A test run is a single execution of a benchmark test using a defined model configuration.
Each run represents how a particular large language model (LLM) — such as GPT-4, Claude-3, or Gemini — performed on a given task at a specific time, with specific settings.
A test run includes:
Together, test runs make it possible to compare models, providers, and configurations across benchmarks in a transparent and reproducible way.
{'document-type': ['newspaper-page'], 'century': [18], 'language': ['en'], 'task': ['data-correction']}
| Provider | alibaba |
| Model | qwen3.5-27b |
| Temperature | 0.0 |
| Dataclass | CorrectedAdvert |
| Normalized Score | 100.00 % |
| Test time | unknown seconds |
Fix this xml. Add xml-tags if faulty where it makes sense.
Format your response as JSON. Use the keys 'fixed_xml', 'number_of_fixes', 'explanation'.
no valid result
| Fuzzy Score | F1 micro / macro | Micro precision/recall | Tue/False Positives | |||||
| 95.84 | n/a | n/a | n/a | n/a | n/a | n/a | n/a | n/a |
| Micro Precision | Micro Recall | Instances | TP | FP | FN | |||
| Pricing Date: n/a, n/a. | Tokens: 39.2K IT + 413.5K OT = 452.7K TT | Cost: 0.012$ + 0.992$ = 1.004$ |
{'document-type': ['newspaper-page'], 'century': [18], 'language': ['en'], 'task': ['data-correction']}
| Provider | alibaba |
| Model | qwen3.5-35b-a3b |
| Temperature | 0.0 |
| Dataclass | CorrectedAdvert |
| Normalized Score | 100.00 % |
| Test time | unknown seconds |
Fix this xml. Add xml-tags if faulty where it makes sense.
Format your response as JSON. Use the keys 'fixed_xml', 'number_of_fixes', 'explanation'.
no valid result
| Fuzzy Score | F1 micro / macro | Micro precision/recall | Tue/False Positives | |||||
| 92.93 | n/a | n/a | n/a | n/a | n/a | n/a | n/a | n/a |
| Micro Precision | Micro Recall | Instances | TP | FP | FN | |||
| Pricing Date: n/a, n/a. | Tokens: 39.2K IT + 442.2K OT = 481.4K TT | Cost: 0.010$ + 0.884$ = 0.894$ |
{'document-type': ['newspaper-page'], 'century': [18], 'language': ['en'], 'task': ['data-correction']}
| Provider | alibaba |
| Model | qwen3.5-plus |
| Temperature | 0.0 |
| Dataclass | CorrectedAdvert |
| Normalized Score | 100.00 % |
| Test time | unknown seconds |
Fix this xml. Add xml-tags if faulty where it makes sense.
Format your response as JSON. Use the keys 'fixed_xml', 'number_of_fixes', 'explanation'.
no valid result
| Fuzzy Score | F1 micro / macro | Micro precision/recall | Tue/False Positives | |||||
| 95.80 | n/a | n/a | n/a | n/a | n/a | n/a | n/a | n/a |
| Micro Precision | Micro Recall | Instances | TP | FP | FN | |||
| Pricing Date: n/a, n/a. | Tokens: 39.2K IT + 432.7K OT = 472.0K TT | Cost: 0.016$ + 1.038$ = 1.054$ |
{'document-type': ['newspaper-page'], 'century': [18], 'language': ['en'], 'task': ['data-correction']}
| Provider | x-ai |
| Model | grok-4.20-0309-reasoning |
| Temperature | 0.0 |
| Dataclass | CorrectedAdvert |
| Normalized Score | 100.00 % |
| Test time | unknown seconds |
Fix this xml. Add xml-tags if faulty where it makes sense.
Format your response as JSON. Use the keys 'fixed_xml', 'number_of_fixes', 'explanation'.
no valid result
| Fuzzy Score | F1 micro / macro | Micro precision/recall | Tue/False Positives | |||||
| 98.61 | n/a | n/a | n/a | n/a | n/a | n/a | n/a | n/a |
| Micro Precision | Micro Recall | Instances | TP | FP | FN | |||
| Pricing Date: n/a, n/a. | Tokens: 40.4K IT + 26.7K OT = 67.0K TT | Cost: 0.081$ + 0.160$ = 0.241$ |
{'document-type': ['newspaper-page'], 'century': [18], 'language': ['en'], 'task': ['data-correction']}
| Provider | openai |
| Model | gpt-5.3-codex |
| Temperature | 1.0 |
| Dataclass | not set (=no auto-parsed result) |
| Normalized Score | 100.00 % |
| Test time | unknown seconds |
Fix this xml. Add xml-tags if faulty where it makes sense.
Format your response as JSON. Use the keys 'fixed_xml', 'number_of_fixes', 'explanation'.
no valid result
| Fuzzy Score | F1 micro / macro | Micro precision/recall | Tue/False Positives | |||||
| 97.35 | n/a | n/a | n/a | n/a | n/a | n/a | n/a | n/a |
| Micro Precision | Micro Recall | Instances | TP | FP | FN | |||
| Pricing Date: n/a, n/a. | Tokens: 27.9K IT + 29.7K OT = 57.7K TT | Cost: 0.049$ + 0.416$ = 0.465$ |
{'document-type': ['newspaper-page'], 'century': [18], 'language': ['en'], 'task': ['data-correction']}
| Provider | openai |
| Model | gpt-5.3-codex |
| Temperature | 1.0 |
| Dataclass | not set (=no auto-parsed result) |
| Normalized Score | 100.00 % |
| Test time | unknown seconds |
Fix this xml. Add xml-tags if faulty where it makes sense.
Format your response as JSON. Use the keys 'fixed_xml', 'number_of_fixes', 'explanation'.
no valid result
| Fuzzy Score | F1 micro / macro | Micro precision/recall | Tue/False Positives | |||||
| 97.12 | n/a | n/a | n/a | n/a | n/a | n/a | n/a | n/a |
| Micro Precision | Micro Recall | Instances | TP | FP | FN | |||
| Pricing Date: n/a, n/a. | Tokens: 27.9K IT + 30.6K OT = 58.5K TT | Cost: 0.049$ + 0.428$ = 0.477$ |
{'document-type': ['newspaper-page'], 'century': [18], 'language': ['en'], 'task': ['data-correction']}
| Provider | anthropic |
| Model | claude-opus-4-6 |
| Temperature | 0.0 |
| Dataclass | CorrectedAdvert |
| Normalized Score | 100.00 % |
| Test time | unknown seconds |
Fix this xml. Add xml-tags if faulty where it makes sense.
Format your response as JSON. Use the keys 'fixed_xml', 'number_of_fixes', 'explanation'.
no valid result
| Fuzzy Score | F1 micro / macro | Micro precision/recall | Tue/False Positives | |||||
| 97.46 | n/a | n/a | n/a | n/a | n/a | n/a | n/a | n/a |
| Micro Precision | Micro Recall | Instances | TP | FP | FN | |||
| Pricing Date: n/a, n/a. | Tokens: 74.1K IT + 41.3K OT = 115.4K TT | Cost: 0.371$ + 1.032$ = 1.403$ |
{'document-type': ['newspaper-page'], 'century': [18], 'language': ['en'], 'task': ['data-correction']}
| Provider | genai |
| Model | gemini-3.1-pro-preview |
| Temperature | 0.0 |
| Dataclass | CorrectedAdvert |
| Normalized Score | 100.00 % |
| Test time | unknown seconds |
Fix this xml. Add xml-tags if faulty where it makes sense.
Format your response as JSON. Use the keys 'fixed_xml', 'number_of_fixes', 'explanation'.
no valid result
| Fuzzy Score | F1 micro / macro | Micro precision/recall | Tue/False Positives | |||||
| 94.28 | n/a | n/a | n/a | n/a | n/a | n/a | n/a | n/a |
| Micro Precision | Micro Recall | Instances | TP | FP | FN | |||
| Pricing Date: n/a, n/a. | Tokens: 31.5K IT + 31.9K OT = 63.5K TT | Cost: 0.063$ + 0.383$ = 0.446$ |
{'document-type': ['newspaper-page'], 'century': [18], 'language': ['en'], 'task': ['data-correction']}
| Provider | anthropic |
| Model | claude-sonnet-4-6 |
| Temperature | 0.0 |
| Dataclass | CorrectedAdvert |
| Normalized Score | 100.00 % |
| Test time | unknown seconds |
Fix this xml. Add xml-tags if faulty where it makes sense.
Format your response as JSON. Use the keys 'fixed_xml', 'number_of_fixes', 'explanation'.
no valid result
| Fuzzy Score | F1 micro / macro | Micro precision/recall | Tue/False Positives | |||||
| 96.55 | n/a | n/a | n/a | n/a | n/a | n/a | n/a | n/a |
| Micro Precision | Micro Recall | Instances | TP | FP | FN | |||
| Pricing Date: n/a, n/a. | Tokens: 74.1K IT + 46.3K OT = 120.4K TT | Cost: 0.222$ + 0.695$ = 0.917$ |
{'document-type': ['newspaper-page'], 'century': [18], 'language': ['en'], 'task': ['data-correction']}
| Provider | openai |
| Model | gpt-5.4-2026-03-05 |
| Temperature | 1.0 |
| Dataclass | CorrectedAdvert |
| Normalized Score | 100.00 % |
| Test time | unknown seconds |
Fix this xml. Add xml-tags if faulty where it makes sense.
Format your response as JSON. Use the keys 'fixed_xml', 'number_of_fixes', 'explanation'.
no valid result
| Fuzzy Score | F1 micro / macro | Micro precision/recall | Tue/False Positives | |||||
| 96.78 | n/a | n/a | n/a | n/a | n/a | n/a | n/a | n/a |
| Micro Precision | Micro Recall | Instances | TP | FP | FN | |||
| Pricing Date: n/a, n/a. | Tokens: 33.6K IT + 26.2K OT = 59.8K TT | Cost: 0.084$ + 0.393$ = 0.477$ |