120 |
| `xml` | 71.7% | 4,374 | 86/120 |
##### Top 100 GitHub repositories
| Format | Accuracy | Tokens | Correct/Total |
| ------ | -------- | ------ | ------------- |
| `toon` | 63.7% | 8,745 | 79/124 |
| `csv` | 60.5% | 8,513 | 75/124 |
| `json-compact` | 56.5% | 11,455 | 70/124 |
| `yaml` | 53.2% | 13,129 | 66/124 |
| `json-pretty` | 53.2% | 15,145 | 66/124 |
| `xml` | 53.2% | 17,095 | 66/124 |
| `toon` | 64.4% | 8,745 | 85/132 |
| `csv` | 59.8% | 8,513 | 79/132 |
| `json-compact` | 60.6% | 11,455 | 80/132 |
| `yaml` | 61.4% | 13,129 | 81/132 |
| `json-pretty` | 59.1% | 15,145 | 78/132 |
| `xml` | 51.5% | 17,095 | 68/132 |
##### Semi-uniform event logs
| Format | Accuracy | Tokens | Correct/Total |
| ------ | -------- | ------ | ------------- |
| `json-compact` | 55.0% | 4,809 | 66/120 |
| `yaml` | 52.5% | 5,814 | 63/120 |
| `json-pretty` | 52.5% | 6,784 | 63/120 |
| `toon` | 45.8% | 5,764 | 55/120 |
| `xml` | 50.8% | 7,699 | 61/120 |
| `json-compact` | 67.5% | 4,809 | 81/120 |
| `yaml` | 63.3% | 5,814 | 76/120 |
| `toon` | 62.5% | 5,764 | 75/120 |
| `json-pretty` | 59.2% | 6,784 | 71/120 |
| `xml` | 55.0% | 7,699 | 66/120 |
##### Deeply nested configuration
| Format | Accuracy | Tokens | Correct/Total |
| ------ | -------- | ------ | ------------- |
| `json-compact` | 91.9% | 564 | 114/124 |
| `toon` | 92.7% | 631 | 115/124 |
| `yaml` | 91.9% | 673 | 114/124 |
| `json-pretty` | 91.9% | 919 | 114/124 |
| `xml` | 89.5% | 1,008 | 111/124 |
| `json-compact` | 91.4% | 564 | 106/116 |
| `toon` | 94.8% | 631 | 110/116 |
| `yaml` | 91.4% | 673 | 106/116 |
| `json-pretty` | 93.1% | 919 | 108/116 |
| `xml` | 91.4% | 1,008 | 106/116 |
#### Performance by Model
##### gpt-5-nano
| Format | Accuracy | Correct/Total |
| ------ | -------- | ------------- |
| `toon` | 88.6% | 178/201 |
| `json-compact` | 88.1% | 177/201 |
| `csv` | 88.0% | 88/100 |
| `yaml` | 84.6% | 170/201 |
| `xml` | 81.6% | 164/201 |
| `json-pretty` | 80.1% | 161/201 |
##### claude-haiku-4-5-20251001
| Format | Accuracy | Correct/Total |
| ------ | -------- | ------------- |
| `yaml` | 52.2% | 105/201 |
| `toon` | 50.7% | 102/201 |
| `json-pretty` | 50.2% | 101/201 |
| `json-compact` | 49.8% | 100/201 |
| `xml` | 49.3% | 99/201 |
| `csv` | 39.0% | 39/100 |
| `toon` | 62.3% | 127/204 |
| `json-pretty` | 56.9% | 116/204 |
| `yaml` | 55.9% | 114/204 |
| `json-compact` | 54.9% | 112/204 |
| `xml` | 54.9% | 112/204 |
| `csv` | 47.1% | 49/104 |
##### gemini-2.5-flash
| Format | Accuracy | Correct/Total |
| ------ | -------- | ------------- |
| `xml` | 86.1% | 173/201 |
| `toon` | 84.1% | 169/201 |
| `csv` | 82.0% | 82/100 |
| `json-compact` | 81.1% | 163/201 |
| `yaml` | 81.1% | 163/201 |
| `json-pretty` | 81.1% | 163/201 |
| `toon` | 91.2% | 186/204 |
| `yaml` | 89.7% | 183/204 |
| `json-compact` | 87.7% | 179/204 |
| `json-pretty` | 87.7% | 179/204 |
| `xml` | 87.3% | 178/204 |
| `csv` | 85.6% | 89/104 |
##### gpt-5-nano
| Format | Accuracy | Correct/Total |
| ------ | -------- | ------------- |
| `json-compact` | 93.6% | 191/204 |
| `csv` | 90.4% | 94/104 |
| `json-pretty` | 89.7% | 183/204 |
| `toon` | 89.2% | 182/204 |
| `yaml` | 89.2% | 182/204 |
| `xml` | 81.4% | 166/204 |
##### grok-4-fast-non-reasoning
| Format | Accuracy | Correct/Total |
| ------ | -------- | ------------- |
| `toon` | 51.2% | 103/201 |
| `json-pretty` | 51.2% | 103/201 |
| `xml` | 50.2% | 101/201 |
| `json-compact` | 49.8% | 100/201 |
| `yaml` | 48.8% | 98/201 |
| `csv` | 40.0% | 40/100 |
| `toon` | 59.3% | 121/204 |
| `json-compact` | 56.9% | 116/204 |
| `json-pretty` | 55.4% | 113/204 |
| `yaml` | 54.9% | 112/204 |
| `xml` | 52.5% | 107/204 |
| `csv` | 48.1% | 50/104 |
</details>
@@ -536,34 +545,39 @@ Six datasets designed to test different structural patterns:
#### Question Types
201 questions are generated dynamically across three categories:
204 questions are generated dynamically across four categories:
- **Field retrieval (36%)**: Direct value lookups or values that can be read straight off a record (including booleans and simple counts such as array lengths)
- **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 (37%)**: Dataset-level totals and averages plus single-condition filters (counts, sums, min/max comparisons)
- **Aggregation (31%)**: 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 (27%)**: Multi-condition queries requiring compound logic (AND constraints across fields)
- **Filtering (24%)**: 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`
#### Evaluation Process
1. **Format conversion**: Each dataset is converted to all 6 formats (TOON, JSON compact, XML, YAML, JSON, CSV).
1. **Format conversion**: Each dataset is converted to all 6 formats (TOON, JSON compact, JSON, YAML, XML, CSV).
2. **Query LLM**: Each model receives formatted data + question in a prompt and extracts the answer.
3. **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`).
#### Models & Configuration
- **Models tested**: `gpt-5-nano`, `claude-haiku-4-5-20251001`, `gemini-2.5-flash`, `grok-4-fast-non-reasoning`
- **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**: 201 questions × 6 formats × 4 models = 4,824 LLM calls
- **Total evaluations**: 204 questions × 6 formats × 4 models = 4,896 LLM calls
</details>

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@@ -1,7 +1,7 @@
Benchmarks test LLM comprehension across different input formats using 201 data retrieval questions on 4 models.
Benchmarks test LLM comprehension across different input formats using 204 data retrieval questions on 4 models.
<details>
<summary><strong>View Dataset Catalog</strong></summary>
<summary><strong>Show Dataset Catalog</strong></summary>
#### Dataset Catalog
@@ -31,58 +31,67 @@ Benchmarks test LLM comprehension across different input formats using 201 data
Each format's overall performance, balancing accuracy against token cost:
```
TOON ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ 15.6 │ 68.7% acc │ 4,389 tokens
CSV ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ 15.3 │ 62.3% acc │ 4,080 tokens
JSON compact ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓░░░ 13.5 │ 67.2% acc │ 4,982 tokens
YAML ▓▓▓▓▓▓▓▓▓▓▓▓▓▓░░░░░░ 11.2 │ 66.7% acc │ 5,976 tokens
JSON ▓▓▓▓▓▓▓▓▓▓▓▓░░░░░░░░ 9.0 │ 65.7% acc │ 7,260 tokens
XML ▓▓▓▓▓▓▓▓▓▓░░░░░░░░░░ 8.1 │ 66.8% acc │ 8,251 tokens
TOON ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ 17.2 │ 75.5% acc │ 4,389 tokens
CSV ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓░ 16.6 │ 67.8% acc │ 4,080 tokens
JSON compact ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓░░░ 14.7 │ 73.3% acc │ 4,982 tokens
YAML ▓▓▓▓▓▓▓▓▓▓▓▓▓▓░░░░░░ 12.1 │ 72.4% acc │ 5,976 tokens
JSON ▓▓▓▓▓▓▓▓▓▓▓▓░░░░░░░░ 10.0 │ 72.4% acc │ 7,260 tokens
XML ▓▓▓▓▓▓▓▓▓▓░░░░░░░░░░ 8.4 │ 69.0% acc │ 8,251 tokens
```
TOON achieves **68.7%** accuracy (vs JSON's 65.7%) while using **39.5% fewer tokens**.
TOON achieves **75.5%** accuracy (vs JSON's 72.4%) while using **39.5% fewer tokens**.
#### Per-Model Accuracy
Accuracy across 4 LLMs on 201 data retrieval questions:
Accuracy across 4 LLMs on 204 data retrieval questions:
```
gpt-5-nano
→ TOON ██████████████████░░ 88.6% (178/201)
JSON compact ██████████████████░░ 88.1% (177/201)
CSV ██████████████████░░ 88.0% (88/100)
YAML █████████████████░░░ 84.6% (170/201)
XML ████████████████░░░░ 81.6% (164/201)
JSON ████████████████░░░░ 80.1% (161/201)
claude-haiku-4-5-20251001
YAML ██████████░░░░░░░░░░ 52.2% (105/201)
→ TOON ██████████░░░░░░░░░░ 50.7% (102/201)
JSON ██████████░░░░░░░░░░ 50.2% (101/201)
JSON compact ██████████░░░░░░░░░░ 49.8% (100/201)
XML ██████████░░░░░░░░░░ 49.3% (99/201)
CSV ████████░░░░░░░░░░░░ 39.0% (39/100)
→ TOON ████████████░░░░░░░░ 62.3% (127/204)
JSON ███████████░░░░░░░░░ 56.9% (116/204)
YAML ███████████░░░░░░░░░ 55.9% (114/204)
JSON compact ███████████░░░░░░░░░ 54.9% (112/204)
XML ███████████░░░░░░░░░ 54.9% (112/204)
CSV █████████░░░░░░░░░░░ 47.1% (49/104)
gemini-2.5-flash
XML █████████████████░░░ 86.1% (173/201)
→ TOON █████████████████░░░ 84.1% (169/201)
CSV ████████████████░░░░ 82.0% (82/100)
JSON compact ████████████████░░░░ 81.1% (163/201)
YAML ████████████████░░░░ 81.1% (163/201)
JSON ████████████████░░░░ 81.1% (163/201)
→ TOON ██████████████████░░ 91.2% (186/204)
YAML ██████████████████░░ 89.7% (183/204)
JSON compact ██████████████████░░ 87.7% (179/204)
JSON ██████████████████░░ 87.7% (179/204)
XML █████████████████░░░ 87.3% (178/204)
CSV █████████████████░░░ 85.6% (89/104)
gpt-5-nano
JSON compact ███████████████████░ 93.6% (191/204)
CSV ██████████████████░░ 90.4% (94/104)
JSON ██████████████████░░ 89.7% (183/204)
→ TOON ██████████████████░░ 89.2% (182/204)
YAML ██████████████████░░ 89.2% (182/204)
XML ████████████████░░░░ 81.4% (166/204)
grok-4-fast-non-reasoning
→ TOON ██████████░░░░░░░░░░ 51.2% (103/201)
JSON ██████████░░░░░░░░░░ 51.2% (103/201)
XML ██████████░░░░░░░░░░ 50.2% (101/201)
JSON compact ██████████░░░░░░░░░░ 49.8% (100/201)
YAML ██████████░░░░░░░░░░ 48.8% (98/201)
CSV ████████░░░░░░░░░░░░ 40.0% (40/100)
→ TOON ████████████░░░░░░░░ 59.3% (121/204)
JSON compact ███████████░░░░░░░░░ 56.9% (116/204)
JSON ███████████░░░░░░░░░ 55.4% (113/204)
YAML ███████████░░░░░░░░░ 54.9% (112/204)
XML ██████████░░░░░░░░░░ 52.5% (107/204)
CSV ██████████░░░░░░░░░░ 48.1% (50/104)
```
**Key tradeoff:** TOON achieves **68.7% accuracy** (vs JSON's 65.7%) while using **39.5% fewer tokens** on these datasets.
**Key tradeoff:** TOON achieves **75.5% accuracy** (vs JSON's 72.4%) while using **39.5% fewer tokens** on these datasets.
<details>
<summary><strong>Performance by dataset and model</strong></summary>
<summary><strong>Performance by dataset, model, and question type</strong></summary>
#### Performance by Question Type
| Question Type | TOON | JSON compact | JSON | YAML | XML | CSV |
| ------------- | ---- | ---- | ---- | ---- | ---- | ---- |
| Field Retrieval | 100.0% | 98.9% | 99.6% | 99.3% | 98.5% | 100.0% |
| Aggregation | 56.3% | 52.4% | 53.2% | 53.2% | 47.2% | 40.5% |
| Filtering | 58.9% | 58.3% | 54.2% | 53.1% | 50.5% | 49.1% |
| Structure Awareness | 89.0% | 85.0% | 82.0% | 85.0% | 79.0% | 84.4% |
#### Performance by Dataset
@@ -90,110 +99,110 @@ grok-4-fast-non-reasoning
| Format | Accuracy | Tokens | Correct/Total |
| ------ | -------- | ------ | ------------- |
| `toon` | 65.6% | 2,483 | 105/160 |
| `csv` | 62.5% | 2,337 | 100/160 |
| `json-compact` | 66.3% | 3,943 | 106/160 |
| `yaml` | 63.7% | 4,969 | 102/160 |
| `xml` | 67.5% | 7,314 | 108/160 |
| `json-pretty` | 62.5% | 6,347 | 100/160 |
| `csv` | 70.7% | 2,337 | 116/164 |
| `toon` | 72.0% | 2,483 | 118/164 |
| `json-compact` | 71.3% | 3,943 | 117/164 |
| `yaml` | 70.1% | 4,969 | 115/164 |
| `json-pretty` | 72.6% | 6,347 | 119/164 |
| `xml` | 70.7% | 7,314 | 116/164 |
##### E-commerce orders with nested structures
| Format | Accuracy | Tokens | Correct/Total |
| ------ | -------- | ------ | ------------- |
| `toon` | 75.6% | 7,197 | 121/160 |
| `json-compact` | 70.6% | 6,784 | 113/160 |
| `yaml` | 71.9% | 8,334 | 115/160 |
| `json-pretty` | 68.8% | 10,700 | 110/160 |
| `xml` | 71.9% | 12,013 | 115/160 |
| `toon` | 83.5% | 7,197 | 137/164 |
| `json-compact` | 79.3% | 6,784 | 130/164 |
| `yaml` | 78.7% | 8,334 | 129/164 |
| `json-pretty` | 78.7% | 10,700 | 129/164 |
| `xml` | 73.8% | 12,013 | 121/164 |
##### Time-series analytics data
| Format | Accuracy | Tokens | Correct/Total |
| ------ | -------- | ------ | ------------- |
| `csv` | 63.8% | 1,391 | 74/116 |
| `toon` | 66.4% | 1,513 | 77/116 |
| `json-compact` | 61.2% | 2,339 | 71/116 |
| `yaml` | 65.5% | 2,936 | 76/116 |
| `json-pretty` | 64.7% | 3,663 | 75/116 |
| `xml` | 65.5% | 4,374 | 76/116 |
| `toon` | 75.8% | 1,513 | 91/120 |
| `csv` | 72.5% | 1,391 | 87/120 |
| `json-compact` | 70.0% | 2,339 | 84/120 |
| `yaml` | 70.0% | 2,936 | 84/120 |
| `json-pretty` | 71.7% | 3,663 | 86/120 |
| `xml` | 71.7% | 4,374 | 86/120 |
##### Top 100 GitHub repositories
| Format | Accuracy | Tokens | Correct/Total |
| ------ | -------- | ------ | ------------- |
| `toon` | 63.7% | 8,745 | 79/124 |
| `csv` | 60.5% | 8,513 | 75/124 |
| `json-compact` | 56.5% | 11,455 | 70/124 |
| `yaml` | 53.2% | 13,129 | 66/124 |
| `json-pretty` | 53.2% | 15,145 | 66/124 |
| `xml` | 53.2% | 17,095 | 66/124 |
| `toon` | 64.4% | 8,745 | 85/132 |
| `csv` | 59.8% | 8,513 | 79/132 |
| `json-compact` | 60.6% | 11,455 | 80/132 |
| `yaml` | 61.4% | 13,129 | 81/132 |
| `json-pretty` | 59.1% | 15,145 | 78/132 |
| `xml` | 51.5% | 17,095 | 68/132 |
##### Semi-uniform event logs
| Format | Accuracy | Tokens | Correct/Total |
| ------ | -------- | ------ | ------------- |
| `json-compact` | 55.0% | 4,809 | 66/120 |
| `yaml` | 52.5% | 5,814 | 63/120 |
| `json-pretty` | 52.5% | 6,784 | 63/120 |
| `toon` | 45.8% | 5,764 | 55/120 |
| `xml` | 50.8% | 7,699 | 61/120 |
| `json-compact` | 67.5% | 4,809 | 81/120 |
| `yaml` | 63.3% | 5,814 | 76/120 |
| `toon` | 62.5% | 5,764 | 75/120 |
| `json-pretty` | 59.2% | 6,784 | 71/120 |
| `xml` | 55.0% | 7,699 | 66/120 |
##### Deeply nested configuration
| Format | Accuracy | Tokens | Correct/Total |
| ------ | -------- | ------ | ------------- |
| `json-compact` | 91.9% | 564 | 114/124 |
| `toon` | 92.7% | 631 | 115/124 |
| `yaml` | 91.9% | 673 | 114/124 |
| `json-pretty` | 91.9% | 919 | 114/124 |
| `xml` | 89.5% | 1,008 | 111/124 |
| `json-compact` | 91.4% | 564 | 106/116 |
| `toon` | 94.8% | 631 | 110/116 |
| `yaml` | 91.4% | 673 | 106/116 |
| `json-pretty` | 93.1% | 919 | 108/116 |
| `xml` | 91.4% | 1,008 | 106/116 |
#### Performance by Model
##### gpt-5-nano
| Format | Accuracy | Correct/Total |
| ------ | -------- | ------------- |
| `toon` | 88.6% | 178/201 |
| `json-compact` | 88.1% | 177/201 |
| `csv` | 88.0% | 88/100 |
| `yaml` | 84.6% | 170/201 |
| `xml` | 81.6% | 164/201 |
| `json-pretty` | 80.1% | 161/201 |
##### claude-haiku-4-5-20251001
| Format | Accuracy | Correct/Total |
| ------ | -------- | ------------- |
| `yaml` | 52.2% | 105/201 |
| `toon` | 50.7% | 102/201 |
| `json-pretty` | 50.2% | 101/201 |
| `json-compact` | 49.8% | 100/201 |
| `xml` | 49.3% | 99/201 |
| `csv` | 39.0% | 39/100 |
| `toon` | 62.3% | 127/204 |
| `json-pretty` | 56.9% | 116/204 |
| `yaml` | 55.9% | 114/204 |
| `json-compact` | 54.9% | 112/204 |
| `xml` | 54.9% | 112/204 |
| `csv` | 47.1% | 49/104 |
##### gemini-2.5-flash
| Format | Accuracy | Correct/Total |
| ------ | -------- | ------------- |
| `xml` | 86.1% | 173/201 |
| `toon` | 84.1% | 169/201 |
| `csv` | 82.0% | 82/100 |
| `json-compact` | 81.1% | 163/201 |
| `yaml` | 81.1% | 163/201 |
| `json-pretty` | 81.1% | 163/201 |
| `toon` | 91.2% | 186/204 |
| `yaml` | 89.7% | 183/204 |
| `json-compact` | 87.7% | 179/204 |
| `json-pretty` | 87.7% | 179/204 |
| `xml` | 87.3% | 178/204 |
| `csv` | 85.6% | 89/104 |
##### gpt-5-nano
| Format | Accuracy | Correct/Total |
| ------ | -------- | ------------- |
| `json-compact` | 93.6% | 191/204 |
| `csv` | 90.4% | 94/104 |
| `json-pretty` | 89.7% | 183/204 |
| `toon` | 89.2% | 182/204 |
| `yaml` | 89.2% | 182/204 |
| `xml` | 81.4% | 166/204 |
##### grok-4-fast-non-reasoning
| Format | Accuracy | Correct/Total |
| ------ | -------- | ------------- |
| `toon` | 51.2% | 103/201 |
| `json-pretty` | 51.2% | 103/201 |
| `xml` | 50.2% | 101/201 |
| `json-compact` | 49.8% | 100/201 |
| `yaml` | 48.8% | 98/201 |
| `csv` | 40.0% | 40/100 |
| `toon` | 59.3% | 121/204 |
| `json-compact` | 56.9% | 116/204 |
| `json-pretty` | 55.4% | 113/204 |
| `yaml` | 54.9% | 112/204 |
| `xml` | 52.5% | 107/204 |
| `csv` | 48.1% | 50/104 |
</details>
@@ -217,33 +226,38 @@ Six datasets designed to test different structural patterns:
#### Question Types
201 questions are generated dynamically across three categories:
204 questions are generated dynamically across four categories:
- **Field retrieval (36%)**: Direct value lookups or values that can be read straight off a record (including booleans and simple counts such as array lengths)
- **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 (37%)**: Dataset-level totals and averages plus single-condition filters (counts, sums, min/max comparisons)
- **Aggregation (31%)**: 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 (27%)**: Multi-condition queries requiring compound logic (AND constraints across fields)
- **Filtering (24%)**: 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`
#### Evaluation Process
1. **Format conversion**: Each dataset is converted to all 6 formats (TOON, JSON compact, XML, YAML, JSON, CSV).
1. **Format conversion**: Each dataset is converted to all 6 formats (TOON, JSON compact, JSON, YAML, XML, CSV).
2. **Query LLM**: Each model receives formatted data + question in a prompt and extracts the answer.
3. **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`).
#### Models & Configuration
- **Models tested**: `gpt-5-nano`, `claude-haiku-4-5-20251001`, `gemini-2.5-flash`, `grok-4-fast-non-reasoning`
- **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**: 201 questions × 6 formats × 4 models = 4,824 LLM calls
- **Total evaluations**: 204 questions × 6 formats × 4 models = 4,896 LLM calls
</details>

2
benchmarks/results/token-efficiency.md
View File

@@ -77,7 +77,7 @@ Datasets with flat tabular structures where CSV is applicable.
```
<details>
<summary><strong>View detailed examples</strong></summary>
<summary><strong>Show detailed examples</strong></summary>
#### 📈 Time-series analytics data

14
benchmarks/scripts/token-efficiency-benchmark.ts
View File

@@ -88,7 +88,7 @@ function generateTotalLines(
baselineFormat?: { name: string, tokens: number },
) {
const separatorHalf = '─'.repeat(36)
const lines: string[] = [`${separatorHalf} Total ${separatorHalf}`]
const lines = [`${separatorHalf} Total ${separatorHalf}`]
if (baselineFormat) {
// Flat-only track with CSV baseline
@@ -300,12 +300,13 @@ const detailedExamples = results
displayData = { metrics: displayData.metrics.slice(0, ANALYTICS_METRICS_LIMIT) }
}
const separator = i < filtered.length - 1 ? '\n\n---' : ''
const emoji = DATASET_ICONS[result.dataset.name] || DEFAULT_DATASET_ICON
const json = result.formats.find(f => f.name === 'json-pretty')!
const toon = result.formats.find(f => f.name === 'toon')!
const separator = i < filtered.length - 1 ? '---' : ''
return `#### ${emoji} ${result.dataset.description}
return `
#### ${emoji} ${result.dataset.description}
**Savings:** ${json.savings.toLocaleString('en-US')} tokens (${json.savingsPercent.toFixed(1)}% reduction vs JSON)
@@ -319,7 +320,10 @@ ${JSON.stringify(displayData, undefined, 2)}
\`\`\`
${encode(displayData)}
\`\`\`${separator}`
\`\`\`
${separator}
`.trim()
})
.join('\n\n')
@@ -327,7 +331,7 @@ const markdown = `
${barChartSection}
<details>
<summary><strong>View detailed examples</strong></summary>
<summary><strong>Show detailed examples</strong></summary>
${detailedExamples}

2
benchmarks/src/evaluate.ts
View File

@@ -10,9 +10,9 @@ import { generateText } from 'ai'
* Models used for evaluation
*/
export const models: LanguageModelV2[] = [
openai('gpt-5-nano'),
anthropic('claude-haiku-4-5-20251001'),
google('gemini-2.5-flash'),
openai('gpt-5-nano'),
xai('grok-4-fast-non-reasoning'),
]

2
benchmarks/src/report.ts
View File

@@ -81,7 +81,7 @@ export function generateAccuracyReport(
Benchmarks test LLM comprehension across different input formats using ${totalQuestions} data retrieval questions on ${modelNames.length} ${modelNames.length === 1 ? 'model' : 'models'}.
<details>
<summary><strong>View Dataset Catalog</strong></summary>
<summary><strong>Show Dataset Catalog</strong></summary>
${generateDatasetCatalog(ACCURACY_DATASETS)}