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': ['index-card'], 'writing': ['typed', 'printed', 'handwritten'], 'century': [20, 19], 'language': ['de', 'fr', 'en', 'la', 'el', 'fi', 'sv', 'pl'], 'layout': ['index'], 'entry-type': ['bibliographic'], 'task': ['information-extraction']}
| Provider | mistral |
| Model | ministral-8b-2512 |
| Temperature | 0.0 |
| Dataclass | Document |
| Normalized Score | 0.00 % |
| Test time | unknown seconds |
Extract the bibliographic information about a historical dissertation from this index card and return it as a structured JSON object with the following exact format:
```json
{
"type": {
"type": "Dissertation or thesis" OR "Reference"
},
"author": {
"last_name": "string",
"first_name": "string"
},
"publication": {
"title": "string",
"year": integer,
"place": "string or empty string",
"pages": "string or empty string",
"publisher": "string or empty string",
"format": "string or empty string"
},
"library_reference": {
"shelfmark": "string or empty string",
"subjects": "string or empty string"
}
}
```
EXTRACTION RULES:
1. **Card Type**: If a card contains the note "s." on a separate line, it is a "Reference". Otherwise, it is a "Dissertation or thesis".
2. **Author**: Extract last_name and first_name. If only one name is given, put it in last_name and leave first_name empty.
3. **Publication**:
- title: The main title of the work
- year: Publication year as integer
- place: Publication place
- pages: Page count (remove " S." suffix if present)
- publisher: Publishing house/institution
- format: Usually "8°", "8'", or "4°" - single value only
4. **Library Reference**:
- shelfmark: Often begins with "Diss." or "AT", may be marked with "Standort:"
- subjects: Subject classifications or keywords
5. **Missing Information**: Use empty string "" for missing text fields, omit year fields entirely if not present.
6. **Ignore**: Disregard any information that doesn't fit into these categories.
Return ONLY the JSON object, no additional text or explanation.
no valid result
| Fuzzy Score | F1 micro / macro | Micro precision/recall | Tue/False Positives | |||||
| n/a | 0.00 | 0.00 | 0.00 | 0.00 | 263 | 0 | 0 | 2415 |
| Micro Precision | Micro Recall | Instances | TP | FP | FN | |||
| Pricing Date: n/a, n/a. | Tokens: 314.5K IT + 52.9K OT = 367.5K TT | Cost: 0.047$ + 0.008$ = 0.055$ |
{'document-type': ['index-card'], 'writing': ['typed', 'printed', 'handwritten'], 'century': [20, 19], 'language': ['de', 'fr', 'en', 'la', 'el', 'fi', 'sv', 'pl'], 'layout': ['index'], 'entry-type': ['bibliographic'], 'task': ['information-extraction']}
| Provider | openai |
| Model | gpt-4o-mini-2024-07-18 |
| Temperature | 0.0 |
| Dataclass | Document |
| Normalized Score | 1.51 % |
| Test time | unknown seconds |
Extract the bibliographic information about a historical dissertation from this index card and return it as a structured JSON object with the following exact format:
```json
{
"type": {
"type": "Dissertation or thesis" OR "Reference"
},
"author": {
"last_name": "string",
"first_name": "string"
},
"publication": {
"title": "string",
"year": integer,
"place": "string or empty string",
"pages": "string or empty string",
"publisher": "string or empty string",
"format": "string or empty string"
},
"library_reference": {
"shelfmark": "string or empty string",
"subjects": "string or empty string"
}
}
```
EXTRACTION RULES:
1. **Card Type**: If a card contains the note "s." on a separate line, it is a "Reference". Otherwise, it is a "Dissertation or thesis".
2. **Author**: Extract last_name and first_name. If only one name is given, put it in last_name and leave first_name empty.
3. **Publication**:
- title: The main title of the work
- year: Publication year as integer
- place: Publication place
- pages: Page count (remove " S." suffix if present)
- publisher: Publishing house/institution
- format: Usually "8°", "8'", or "4°" - single value only
4. **Library Reference**:
- shelfmark: Often begins with "Diss." or "AT", may be marked with "Standort:"
- subjects: Subject classifications or keywords
5. **Missing Information**: Use empty string "" for missing text fields, omit year fields entirely if not present.
6. **Ignore**: Disregard any information that doesn't fit into these categories.
Return ONLY the JSON object, no additional text or explanation.
no valid result
| Fuzzy Score | F1 micro / macro | Micro precision/recall | Tue/False Positives | |||||
| n/a | 0.02 | 0.02 | 0.54 | 0.01 | 263 | 27 | 23 | 2388 |
| Micro Precision | Micro Recall | Instances | TP | FP | FN | |||
| Pricing Date: n/a, n/a. | Tokens: 6.5M IT + 37.0K OT = 6.6M TT | Cost: 0.980$ + 0.022$ = 1.003$ |
{'document-type': ['index-card'], 'writing': ['typed', 'printed', 'handwritten'], 'century': [20, 19], 'language': ['de', 'fr', 'en', 'la', 'el', 'fi', 'sv', 'pl'], 'layout': ['index'], 'entry-type': ['bibliographic'], 'task': ['information-extraction']}
| Provider | openrouter |
| Model | x-ai/grok-4 |
| Temperature | 0.0 |
| Dataclass | Document |
| Normalized Score | None % |
| Test time | unknown seconds |
Extract the bibliographic information about a historical dissertation from this index card and return it as a structured JSON object with the following exact format:
```json
{
"type": {
"type": "Dissertation or thesis" OR "Reference"
},
"author": {
"last_name": "string",
"first_name": "string"
},
"publication": {
"title": "string",
"year": integer,
"place": "string or empty string",
"pages": "string or empty string",
"publisher": "string or empty string",
"format": "string or empty string"
},
"library_reference": {
"shelfmark": "string or empty string",
"subjects": "string or empty string"
}
}
```
EXTRACTION RULES:
1. **Card Type**: If a card contains the note "s." on a separate line, it is a "Reference". Otherwise, it is a "Dissertation or thesis".
2. **Author**: Extract last_name and first_name. If only one name is given, put it in last_name and leave first_name empty.
3. **Publication**:
- title: The main title of the work
- year: Publication year as integer
- place: Publication place
- pages: Page count (remove " S." suffix if present)
- publisher: Publishing house/institution
- format: Usually "8°", "8'", or "4°" - single value only
4. **Library Reference**:
- shelfmark: Often begins with "Diss." or "AT", may be marked with "Standort:"
- subjects: Subject classifications or keywords
5. **Missing Information**: Use empty string "" for missing text fields, omit year fields entirely if not present.
6. **Ignore**: Disregard any information that doesn't fit into these categories.
Return ONLY the JSON object, no additional text or explanation.
no valid result
| Fuzzy Score | F1 micro / macro | Micro precision/recall | Tue/False Positives | |||||
| None | None | None | None | None | None | None | None | None |
| Micro Precision | Micro Recall | Instances | TP | FP | FN | |||
| Pricing Date: None, None. | Tokens: None IT + None OT = None TT | Cost: None$ + None$ = None$ |
{'document-type': ['index-card'], 'writing': ['typed', 'printed', 'handwritten'], 'century': [20, 19], 'language': ['de', 'fr', 'en', 'la', 'el', 'fi', 'sv', 'pl'], 'layout': ['index'], 'entry-type': ['bibliographic'], 'task': ['information-extraction']}
| Provider | openrouter |
| Model | qwen/qwen3-vl-8b-instruct |
| Temperature | 0.0 |
| Dataclass | Document |
| Normalized Score | 61.59 % |
| Test time | unknown seconds |
Extract the bibliographic information about a historical dissertation from this index card and return it as a structured JSON object with the following exact format:
```json
{
"type": {
"type": "Dissertation or thesis" OR "Reference"
},
"author": {
"last_name": "string",
"first_name": "string"
},
"publication": {
"title": "string",
"year": integer,
"place": "string or empty string",
"pages": "string or empty string",
"publisher": "string or empty string",
"format": "string or empty string"
},
"library_reference": {
"shelfmark": "string or empty string",
"subjects": "string or empty string"
}
}
```
EXTRACTION RULES:
1. **Card Type**: If a card contains the note "s." on a separate line, it is a "Reference". Otherwise, it is a "Dissertation or thesis".
2. **Author**: Extract last_name and first_name. If only one name is given, put it in last_name and leave first_name empty.
3. **Publication**:
- title: The main title of the work
- year: Publication year as integer
- place: Publication place
- pages: Page count (remove " S." suffix if present)
- publisher: Publishing house/institution
- format: Usually "8°", "8'", or "4°" - single value only
4. **Library Reference**:
- shelfmark: Often begins with "Diss." or "AT", may be marked with "Standort:"
- subjects: Subject classifications or keywords
5. **Missing Information**: Use empty string "" for missing text fields, omit year fields entirely if not present.
6. **Ignore**: Disregard any information that doesn't fit into these categories.
Return ONLY the JSON object, no additional text or explanation.
no valid result
| Fuzzy Score | F1 micro / macro | Micro precision/recall | Tue/False Positives | |||||
| n/a | 0.70 | 0.62 | 0.78 | 0.64 | 263 | 1536 | 429 | 879 |
| Micro Precision | Micro Recall | Instances | TP | FP | FN | |||
| Pricing Date: n/a, n/a. | Tokens: 439.0K IT + 42.6K OT = 481.6K TT | Cost: 0.035$ + 0.021$ = 0.056$ |
{'document-type': ['index-card'], 'writing': ['typed', 'printed', 'handwritten'], 'century': [20, 19], 'language': ['de', 'fr', 'en', 'la', 'el', 'fi', 'sv', 'pl'], 'layout': ['index'], 'entry-type': ['bibliographic'], 'task': ['information-extraction']}
| Provider | mistral |
| Model | magistral-small-2509 |
| Temperature | 0.0 |
| Dataclass | Document |
| Normalized Score | 0.04 % |
| Test time | unknown seconds |
Extract the bibliographic information about a historical dissertation from this index card and return it as a structured JSON object with the following exact format:
```json
{
"type": {
"type": "Dissertation or thesis" OR "Reference"
},
"author": {
"last_name": "string",
"first_name": "string"
},
"publication": {
"title": "string",
"year": integer,
"place": "string or empty string",
"pages": "string or empty string",
"publisher": "string or empty string",
"format": "string or empty string"
},
"library_reference": {
"shelfmark": "string or empty string",
"subjects": "string or empty string"
}
}
```
EXTRACTION RULES:
1. **Card Type**: If a card contains the note "s." on a separate line, it is a "Reference". Otherwise, it is a "Dissertation or thesis".
2. **Author**: Extract last_name and first_name. If only one name is given, put it in last_name and leave first_name empty.
3. **Publication**:
- title: The main title of the work
- year: Publication year as integer
- place: Publication place
- pages: Page count (remove " S." suffix if present)
- publisher: Publishing house/institution
- format: Usually "8°", "8'", or "4°" - single value only
4. **Library Reference**:
- shelfmark: Often begins with "Diss." or "AT", may be marked with "Standort:"
- subjects: Subject classifications or keywords
5. **Missing Information**: Use empty string "" for missing text fields, omit year fields entirely if not present.
6. **Ignore**: Disregard any information that doesn't fit into these categories.
Return ONLY the JSON object, no additional text or explanation.
no valid result
| Fuzzy Score | F1 micro / macro | Micro precision/recall | Tue/False Positives | |||||
| n/a | 0.00 | 0.00 | 0.09 | 0.00 | 263 | 1 | 10 | 2414 |
| Micro Precision | Micro Recall | Instances | TP | FP | FN | |||
| Pricing Date: n/a, n/a. | Tokens: 314.5K IT + 158.8K OT = 473.3K TT | Cost: 0.157$ + 0.079$ = 0.237$ |
{'document-type': ['index-card'], 'writing': ['typed', 'printed', 'handwritten'], 'century': [20, 19], 'language': ['de', 'fr', 'en', 'la', 'el', 'fi', 'sv', 'pl'], 'layout': ['index'], 'entry-type': ['bibliographic'], 'task': ['information-extraction']}
| Provider | openai |
| Model | gpt-4.1-nano-2025-04-14 |
| Temperature | 0.0 |
| Dataclass | Document |
| Normalized Score | 64.54 % |
| Test time | unknown seconds |
Extract the bibliographic information about a historical dissertation from this index card and return it as a structured JSON object with the following exact format:
```json
{
"type": {
"type": "Dissertation or thesis" OR "Reference"
},
"author": {
"last_name": "string",
"first_name": "string"
},
"publication": {
"title": "string",
"year": integer,
"place": "string or empty string",
"pages": "string or empty string",
"publisher": "string or empty string",
"format": "string or empty string"
},
"library_reference": {
"shelfmark": "string or empty string",
"subjects": "string or empty string"
}
}
```
EXTRACTION RULES:
1. **Card Type**: If a card contains the note "s." on a separate line, it is a "Reference". Otherwise, it is a "Dissertation or thesis".
2. **Author**: Extract last_name and first_name. If only one name is given, put it in last_name and leave first_name empty.
3. **Publication**:
- title: The main title of the work
- year: Publication year as integer
- place: Publication place
- pages: Page count (remove " S." suffix if present)
- publisher: Publishing house/institution
- format: Usually "8°", "8'", or "4°" - single value only
4. **Library Reference**:
- shelfmark: Often begins with "Diss." or "AT", may be marked with "Standort:"
- subjects: Subject classifications or keywords
5. **Missing Information**: Use empty string "" for missing text fields, omit year fields entirely if not present.
6. **Ignore**: Disregard any information that doesn't fit into these categories.
Return ONLY the JSON object, no additional text or explanation.
no valid result
| Fuzzy Score | F1 micro / macro | Micro precision/recall | Tue/False Positives | |||||
| n/a | 0.66 | 0.65 | 0.74 | 0.59 | 263 | 1428 | 494 | 987 |
| Micro Precision | Micro Recall | Instances | TP | FP | FN | |||
| Pricing Date: n/a, n/a. | Tokens: 642.8K IT + 25.7K OT = 668.5K TT | Cost: 0.064$ + 0.010$ = 0.075$ |
{'document-type': ['index-card'], 'writing': ['typed', 'printed', 'handwritten'], 'century': [20, 19], 'language': ['de', 'fr', 'en', 'la', 'el', 'fi', 'sv', 'pl'], 'layout': ['index'], 'entry-type': ['bibliographic'], 'task': ['information-extraction']}
| Provider | genai |
| Model | gemini-3-flash-preview |
| Temperature | 0.0 |
| Dataclass | Document |
| Normalized Score | 85.62 % |
| Test time | unknown seconds |
Extract the bibliographic information about a historical dissertation from this index card and return it as a structured JSON object with the following exact format:
```json
{
"type": {
"type": "Dissertation or thesis" OR "Reference"
},
"author": {
"last_name": "string",
"first_name": "string"
},
"publication": {
"title": "string",
"year": integer,
"place": "string or empty string",
"pages": "string or empty string",
"publisher": "string or empty string",
"format": "string or empty string"
},
"library_reference": {
"shelfmark": "string or empty string",
"subjects": "string or empty string"
}
}
```
EXTRACTION RULES:
1. **Card Type**: If a card contains the note "s." on a separate line, it is a "Reference". Otherwise, it is a "Dissertation or thesis".
2. **Author**: Extract last_name and first_name. If only one name is given, put it in last_name and leave first_name empty.
3. **Publication**:
- title: The main title of the work
- year: Publication year as integer
- place: Publication place
- pages: Page count (remove " S." suffix if present)
- publisher: Publishing house/institution
- format: Usually "8°", "8'", or "4°" - single value only
4. **Library Reference**:
- shelfmark: Often begins with "Diss." or "AT", may be marked with "Standort:"
- subjects: Subject classifications or keywords
5. **Missing Information**: Use empty string "" for missing text fields, omit year fields entirely if not present.
6. **Ignore**: Disregard any information that doesn't fit into these categories.
Return ONLY the JSON object, no additional text or explanation.
no valid result
| Fuzzy Score | F1 micro / macro | Micro precision/recall | Tue/False Positives | |||||
| n/a | 0.87 | 0.86 | 0.83 | 0.91 | 263 | 2187 | 450 | 228 |
| Micro Precision | Micro Recall | Instances | TP | FP | FN | |||
| Pricing Date: n/a, n/a. | Tokens: 406.2K IT + 38.3K OT = 444.5K TT | Cost: 0.203$ + 0.115$ = 0.318$ |
{'document-type': ['index-card'], 'writing': ['typed', 'printed', 'handwritten'], 'century': [20, 19], 'language': ['de', 'fr', 'en', 'la', 'el', 'fi', 'sv', 'pl'], 'layout': ['index'], 'entry-type': ['bibliographic'], 'task': ['information-extraction']}
| Provider | openai |
| Model | gpt-5.1-2025-11-13 |
| Temperature | 0.0 |
| Dataclass | Document |
| Normalized Score | 84.16 % |
| Test time | unknown seconds |
Extract the bibliographic information about a historical dissertation from this index card and return it as a structured JSON object with the following exact format:
```json
{
"type": {
"type": "Dissertation or thesis" OR "Reference"
},
"author": {
"last_name": "string",
"first_name": "string"
},
"publication": {
"title": "string",
"year": integer,
"place": "string or empty string",
"pages": "string or empty string",
"publisher": "string or empty string",
"format": "string or empty string"
},
"library_reference": {
"shelfmark": "string or empty string",
"subjects": "string or empty string"
}
}
```
EXTRACTION RULES:
1. **Card Type**: If a card contains the note "s." on a separate line, it is a "Reference". Otherwise, it is a "Dissertation or thesis".
2. **Author**: Extract last_name and first_name. If only one name is given, put it in last_name and leave first_name empty.
3. **Publication**:
- title: The main title of the work
- year: Publication year as integer
- place: Publication place
- pages: Page count (remove " S." suffix if present)
- publisher: Publishing house/institution
- format: Usually "8°", "8'", or "4°" - single value only
4. **Library Reference**:
- shelfmark: Often begins with "Diss." or "AT", may be marked with "Standort:"
- subjects: Subject classifications or keywords
5. **Missing Information**: Use empty string "" for missing text fields, omit year fields entirely if not present.
6. **Ignore**: Disregard any information that doesn't fit into these categories.
Return ONLY the JSON object, no additional text or explanation.
no valid result
| Fuzzy Score | F1 micro / macro | Micro precision/recall | Tue/False Positives | |||||
| n/a | 0.85 | 0.84 | 0.85 | 0.84 | 263 | 2033 | 353 | 382 |
| Micro Precision | Micro Recall | Instances | TP | FP | FN | |||
| Pricing Date: n/a, n/a. | Tokens: 421.9K IT + 135.1K OT = 557.0K TT | Cost: 0.527$ + 1.351$ = 1.879$ |
{'document-type': ['index-card'], 'writing': ['typed', 'printed', 'handwritten'], 'century': [20, 19], 'language': ['de', 'fr', 'en', 'la', 'el', 'fi', 'sv', 'pl'], 'layout': ['index'], 'entry-type': ['bibliographic'], 'task': ['information-extraction']}
| Provider | mistral |
| Model | ministral-14b-2512 |
| Temperature | 0.0 |
| Dataclass | Document |
| Normalized Score | 0.61 % |
| Test time | unknown seconds |
Extract the bibliographic information about a historical dissertation from this index card and return it as a structured JSON object with the following exact format:
```json
{
"type": {
"type": "Dissertation or thesis" OR "Reference"
},
"author": {
"last_name": "string",
"first_name": "string"
},
"publication": {
"title": "string",
"year": integer,
"place": "string or empty string",
"pages": "string or empty string",
"publisher": "string or empty string",
"format": "string or empty string"
},
"library_reference": {
"shelfmark": "string or empty string",
"subjects": "string or empty string"
}
}
```
EXTRACTION RULES:
1. **Card Type**: If a card contains the note "s." on a separate line, it is a "Reference". Otherwise, it is a "Dissertation or thesis".
2. **Author**: Extract last_name and first_name. If only one name is given, put it in last_name and leave first_name empty.
3. **Publication**:
- title: The main title of the work
- year: Publication year as integer
- place: Publication place
- pages: Page count (remove " S." suffix if present)
- publisher: Publishing house/institution
- format: Usually "8°", "8'", or "4°" - single value only
4. **Library Reference**:
- shelfmark: Often begins with "Diss." or "AT", may be marked with "Standort:"
- subjects: Subject classifications or keywords
5. **Missing Information**: Use empty string "" for missing text fields, omit year fields entirely if not present.
6. **Ignore**: Disregard any information that doesn't fit into these categories.
Return ONLY the JSON object, no additional text or explanation.
no valid result
| Fuzzy Score | F1 micro / macro | Micro precision/recall | Tue/False Positives | |||||
| n/a | 0.02 | 0.01 | 0.02 | 0.02 | 263 | 43 | 2075 | 2372 |
| Micro Precision | Micro Recall | Instances | TP | FP | FN | |||
| Pricing Date: n/a, n/a. | Tokens: 314.5K IT + 161.6K OT = 476.1K TT | Cost: 0.063$ + 0.032$ = 0.095$ |
{'document-type': ['index-card'], 'writing': ['typed', 'printed', 'handwritten'], 'century': [20, 19], 'language': ['de', 'fr', 'en', 'la', 'el', 'fi', 'sv', 'pl'], 'layout': ['index'], 'entry-type': ['bibliographic'], 'task': ['information-extraction']}
| Provider | openai |
| Model | qwen/qwen3-vl-30b-a3b-instruct |
| Temperature | 0.0 |
| Dataclass | Document |
| Normalized Score | 53.63 % |
| Test time | unknown seconds |
Extract the bibliographic information about a historical dissertation from this index card and return it as a structured JSON object with the following exact format:
```json
{
"type": {
"type": "Dissertation or thesis" OR "Reference"
},
"author": {
"last_name": "string",
"first_name": "string"
},
"publication": {
"title": "string",
"year": integer,
"place": "string or empty string",
"pages": "string or empty string",
"publisher": "string or empty string",
"format": "string or empty string"
},
"library_reference": {
"shelfmark": "string or empty string",
"subjects": "string or empty string"
}
}
```
EXTRACTION RULES:
1. **Card Type**: If a card contains the note "s." on a separate line, it is a "Reference". Otherwise, it is a "Dissertation or thesis".
2. **Author**: Extract last_name and first_name. If only one name is given, put it in last_name and leave first_name empty.
3. **Publication**:
- title: The main title of the work
- year: Publication year as integer
- place: Publication place
- pages: Page count (remove " S." suffix if present)
- publisher: Publishing house/institution
- format: Usually "8°", "8'", or "4°" - single value only
4. **Library Reference**:
- shelfmark: Often begins with "Diss." or "AT", may be marked with "Standort:"
- subjects: Subject classifications or keywords
5. **Missing Information**: Use empty string "" for missing text fields, omit year fields entirely if not present.
6. **Ignore**: Disregard any information that doesn't fit into these categories.
Return ONLY the JSON object, no additional text or explanation.
no valid result
| Fuzzy Score | F1 micro / macro | Micro precision/recall | Tue/False Positives | |||||
| n/a | 0.61 | 0.54 | 0.66 | 0.57 | 263 | 1372 | 693 | 1043 |
| Micro Precision | Micro Recall | Instances | TP | FP | FN | |||
| Pricing Date: n/a, n/a. | Tokens: 345.3K IT + 43.7K OT = 389.0K TT | Cost: 0.000$ + 0.000$ = 0.025$ |