diff --git a/README.md b/README.md
index 49f006b..7937585 100644
--- a/README.md
+++ b/README.md
@@ -57,26 +57,30 @@ The benchmarks test datasets that favor TOON's strengths (uniform tabular data).
### Token Efficiency
```
-⭐ GitHub Repositories ██████████████░░░░░░░░░░░ 8,745 tokens
- vs JSON: 15,145 (-42.3%)
- vs YAML: 13,129 (-33.4%)
- vs XML: 17,095 (-48.8%)
+⭐ GitHub Repositories ██████████████░░░░░░░░░░░ 8,745 tokens
+ vs JSON (-42.3%) 15,145
+ vs JSON compact (-23.7%) 11,455
+ vs YAML (-33.4%) 13,129
+ vs XML (-48.8%) 17,095
-📈 Daily Analytics ██████████░░░░░░░░░░░░░░░ 4,507 tokens
- vs JSON: 10,977 (-58.9%)
- vs YAML: 8,810 (-48.8%)
- vs XML: 13,128 (-65.7%)
+📈 Daily Analytics ██████████░░░░░░░░░░░░░░░ 4,507 tokens
+ vs JSON (-58.9%) 10,977
+ vs JSON compact (-35.7%) 7,013
+ vs YAML (-48.8%) 8,810
+ vs XML (-65.7%) 13,128
-🛒 E-Commerce Order ████████████████░░░░░░░░░ 166 tokens
- vs JSON: 257 (-35.4%)
- vs YAML: 197 (-15.7%)
- vs XML: 271 (-38.7%)
+🛒 E-Commerce Order ████████████████░░░░░░░░░ 166 tokens
+ vs JSON (-35.4%) 257
+ vs JSON compact (-2.9%) 171
+ vs YAML (-15.7%) 197
+ vs XML (-38.7%) 271
─────────────────────────────────────────────────────────────────────
-Total █████████████░░░░░░░░░░░░ 13,418 tokens
- vs JSON: 26,379 (-49.1%)
- vs YAML: 22,136 (-39.4%)
- vs XML: 30,494 (-56.0%)
+Total ██████████████░░░░░░░░░░░ 13,418 tokens
+ vs JSON (-49.1%) 26,379
+ vs JSON compact (-28.0%) 18,639
+ vs YAML (-39.4%) 22,136
+ vs XML (-56.0%) 30,494
```
@@ -218,7 +222,7 @@ metrics[5]{date,views,clicks,conversions,revenue,bounceRate}:
> [!NOTE]
-> Measured with [`gpt-tokenizer`](https://github.com/niieani/gpt-tokenizer) using `o200k_base` encoding (used by GPT-5 and other modern models). Savings will vary across models and tokenizers.
+> Token savings are measured against formatted JSON (2-space indentation) as the primary baseline. Additional comparisons include compact JSON (minified), YAML, and XML to provide a comprehensive view across common data formats. Measured with [`gpt-tokenizer`](https://github.com/niieani/gpt-tokenizer) using `o200k_base` encoding (GPT-5 tokenizer). Actual savings vary by model and tokenizer.
@@ -228,35 +232,39 @@ Accuracy across **4 LLMs** on 154 data retrieval questions:
```
gpt-5-nano
-→ toon ███████████████████░ 96.1% (148/154)
- csv ██████████████████░░ 90.3% (139/154)
- yaml ██████████████████░░ 89.0% (137/154)
- json ██████████████████░░ 87.7% (135/154)
- xml █████████████████░░░ 83.8% (129/154)
+→ TOON ███████████████████░ 96.1% (148/154)
+ CSV ██████████████████░░ 91.6% (141/154)
+ YAML ██████████████████░░ 91.6% (141/154)
+ JSON compact ██████████████████░░ 91.6% (141/154)
+ XML █████████████████░░░ 87.0% (134/154)
+ JSON █████████████████░░░ 86.4% (133/154)
claude-haiku-4-5-20251001
- yaml ██████████░░░░░░░░░░ 49.4% (76/154)
-→ toon ██████████░░░░░░░░░░ 48.1% (74/154)
- csv ██████████░░░░░░░░░░ 48.1% (74/154)
- json █████████░░░░░░░░░░░ 47.4% (73/154)
- xml █████████░░░░░░░░░░░ 46.8% (72/154)
+ JSON ██████████░░░░░░░░░░ 50.0% (77/154)
+ YAML ██████████░░░░░░░░░░ 49.4% (76/154)
+→ TOON ██████████░░░░░░░░░░ 48.7% (75/154)
+ XML ██████████░░░░░░░░░░ 48.1% (74/154)
+ CSV █████████░░░░░░░░░░░ 47.4% (73/154)
+ JSON compact █████████░░░░░░░░░░░ 44.2% (68/154)
gemini-2.5-flash
- csv ██████████████████░░ 87.7% (135/154)
- xml █████████████████░░░ 85.1% (131/154)
-→ toon █████████████████░░░ 83.8% (129/154)
- json ████████████████░░░░ 78.6% (121/154)
- yaml ███████████████░░░░░ 76.6% (118/154)
+ CSV ██████████████████░░ 87.7% (135/154)
+ XML ██████████████████░░ 87.7% (135/154)
+→ TOON █████████████████░░░ 86.4% (133/154)
+ YAML ████████████████░░░░ 79.9% (123/154)
+ JSON compact ████████████████░░░░ 79.9% (123/154)
+ JSON ███████████████░░░░░ 76.6% (118/154)
grok-4-fast-non-reasoning
-→ toon ██████████░░░░░░░░░░ 48.7% (75/154)
- json ██████████░░░░░░░░░░ 48.1% (74/154)
- xml █████████░░░░░░░░░░░ 47.4% (73/154)
- yaml █████████░░░░░░░░░░░ 46.8% (72/154)
- csv █████████░░░░░░░░░░░ 45.5% (70/154)
+→ TOON ██████████░░░░░░░░░░ 49.4% (76/154)
+ JSON ██████████░░░░░░░░░░ 48.7% (75/154)
+ XML █████████░░░░░░░░░░░ 46.1% (71/154)
+ YAML █████████░░░░░░░░░░░ 46.1% (71/154)
+ JSON compact █████████░░░░░░░░░░░ 45.5% (70/154)
+ CSV █████████░░░░░░░░░░░ 44.2% (68/154)
```
-**Key tradeoff:** TOON achieves **69.2% accuracy** (vs JSON's 65.4%) while using **46.3% fewer tokens** on these datasets.
+**Key tradeoff:** TOON achieves **70.1% accuracy** (vs JSON's 65.4%) while using **46.3% fewer tokens** on these datasets.
Performance by dataset and model
@@ -267,41 +275,45 @@ grok-4-fast-non-reasoning
| Format | Accuracy | Tokens | Correct/Total |
| ------ | -------- | ------ | ------------- |
-| `csv` | 67.0% | 2,337 | 134/200 |
-| `toon` | 66.5% | 2,483 | 133/200 |
-| `yaml` | 65.5% | 4,969 | 131/200 |
-| `json` | 63.5% | 6,347 | 127/200 |
-| `xml` | 66.5% | 7,314 | 133/200 |
+| `csv` | 65.5% | 2,337 | 131/200 |
+| `toon` | 67.5% | 2,483 | 135/200 |
+| `json-compact` | 65.5% | 3,943 | 131/200 |
+| `yaml` | 68.5% | 4,969 | 137/200 |
+| `xml` | 69.5% | 7,314 | 139/200 |
+| `json-pretty` | 64.5% | 6,347 | 129/200 |
##### E-commerce orders with nested structures
| Format | Accuracy | Tokens | Correct/Total |
| ------ | -------- | ------ | ------------- |
| `toon` | 78.8% | 5,967 | 126/160 |
-| `csv` | 71.9% | 6,735 | 115/160 |
-| `yaml` | 71.9% | 7,328 | 115/160 |
-| `json` | 73.1% | 9,694 | 117/160 |
-| `xml` | 73.8% | 10,992 | 118/160 |
+| `csv` | 76.3% | 6,735 | 122/160 |
+| `json-compact` | 70.6% | 5,962 | 113/160 |
+| `yaml` | 72.5% | 7,328 | 116/160 |
+| `json-pretty` | 76.9% | 9,694 | 123/160 |
+| `xml` | 73.1% | 10,992 | 117/160 |
##### Time-series analytics data
| Format | Accuracy | Tokens | Correct/Total |
| ------ | -------- | ------ | ------------- |
-| `csv` | 67.6% | 1,393 | 92/136 |
-| `toon` | 67.6% | 1,515 | 92/136 |
-| `yaml` | 64.7% | 2,938 | 88/136 |
-| `json` | 68.4% | 3,665 | 93/136 |
-| `xml` | 66.2% | 4,376 | 90/136 |
+| `toon` | 68.4% | 1,515 | 93/136 |
+| `csv` | 65.4% | 1,393 | 89/136 |
+| `json-compact` | 64.7% | 2,341 | 88/136 |
+| `yaml` | 66.2% | 2,938 | 90/136 |
+| `json-pretty` | 64.7% | 3,665 | 88/136 |
+| `xml` | 66.9% | 4,376 | 91/136 |
##### Top 100 GitHub repositories
| Format | Accuracy | Tokens | Correct/Total |
| ------ | -------- | ------ | ------------- |
-| `csv` | 64.2% | 8,513 | 77/120 |
-| `toon` | 62.5% | 8,745 | 75/120 |
-| `yaml` | 57.5% | 13,129 | 69/120 |
-| `json` | 55.0% | 15,145 | 66/120 |
-| `xml` | 53.3% | 17,095 | 64/120 |
+| `toon` | 65.0% | 8,745 | 78/120 |
+| `csv` | 62.5% | 8,513 | 75/120 |
+| `json-compact` | 58.3% | 11,455 | 70/120 |
+| `yaml` | 56.7% | 13,129 | 68/120 |
+| `xml` | 55.8% | 17,095 | 67/120 |
+| `json-pretty` | 52.5% | 15,145 | 63/120 |
#### Performance by Model
@@ -310,40 +322,44 @@ grok-4-fast-non-reasoning
| Format | Accuracy | Correct/Total |
| ------ | -------- | ------------- |
| `toon` | 96.1% | 148/154 |
-| `csv` | 90.3% | 139/154 |
-| `yaml` | 89.0% | 137/154 |
-| `json` | 87.7% | 135/154 |
-| `xml` | 83.8% | 129/154 |
+| `csv` | 91.6% | 141/154 |
+| `yaml` | 91.6% | 141/154 |
+| `json-compact` | 91.6% | 141/154 |
+| `xml` | 87.0% | 134/154 |
+| `json-pretty` | 86.4% | 133/154 |
##### claude-haiku-4-5-20251001
| Format | Accuracy | Correct/Total |
| ------ | -------- | ------------- |
+| `json-pretty` | 50.0% | 77/154 |
| `yaml` | 49.4% | 76/154 |
-| `toon` | 48.1% | 74/154 |
-| `csv` | 48.1% | 74/154 |
-| `json` | 47.4% | 73/154 |
-| `xml` | 46.8% | 72/154 |
+| `toon` | 48.7% | 75/154 |
+| `xml` | 48.1% | 74/154 |
+| `csv` | 47.4% | 73/154 |
+| `json-compact` | 44.2% | 68/154 |
##### gemini-2.5-flash
| Format | Accuracy | Correct/Total |
| ------ | -------- | ------------- |
| `csv` | 87.7% | 135/154 |
-| `xml` | 85.1% | 131/154 |
-| `toon` | 83.8% | 129/154 |
-| `json` | 78.6% | 121/154 |
-| `yaml` | 76.6% | 118/154 |
+| `xml` | 87.7% | 135/154 |
+| `toon` | 86.4% | 133/154 |
+| `yaml` | 79.9% | 123/154 |
+| `json-compact` | 79.9% | 123/154 |
+| `json-pretty` | 76.6% | 118/154 |
##### grok-4-fast-non-reasoning
| Format | Accuracy | Correct/Total |
| ------ | -------- | ------------- |
-| `toon` | 48.7% | 75/154 |
-| `json` | 48.1% | 74/154 |
-| `xml` | 47.4% | 73/154 |
-| `yaml` | 46.8% | 72/154 |
-| `csv` | 45.5% | 70/154 |
+| `toon` | 49.4% | 76/154 |
+| `json-pretty` | 48.7% | 75/154 |
+| `xml` | 46.1% | 71/154 |
+| `yaml` | 46.1% | 71/154 |
+| `json-compact` | 45.5% | 70/154 |
+| `csv` | 44.2% | 68/154 |
@@ -383,7 +399,7 @@ Four datasets designed to test different structural patterns (all contain arrays
#### Evaluation Process
-1. **Format conversion**: Each dataset is converted to all 5 formats (TOON, CSV, XML, JSON, YAML).
+1. **Format conversion**: Each dataset is converted to all 6 formats (TOON, CSV, XML, YAML, JSON, JSON compact).
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`).
@@ -392,7 +408,7 @@ Four datasets designed to test different structural patterns (all contain arrays
- **Models tested**: `gpt-5-nano`, `claude-haiku-4-5-20251001`, `gemini-2.5-flash`, `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**: 154 questions × 5 formats × 4 models = 3,080 LLM calls
+- **Total evaluations**: 154 questions × 6 formats × 4 models = 3,696 LLM calls
diff --git a/benchmarks/results/accuracy/models/claude-haiku-4-5-20251001 b/benchmarks/results/accuracy/models/claude-haiku-4-5-20251001
index 9acbdcd..4aeb7b3 100644
--- a/benchmarks/results/accuracy/models/claude-haiku-4-5-20251001
+++ b/benchmarks/results/accuracy/models/claude-haiku-4-5-20251001
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/benchmarks/results/accuracy/models/gemini-2.5-flash b/benchmarks/results/accuracy/models/gemini-2.5-flash
index 7a4fc3a..cb999c7 100644
--- a/benchmarks/results/accuracy/models/gemini-2.5-flash
+++ b/benchmarks/results/accuracy/models/gemini-2.5-flash
@@ -1 +1 @@
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diff --git a/benchmarks/results/accuracy/models/gpt-5-nano b/benchmarks/results/accuracy/models/gpt-5-nano
index 9741bd9..4409451 100644
--- a/benchmarks/results/accuracy/models/gpt-5-nano
+++ b/benchmarks/results/accuracy/models/gpt-5-nano
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/benchmarks/results/accuracy/models/grok-4-fast-non-reasoning b/benchmarks/results/accuracy/models/grok-4-fast-non-reasoning
index a17dd49..751144b 100644
--- a/benchmarks/results/accuracy/models/grok-4-fast-non-reasoning
+++ b/benchmarks/results/accuracy/models/grok-4-fast-non-reasoning
@@ -1 +1 @@
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cted":"1","actual":"1","isCorrect":true,"inputTokens":8839,"outputTokens":1,"latencyMs":632.4096670001745},{"questionId":"q154","format":"csv","model":"grok-4-fast-non-reasoning","expected":"1","actual":"5","isCorrect":false,"inputTokens":8609,"outputTokens":1,"latencyMs":1502.6806669998914},{"questionId":"q154","format":"xml","model":"grok-4-fast-non-reasoning","expected":"1","actual":"2","isCorrect":false,"inputTokens":17149,"outputTokens":1,"latencyMs":618.4989590002224},{"questionId":"q154","format":"yaml","model":"grok-4-fast-non-reasoning","expected":"1","actual":"5","isCorrect":false,"inputTokens":13233,"outputTokens":1,"latencyMs":621.8662089998834}]
\ No newline at end of file
diff --git a/benchmarks/results/retrieval-accuracy.md b/benchmarks/results/retrieval-accuracy.md
index 437efcf..1a2c137 100644
--- a/benchmarks/results/retrieval-accuracy.md
+++ b/benchmarks/results/retrieval-accuracy.md
@@ -4,35 +4,39 @@ Accuracy across **4 LLMs** on 154 data retrieval questions:
```
gpt-5-nano
-→ toon ███████████████████░ 96.1% (148/154)
- csv ██████████████████░░ 90.3% (139/154)
- yaml ██████████████████░░ 89.0% (137/154)
- json ██████████████████░░ 87.7% (135/154)
- xml █████████████████░░░ 83.8% (129/154)
+→ TOON ███████████████████░ 96.1% (148/154)
+ CSV ██████████████████░░ 91.6% (141/154)
+ YAML ██████████████████░░ 91.6% (141/154)
+ JSON compact ██████████████████░░ 91.6% (141/154)
+ XML █████████████████░░░ 87.0% (134/154)
+ JSON █████████████████░░░ 86.4% (133/154)
claude-haiku-4-5-20251001
- yaml ██████████░░░░░░░░░░ 49.4% (76/154)
-→ toon ██████████░░░░░░░░░░ 48.1% (74/154)
- csv ██████████░░░░░░░░░░ 48.1% (74/154)
- json █████████░░░░░░░░░░░ 47.4% (73/154)
- xml █████████░░░░░░░░░░░ 46.8% (72/154)
+ JSON ██████████░░░░░░░░░░ 50.0% (77/154)
+ YAML ██████████░░░░░░░░░░ 49.4% (76/154)
+→ TOON ██████████░░░░░░░░░░ 48.7% (75/154)
+ XML ██████████░░░░░░░░░░ 48.1% (74/154)
+ CSV █████████░░░░░░░░░░░ 47.4% (73/154)
+ JSON compact █████████░░░░░░░░░░░ 44.2% (68/154)
gemini-2.5-flash
- csv ██████████████████░░ 87.7% (135/154)
- xml █████████████████░░░ 85.1% (131/154)
-→ toon █████████████████░░░ 83.8% (129/154)
- json ████████████████░░░░ 78.6% (121/154)
- yaml ███████████████░░░░░ 76.6% (118/154)
+ CSV ██████████████████░░ 87.7% (135/154)
+ XML ██████████████████░░ 87.7% (135/154)
+→ TOON █████████████████░░░ 86.4% (133/154)
+ YAML ████████████████░░░░ 79.9% (123/154)
+ JSON compact ████████████████░░░░ 79.9% (123/154)
+ JSON ███████████████░░░░░ 76.6% (118/154)
grok-4-fast-non-reasoning
-→ toon ██████████░░░░░░░░░░ 48.7% (75/154)
- json ██████████░░░░░░░░░░ 48.1% (74/154)
- xml █████████░░░░░░░░░░░ 47.4% (73/154)
- yaml █████████░░░░░░░░░░░ 46.8% (72/154)
- csv █████████░░░░░░░░░░░ 45.5% (70/154)
+→ TOON ██████████░░░░░░░░░░ 49.4% (76/154)
+ JSON ██████████░░░░░░░░░░ 48.7% (75/154)
+ XML █████████░░░░░░░░░░░ 46.1% (71/154)
+ YAML █████████░░░░░░░░░░░ 46.1% (71/154)
+ JSON compact █████████░░░░░░░░░░░ 45.5% (70/154)
+ CSV █████████░░░░░░░░░░░ 44.2% (68/154)
```
-**Key tradeoff:** TOON achieves **69.2% accuracy** (vs JSON's 65.4%) while using **46.3% fewer tokens** on these datasets.
+**Key tradeoff:** TOON achieves **70.1% accuracy** (vs JSON's 65.4%) while using **46.3% fewer tokens** on these datasets.
Performance by dataset and model
@@ -43,41 +47,45 @@ grok-4-fast-non-reasoning
| Format | Accuracy | Tokens | Correct/Total |
| ------ | -------- | ------ | ------------- |
-| `csv` | 67.0% | 2,337 | 134/200 |
-| `toon` | 66.5% | 2,483 | 133/200 |
-| `yaml` | 65.5% | 4,969 | 131/200 |
-| `json` | 63.5% | 6,347 | 127/200 |
-| `xml` | 66.5% | 7,314 | 133/200 |
+| `csv` | 65.5% | 2,337 | 131/200 |
+| `toon` | 67.5% | 2,483 | 135/200 |
+| `json-compact` | 65.5% | 3,943 | 131/200 |
+| `yaml` | 68.5% | 4,969 | 137/200 |
+| `xml` | 69.5% | 7,314 | 139/200 |
+| `json-pretty` | 64.5% | 6,347 | 129/200 |
##### E-commerce orders with nested structures
| Format | Accuracy | Tokens | Correct/Total |
| ------ | -------- | ------ | ------------- |
| `toon` | 78.8% | 5,967 | 126/160 |
-| `csv` | 71.9% | 6,735 | 115/160 |
-| `yaml` | 71.9% | 7,328 | 115/160 |
-| `json` | 73.1% | 9,694 | 117/160 |
-| `xml` | 73.8% | 10,992 | 118/160 |
+| `csv` | 76.3% | 6,735 | 122/160 |
+| `json-compact` | 70.6% | 5,962 | 113/160 |
+| `yaml` | 72.5% | 7,328 | 116/160 |
+| `json-pretty` | 76.9% | 9,694 | 123/160 |
+| `xml` | 73.1% | 10,992 | 117/160 |
##### Time-series analytics data
| Format | Accuracy | Tokens | Correct/Total |
| ------ | -------- | ------ | ------------- |
-| `csv` | 67.6% | 1,393 | 92/136 |
-| `toon` | 67.6% | 1,515 | 92/136 |
-| `yaml` | 64.7% | 2,938 | 88/136 |
-| `json` | 68.4% | 3,665 | 93/136 |
-| `xml` | 66.2% | 4,376 | 90/136 |
+| `toon` | 68.4% | 1,515 | 93/136 |
+| `csv` | 65.4% | 1,393 | 89/136 |
+| `json-compact` | 64.7% | 2,341 | 88/136 |
+| `yaml` | 66.2% | 2,938 | 90/136 |
+| `json-pretty` | 64.7% | 3,665 | 88/136 |
+| `xml` | 66.9% | 4,376 | 91/136 |
##### Top 100 GitHub repositories
| Format | Accuracy | Tokens | Correct/Total |
| ------ | -------- | ------ | ------------- |
-| `csv` | 64.2% | 8,513 | 77/120 |
-| `toon` | 62.5% | 8,745 | 75/120 |
-| `yaml` | 57.5% | 13,129 | 69/120 |
-| `json` | 55.0% | 15,145 | 66/120 |
-| `xml` | 53.3% | 17,095 | 64/120 |
+| `toon` | 65.0% | 8,745 | 78/120 |
+| `csv` | 62.5% | 8,513 | 75/120 |
+| `json-compact` | 58.3% | 11,455 | 70/120 |
+| `yaml` | 56.7% | 13,129 | 68/120 |
+| `xml` | 55.8% | 17,095 | 67/120 |
+| `json-pretty` | 52.5% | 15,145 | 63/120 |
#### Performance by Model
@@ -86,40 +94,44 @@ grok-4-fast-non-reasoning
| Format | Accuracy | Correct/Total |
| ------ | -------- | ------------- |
| `toon` | 96.1% | 148/154 |
-| `csv` | 90.3% | 139/154 |
-| `yaml` | 89.0% | 137/154 |
-| `json` | 87.7% | 135/154 |
-| `xml` | 83.8% | 129/154 |
+| `csv` | 91.6% | 141/154 |
+| `yaml` | 91.6% | 141/154 |
+| `json-compact` | 91.6% | 141/154 |
+| `xml` | 87.0% | 134/154 |
+| `json-pretty` | 86.4% | 133/154 |
##### claude-haiku-4-5-20251001
| Format | Accuracy | Correct/Total |
| ------ | -------- | ------------- |
+| `json-pretty` | 50.0% | 77/154 |
| `yaml` | 49.4% | 76/154 |
-| `toon` | 48.1% | 74/154 |
-| `csv` | 48.1% | 74/154 |
-| `json` | 47.4% | 73/154 |
-| `xml` | 46.8% | 72/154 |
+| `toon` | 48.7% | 75/154 |
+| `xml` | 48.1% | 74/154 |
+| `csv` | 47.4% | 73/154 |
+| `json-compact` | 44.2% | 68/154 |
##### gemini-2.5-flash
| Format | Accuracy | Correct/Total |
| ------ | -------- | ------------- |
| `csv` | 87.7% | 135/154 |
-| `xml` | 85.1% | 131/154 |
-| `toon` | 83.8% | 129/154 |
-| `json` | 78.6% | 121/154 |
-| `yaml` | 76.6% | 118/154 |
+| `xml` | 87.7% | 135/154 |
+| `toon` | 86.4% | 133/154 |
+| `yaml` | 79.9% | 123/154 |
+| `json-compact` | 79.9% | 123/154 |
+| `json-pretty` | 76.6% | 118/154 |
##### grok-4-fast-non-reasoning
| Format | Accuracy | Correct/Total |
| ------ | -------- | ------------- |
-| `toon` | 48.7% | 75/154 |
-| `json` | 48.1% | 74/154 |
-| `xml` | 47.4% | 73/154 |
-| `yaml` | 46.8% | 72/154 |
-| `csv` | 45.5% | 70/154 |
+| `toon` | 49.4% | 76/154 |
+| `json-pretty` | 48.7% | 75/154 |
+| `xml` | 46.1% | 71/154 |
+| `yaml` | 46.1% | 71/154 |
+| `json-compact` | 45.5% | 70/154 |
+| `csv` | 44.2% | 68/154 |
@@ -159,7 +171,7 @@ Four datasets designed to test different structural patterns (all contain arrays
#### Evaluation Process
-1. **Format conversion**: Each dataset is converted to all 5 formats (TOON, CSV, XML, JSON, YAML).
+1. **Format conversion**: Each dataset is converted to all 6 formats (TOON, CSV, XML, YAML, JSON, JSON compact).
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`).
@@ -168,6 +180,6 @@ Four datasets designed to test different structural patterns (all contain arrays
- **Models tested**: `gpt-5-nano`, `claude-haiku-4-5-20251001`, `gemini-2.5-flash`, `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**: 154 questions × 5 formats × 4 models = 3,080 LLM calls
+- **Total evaluations**: 154 questions × 6 formats × 4 models = 3,696 LLM calls
diff --git a/benchmarks/results/token-efficiency.md b/benchmarks/results/token-efficiency.md
index a40fc61..5d68d97 100644
--- a/benchmarks/results/token-efficiency.md
+++ b/benchmarks/results/token-efficiency.md
@@ -1,26 +1,30 @@
### Token Efficiency
```
-⭐ GitHub Repositories ██████████████░░░░░░░░░░░ 8,745 tokens
- vs JSON: 15,145 (-42.3%)
- vs YAML: 13,129 (-33.4%)
- vs XML: 17,095 (-48.8%)
+⭐ GitHub Repositories ██████████████░░░░░░░░░░░ 8,745 tokens
+ vs JSON (-42.3%) 15,145
+ vs JSON compact (-23.7%) 11,455
+ vs YAML (-33.4%) 13,129
+ vs XML (-48.8%) 17,095
-📈 Daily Analytics ██████████░░░░░░░░░░░░░░░ 4,507 tokens
- vs JSON: 10,977 (-58.9%)
- vs YAML: 8,810 (-48.8%)
- vs XML: 13,128 (-65.7%)
+📈 Daily Analytics ██████████░░░░░░░░░░░░░░░ 4,507 tokens
+ vs JSON (-58.9%) 10,977
+ vs JSON compact (-35.7%) 7,013
+ vs YAML (-48.8%) 8,810
+ vs XML (-65.7%) 13,128
-🛒 E-Commerce Order ████████████████░░░░░░░░░ 166 tokens
- vs JSON: 257 (-35.4%)
- vs YAML: 197 (-15.7%)
- vs XML: 271 (-38.7%)
+🛒 E-Commerce Order ████████████████░░░░░░░░░ 166 tokens
+ vs JSON (-35.4%) 257
+ vs JSON compact (-2.9%) 171
+ vs YAML (-15.7%) 197
+ vs XML (-38.7%) 271
─────────────────────────────────────────────────────────────────────
-Total █████████████░░░░░░░░░░░░ 13,418 tokens
- vs JSON: 26,379 (-49.1%)
- vs YAML: 22,136 (-39.4%)
- vs XML: 30,494 (-56.0%)
+Total ██████████████░░░░░░░░░░░ 13,418 tokens
+ vs JSON (-49.1%) 26,379
+ vs JSON compact (-28.0%) 18,639
+ vs YAML (-39.4%) 22,136
+ vs XML (-56.0%) 30,494
```
diff --git a/benchmarks/scripts/fetch-github-repos.ts b/benchmarks/scripts/fetch-github-repos.ts
index 3b75fd9..bf5e815 100644
--- a/benchmarks/scripts/fetch-github-repos.ts
+++ b/benchmarks/scripts/fetch-github-repos.ts
@@ -1,10 +1,11 @@
+import * as fsp from 'node:fs/promises'
import * as path from 'node:path'
import process from 'node:process'
import * as prompts from '@clack/prompts'
import { ofetch } from 'ofetch'
import pMap from 'p-map'
import { BENCHMARKS_DIR } from '../src/constants'
-import { ensureDir, saveJsonFile } from '../src/utils'
+import { ensureDir } from '../src/utils'
prompts.intro('GitHub Repositories Fetcher')
@@ -79,7 +80,8 @@ async function saveRepos(repos: Record[]): Promise {
const outputFile = path.join(outputDir, 'github-repos.json')
await ensureDir(outputDir)
- await saveJsonFile(outputFile, repos)
+ const jsonOutput = JSON.stringify(repos, undefined, 2)
+ await fsp.writeFile(outputFile, `${jsonOutput}\n`, 'utf-8')
const relativePath = path.relative(BENCHMARKS_DIR, outputFile)
prompts.log.info(`Result saved to \`${relativePath}\``)
diff --git a/benchmarks/scripts/token-efficiency-benchmark.ts b/benchmarks/scripts/token-efficiency-benchmark.ts
index ff6fa30..e903852 100644
--- a/benchmarks/scripts/token-efficiency-benchmark.ts
+++ b/benchmarks/scripts/token-efficiency-benchmark.ts
@@ -3,7 +3,7 @@ import * as path from 'node:path'
import * as prompts from '@clack/prompts'
import { encode } from '../../src/index'
import githubRepos from '../data/github-repos.json' with { type: 'json' }
-import { BENCHMARKS_DIR, ROOT_DIR } from '../src/constants'
+import { BENCHMARKS_DIR, FORMATTER_DISPLAY_NAMES, ROOT_DIR } from '../src/constants'
import { generateAnalyticsData, generateOrderData } from '../src/datasets'
import { formatters } from '../src/formatters'
import { createProgressBar, ensureDir, tokenize } from '../src/utils'
@@ -50,118 +50,102 @@ const BENCHMARK_EXAMPLES = [
prompts.intro('Token Efficiency Benchmark')
-// Calculate total savings
-let totalJsonTokens = 0
-let totalToonTokens = 0
-let totalXmlTokens = 0
-let totalYamlTokens = 0
-
const results: BenchmarkResult[] = []
+const totalTokensByFormat: Record = {}
for (const example of BENCHMARK_EXAMPLES) {
const data = example.getData()
- const jsonString = JSON.stringify(data, undefined, 2)
- const toonString = encode(data)
- const xmlString = formatters.xml!(data)
- const yamlString = formatters.yaml!(data)
+ // Calculate tokens for each format
+ const formatMetrics: FormatMetrics[] = []
+ const tokensByFormat: Record = {}
- const jsonTokens = tokenize(jsonString)
- const toonTokens = tokenize(toonString)
- const xmlTokens = tokenize(xmlString)
- const yamlTokens = tokenize(yamlString)
+ for (const [formatName, formatter] of Object.entries(formatters)) {
+ const formattedString = formatter(data)
+ const tokens = tokenize(formattedString)
+ tokensByFormat[formatName] = tokens
+ totalTokensByFormat[formatName] = (totalTokensByFormat[formatName] || 0) + tokens
+ }
- const jsonSavings = jsonTokens - toonTokens
- const xmlSavings = xmlTokens - toonTokens
- const yamlSavings = yamlTokens - toonTokens
-
- totalJsonTokens += jsonTokens
- totalToonTokens += toonTokens
- totalXmlTokens += xmlTokens
- totalYamlTokens += yamlTokens
+ // Calculate savings vs TOON
+ const toonTokens = tokensByFormat.toon!
+ for (const [formatName, tokens] of Object.entries(tokensByFormat)) {
+ const savings = tokens - toonTokens
+ formatMetrics.push({
+ name: formatName,
+ tokens,
+ savings,
+ savingsPercent: formatName === 'toon' ? '0.0' : ((savings / tokens) * 100).toFixed(1),
+ })
+ }
results.push({
name: example.name,
emoji: example.emoji,
description: example.description,
data,
- formats: [
- {
- name: 'toon',
- tokens: toonTokens,
- savings: 0,
- savingsPercent: '0.0',
- },
- {
- name: 'json',
- tokens: jsonTokens,
- savings: jsonSavings,
- savingsPercent: ((jsonSavings / jsonTokens) * 100).toFixed(1),
- },
- {
- name: 'xml',
- tokens: xmlTokens,
- savings: xmlSavings,
- savingsPercent: ((xmlSavings / xmlTokens) * 100).toFixed(1),
- },
- {
- name: 'yaml',
- tokens: yamlTokens,
- savings: yamlSavings,
- savingsPercent: ((yamlSavings / yamlTokens) * 100).toFixed(1),
- },
- ],
+ formats: formatMetrics,
showDetailed: example.showDetailed,
})
}
-const totalJsonSavings = totalJsonTokens - totalToonTokens
-const totalJsonSavingsPercent = ((totalJsonSavings / totalJsonTokens) * 100).toFixed(1)
-
-const totalXmlSavings = totalXmlTokens - totalToonTokens
-const totalXmlSavingsPercent = ((totalXmlSavings / totalXmlTokens) * 100).toFixed(1)
-
-const totalYamlSavings = totalYamlTokens - totalToonTokens
-const totalYamlSavingsPercent = ((totalYamlSavings / totalYamlTokens) * 100).toFixed(1)
+// Calculate total savings percentages
+const totalToonTokens = totalTokensByFormat.toon!
+const totalSavingsPercent: Record = {}
+for (const [formatName, totalTokens] of Object.entries(totalTokensByFormat)) {
+ if (formatName === 'toon') {
+ totalSavingsPercent[formatName] = '0.0'
+ }
+ else {
+ const savings = totalTokens - totalToonTokens
+ totalSavingsPercent[formatName] = ((savings / totalTokens) * 100).toFixed(1)
+ }
+}
// Generate ASCII bar chart visualization (stacked compact format)
+const formatOrder = ['json-pretty', 'json-compact', 'yaml', 'xml']
const datasetRows = results
.map((result) => {
const toon = result.formats.find(f => f.name === 'toon')!
- const json = result.formats.find(f => f.name === 'json')!
- const xml = result.formats.find(f => f.name === 'xml')!
- const yaml = result.formats.find(f => f.name === 'yaml')!
-
- const percentage = Number.parseFloat(json.savingsPercent)
+ const percentage = Number.parseFloat(result.formats.find(f => f.name === 'json-pretty')!.savingsPercent)
const bar = createProgressBar(100 - percentage, 100) // Invert to show TOON tokens
const toonStr = toon.tokens.toLocaleString('en-US')
- const jsonStr = json.tokens.toLocaleString('en-US')
- const xmlStr = xml.tokens.toLocaleString('en-US')
- const yamlStr = yaml.tokens.toLocaleString('en-US')
- const line1 = `${result.emoji} ${result.name.padEnd(25)} ${bar} ${toonStr.padStart(6)} tokens`
- const line2 = ` vs JSON: ${jsonStr.padStart(6)} (-${json.savingsPercent}%)`
- const line3 = ` vs YAML: ${yamlStr.padStart(6)} (-${yaml.savingsPercent}%)`
- const line4 = ` vs XML: ${xmlStr.padStart(6)} (-${xml.savingsPercent}%)`
+ const line1 = `${result.emoji} ${result.name.padEnd(25)} ${bar} ${toonStr.padStart(6)} tokens`
- return `${line1}\n${line2}\n${line3}\n${line4}`
+ const comparisonLines = formatOrder.map((formatName) => {
+ const format = result.formats.find(f => f.name === formatName)!
+ const label = FORMATTER_DISPLAY_NAMES[formatName] || formatName.toUpperCase()
+ const labelWithSavings = `vs ${label} (-${format.savingsPercent}%)`.padEnd(28)
+ const tokenStr = format.tokens.toLocaleString('en-US').padStart(6)
+ return ` ${labelWithSavings}${tokenStr}`
+ })
+
+ return [line1, ...comparisonLines].join('\n')
})
.join('\n\n')
// Add separator and totals row
const separator = '─────────────────────────────────────────────────────────────────────'
-// Calculate bar for totals (TOON vs average of JSON+YAML+XML)
-const averageComparisonTokens = (totalJsonTokens + totalYamlTokens + totalXmlTokens) / 3
+// Calculate bar for totals (TOON vs average of comparison formats)
+const comparisonTokens = formatOrder.map(name => totalTokensByFormat[name]!)
+const averageComparisonTokens = comparisonTokens.reduce((a, b) => a + b, 0) / comparisonTokens.length
const totalPercentage = (totalToonTokens / averageComparisonTokens) * 100
const totalBar = createProgressBar(totalPercentage, 100)
-const totalLine1 = `Total ${totalBar} ${totalToonTokens.toLocaleString('en-US').padStart(6)} tokens`
-const totalLine2 = ` vs JSON: ${totalJsonTokens.toLocaleString('en-US').padStart(6)} (-${totalJsonSavingsPercent}%)`
-const totalLine3 = ` vs YAML: ${totalYamlTokens.toLocaleString('en-US').padStart(6)} (-${totalYamlSavingsPercent}%)`
-const totalLine4 = ` vs XML: ${totalXmlTokens.toLocaleString('en-US').padStart(6)} (-${totalXmlSavingsPercent}%)`
+const totalLine1 = `Total ${totalBar} ${totalToonTokens.toLocaleString('en-US').padStart(6)} tokens`
-const barChartSection = `${datasetRows}\n\n${separator}\n${totalLine1}\n${totalLine2}\n${totalLine3}\n${totalLine4}`
+const totalComparisonLines = formatOrder.map((formatName) => {
+ const label = FORMATTER_DISPLAY_NAMES[formatName] || formatName.toUpperCase()
+ const tokens = totalTokensByFormat[formatName]!
+ const percent = totalSavingsPercent[formatName]!
+ const labelWithSavings = `vs ${label} (-${percent}%)`.padEnd(28)
+ const tokenStr = tokens.toLocaleString('en-US').padStart(6)
+ return ` ${labelWithSavings}${tokenStr}`
+})
+
+const barChartSection = `${datasetRows}\n\n${separator}\n${totalLine1}\n${totalComparisonLines.join('\n')}`
// Generate detailed examples (only for selected examples)
// Note: Large datasets are truncated for display readability in the report.
@@ -185,7 +169,7 @@ const detailedExamples = results
const separator = i < filtered.length - 1 ? '\n\n---' : ''
- const json = result.formats.find(f => f.name === 'json')!
+ const json = result.formats.find(f => f.name === 'json-pretty')!
const toon = result.formats.find(f => f.name === 'toon')!
return `#### ${result.emoji} ${result.name}
diff --git a/benchmarks/src/constants.ts b/benchmarks/src/constants.ts
index 0c8f780..5d0c7b1 100644
--- a/benchmarks/src/constants.ts
+++ b/benchmarks/src/constants.ts
@@ -14,7 +14,7 @@ export const BENCHMARKS_DIR: string = url.fileURLToPath(new URL('../', import.me
export const MODEL_RPM_LIMITS: Record = {
'claude-haiku-4-5-20251001': 50,
'gemini-2.5-flash': 25,
- 'gpt-5-nano': undefined,
+ 'gpt-5-nano': 50,
'grok-4-fast-non-reasoning': 50,
}
@@ -23,6 +23,18 @@ export const MODEL_RPM_LIMITS: Record = {
*/
export const DEFAULT_CONCURRENCY = 10
+/**
+ * Display names for data format types
+ */
+export const FORMATTER_DISPLAY_NAMES: Record = {
+ 'json-pretty': 'JSON',
+ 'json-compact': 'JSON compact',
+ 'toon': 'TOON',
+ 'csv': 'CSV',
+ 'xml': 'XML',
+ 'yaml': 'YAML',
+} as const
+
/**
* Progress bar configuration
*/
diff --git a/benchmarks/src/formatters.ts b/benchmarks/src/formatters.ts
index b20b1b7..7918b2b 100644
--- a/benchmarks/src/formatters.ts
+++ b/benchmarks/src/formatters.ts
@@ -12,11 +12,12 @@ import { encode as encodeToon } from '../../src/index'
* CSV has inherent limitations with nested structures (see `toCSV` docs).
*/
export const formatters: Record string> = {
- json: data => JSON.stringify(data, undefined, 2),
- toon: data => encodeToon(data),
- csv: data => toCSV(data),
- xml: data => toXML(data),
- yaml: data => stringifyYAML(data),
+ 'json-pretty': data => JSON.stringify(data, undefined, 2),
+ 'json-compact': data => JSON.stringify(data),
+ 'toon': data => encodeToon(data),
+ 'csv': data => toCSV(data),
+ 'xml': data => toXML(data),
+ 'yaml': data => stringifyYAML(data),
}
/**
diff --git a/benchmarks/src/report.ts b/benchmarks/src/report.ts
index 6c33d4b..d3ba9b0 100644
--- a/benchmarks/src/report.ts
+++ b/benchmarks/src/report.ts
@@ -1,7 +1,7 @@
import type { EvaluationResult, FormatResult, Question } from './types'
import * as fsp from 'node:fs/promises'
import * as path from 'node:path'
-import { BENCHMARKS_DIR } from './constants'
+import { BENCHMARKS_DIR, FORMATTER_DISPLAY_NAMES } from './constants'
import { datasets } from './datasets'
import { models } from './evaluate'
import { createProgressBar, ensureDir, tokenize } from './utils'
@@ -49,7 +49,7 @@ export function generateMarkdownReport(
tokenCounts: Record,
): string {
const toon = formatResults.find(r => r.format === 'toon')
- const json = formatResults.find(r => r.format === 'json')
+ const json = formatResults.find(r => r.format === 'json-pretty')
const modelIds = models.map(m => m.modelId)
const modelNames = modelIds.filter(id => results.some(r => r.model === id))
@@ -71,10 +71,11 @@ export function generateMarkdownReport(
const formatLines = modelResults.map((result) => {
const bar = createProgressBar(result.accuracy, 1, 20)
- const accuracyStr = `${(result.accuracy * 100).toFixed(1)}%`.padStart(6)
- const countStr = `(${result.correctCount}/${result.totalCount})`
+ const accuracyString = `${(result.accuracy * 100).toFixed(1)}%`.padStart(6)
+ const countString = `(${result.correctCount}/${result.totalCount})`
const prefix = result.format === 'toon' ? '→ ' : ' '
- return `${prefix}${result.format.padEnd(12)} ${bar} ${accuracyStr} ${countStr}`
+ const displayName = FORMATTER_DISPLAY_NAMES[result.format] || result.format
+ return `${prefix}${displayName.padEnd(12)} ${bar} ${accuracyString} ${countString}`
}).join('\n')
// Add blank line before model name, except for first model
@@ -248,7 +249,7 @@ ${totalQuestions} questions are generated dynamically across three categories:
#### Evaluation Process
-1. **Format conversion**: Each dataset is converted to all ${formatCount} formats (${formatResults.map(f => f.format.toUpperCase()).join(', ')}).
+1. **Format conversion**: Each dataset is converted to all ${formatCount} formats (${formatResults.map(f => FORMATTER_DISPLAY_NAMES[f.format] || f.format).join(', ')}).
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\` = \`$50,000\`, \`Engineering\` = \`engineering\`, \`2025-01-01\` = \`January 1, 2025\`).
diff --git a/benchmarks/src/utils.ts b/benchmarks/src/utils.ts
index bbd44c0..c18c115 100644
--- a/benchmarks/src/utils.ts
+++ b/benchmarks/src/utils.ts
@@ -40,19 +40,3 @@ export function tokenize(text: string): number {
export async function ensureDir(dirPath: string): Promise {
await fsp.mkdir(dirPath, { recursive: true })
}
-
-/**
- * Save data as formatted JSON file
- *
- * @param filePath - Path to save the file
- * @param data - Data to serialize as JSON
- * @param indent - Indentation spaces (default: 2)
- */
-export async function saveJsonFile(
- filePath: string,
- data: unknown,
- indent = 2,
-): Promise {
- const json = JSON.stringify(data, undefined, indent)
- await fsp.writeFile(filePath, `${json}\n`, 'utf-8')
-}
diff --git a/src/encode/encoders.ts b/src/encode/encoders.ts
index e517f22..a71c179 100644
--- a/src/encode/encoders.ts
+++ b/src/encode/encoders.ts
@@ -150,8 +150,8 @@ export function encodeArrayOfObjectsAsTabular(
depth: Depth,
options: ResolvedEncodeOptions,
): void {
- const headerStr = formatHeader(rows.length, { key: prefix, fields: header, delimiter: options.delimiter, lengthMarker: options.lengthMarker })
- writer.push(depth, `${headerStr}`)
+ const formattedHeader = formatHeader(rows.length, { key: prefix, fields: header, delimiter: options.delimiter, lengthMarker: options.lengthMarker })
+ writer.push(depth, `${formattedHeader}`)
writeTabularRows(rows, header, writer, depth + 1, options)
}
@@ -255,8 +255,8 @@ export function encodeObjectAsListItem(obj: JsonObject, writer: LineWriter, dept
const header = extractTabularHeader(firstValue)
if (header) {
// Tabular format for uniform arrays of objects
- const headerStr = formatHeader(firstValue.length, { key: firstKey, fields: header, delimiter: options.delimiter, lengthMarker: options.lengthMarker })
- writer.pushListItem(depth, headerStr)
+ const formattedHeader = formatHeader(firstValue.length, { key: firstKey, fields: header, delimiter: options.delimiter, lengthMarker: options.lengthMarker })
+ writer.pushListItem(depth, formattedHeader)
writeTabularRows(firstValue, header, writer, depth + 1, options)
}
else {