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': ['book-page'], 'writing': ['printed'], 'century': [20], 'language': ['en', 'de'], 'layout': ['list'], 'entry-type': ['company'], 'task': ['information-extraction']}
| Provider | genai |
| Model | gemini-2.5-pro |
| Temperature | 0.5 |
| Dataclass | ListPage |
| Normalized Score | 47.93 % |
| Test time | unknown seconds |
- Answer in valid JSON.
- The page ID is given as {page_id}.
no valid result| Fuzzy Score | F1 micro / macro | Micro precision/recall | Tue/False Positives | |||||
| n/a | 0.48 | 0.48 | 0.47 | 0.49 | 15 | 484 | 543 | 508 |
| Micro Precision | Micro Recall | Instances | TP | FP | FN | |||
| Pricing Date: 7 months ago, 2025-10-28. | Tokens: 4.2K IT + 13.9K OT = 18.1K TT | Cost: 0.005$ + 0.139$ = 0.144$ |
{'document-type': ['book-page'], 'writing': ['printed'], 'century': [20], 'language': ['en', 'de'], 'layout': ['list'], 'entry-type': ['company'], 'task': ['information-extraction']}
| Provider | anthropic |
| Model | claude-sonnet-4-20250514 |
| Temperature | 0.5 |
| Dataclass | ListPage |
| Normalized Score | 44.20 % |
| Test time | unknown seconds |
The image you are presented with stems from a digitized book containing lists of companies.
Your task is to extract structured information about each company listed on the page.
About the source:
- The image stems from a trade index of the British Swiss Chamber of Commerce.
- The image can show an alphabetical or a thematic list of companies.
- The companies are mostly located in Switzerland and the UK.
- The image stems from a trade index between 1925 and 1958.
- Most pages have one column but some years have two columns.
- The source itself is in English and German but the company names can be in English, German, French or Italian.
About the entries:
- Each entry describes a single company or person.
- Alphabetical entries have filling dots between the company name and the page number. Dots and page numbers are not part of the data and should be ignored.
- Alphabetical entries seldom to never have locations.
- Thematic entries often have locations.
- Thematic entries are listed under headings that describe the type of business.
- Some thematic headings are only references to other headings, e.g. "X, s. Y".
About the output:
- Answer in valid JSON. The JSON should be an array of objects with the following fields:
- The page ID is given as {page_id}.
- Do not add country information, if it is not directly written with the location.
{
"entry_id": "A unique identifier for the entry, e.g. '{page_id}-1'",
"company_name": "The name of the company or person",
"location": "The location of the company, e.g. 'Zurich' or 'London, UK'. If no location is given, set to null."
]
}
no valid result| Fuzzy Score | F1 micro / macro | Micro precision/recall | Tue/False Positives | |||||
| n/a | 0.43 | 0.44 | 0.43 | 0.43 | 15 | 426 | 566 | 566 |
| Micro Precision | Micro Recall | Instances | TP | FP | FN | |||
| Pricing Date: 7 months ago, 2025-10-28. | Tokens: 28.0K IT + 17.4K OT = 45.4K TT | Cost: 0.084$ + 0.261$ = 0.345$ |
{'document-type': ['book-page'], 'writing': ['printed'], 'century': [20], 'language': ['en', 'de'], 'layout': ['list'], 'entry-type': ['company'], 'task': ['information-extraction']}
| Provider | openai |
| Model | o3 |
| Temperature | 0.5 |
| Dataclass | ListPage |
| Normalized Score | 41.93 % |
| Test time | unknown seconds |
- Answer in valid JSON.
- The page ID is given as {page_id}.
no valid result| Fuzzy Score | F1 micro / macro | Micro precision/recall | Tue/False Positives | |||||
| n/a | 0.44 | 0.42 | 0.44 | 0.43 | 15 | 424 | 533 | 568 |
| Micro Precision | Micro Recall | Instances | TP | FP | FN | |||
| Pricing Date: 7 months ago, 2025-10-28. | Tokens: 12.3K IT + 31.3K OT = 43.6K TT | Cost: 0.025$ + 0.250$ = 0.275$ |
{'document-type': ['book-page'], 'writing': ['printed'], 'century': [20], 'language': ['en', 'de'], 'layout': ['list'], 'entry-type': ['company'], 'task': ['information-extraction']}
| Provider | openai |
| Model | gpt-4.1 |
| Temperature | 0.5 |
| Dataclass | ListPage |
| Normalized Score | 29.53 % |
| Test time | unknown seconds |
- Answer in valid JSON.
- The page ID is given as {page_id}.
no valid result| Fuzzy Score | F1 micro / macro | Micro precision/recall | Tue/False Positives | |||||
| n/a | 0.33 | 0.30 | 0.35 | 0.31 | 15 | 305 | 578 | 687 |
| Micro Precision | Micro Recall | Instances | TP | FP | FN | |||
| Pricing Date: 7 months ago, 2025-10-28. | Tokens: 13.7K IT + 7.6K OT = 21.4K TT | Cost: 0.027$ + 0.061$ = 0.088$ |
{'document-type': ['book-page'], 'writing': ['printed'], 'century': [20], 'language': ['en', 'de'], 'layout': ['list'], 'entry-type': ['company'], 'task': ['information-extraction']}
| Provider | openrouter |
| Model | qwen/qwen3-vl-30b-a3b-instruct |
| Temperature | 0.5 |
| Dataclass | ListPage |
| Normalized Score | 32.73 % |
| Test time | unknown seconds |
The image you are presented with stems from a digitized book containing lists of companies.
Your task is to extract structured information about each company listed on the page.
About the source:
- The image stems from a trade index of the British Swiss Chamber of Commerce.
- The image can show an alphabetical or a thematic list of companies.
- The companies are mostly located in Switzerland and the UK.
- The image stems from a trade index between 1925 and 1958.
- Most pages have one column but some years have two columns.
- The source itself is in English and German but the company names can be in English, German, French or Italian.
About the entries:
- Each entry describes a single company or person.
- Alphabetical entries have filling dots between the company name and the page number. Dots and page numbers are not part of the data and should be ignored.
- Alphabetical entries seldom to never have locations.
- Thematic entries often have locations.
- Thematic entries are listed under headings that describe the type of business.
- Some thematic headings are only references to other headings, e.g. "X, s. Y".
About the output:
- Answer in valid JSON. The JSON should be an array of objects with the following fields:
- The page ID is given as {page_id}.
- Do not add country information, if it is not directly written with the location.
{
"entry_id": "A unique identifier for the entry, e.g. '{page_id}-1'",
"company_name": "The name of the company or person",
"location": "The location of the company, e.g. 'Zurich' or 'London, UK'. If no location is given, set to null."
]
}
no valid result| Fuzzy Score | F1 micro / macro | Micro precision/recall | Tue/False Positives | |||||
| n/a | 0.32 | 0.33 | 0.31 | 0.34 | 15 | 333 | 749 | 659 |
| Micro Precision | Micro Recall | Instances | TP | FP | FN | |||
| Pricing Date: 7 months ago, 2025-10-28. | Tokens: 16.8K IT + 16.2K OT = 32.9K TT | Cost: 0.003$ + 0.011$ = 0.015$ |
{'document-type': ['book-page'], 'writing': ['printed'], 'century': [20], 'language': ['en', 'de'], 'layout': ['list'], 'entry-type': ['company'], 'task': ['information-extraction']}
| Provider | mistral |
| Model | mistral-medium-2508 |
| Temperature | 0.5 |
| Dataclass | ListPage |
| Normalized Score | 39.27 % |
| Test time | unknown seconds |
- Answer in valid JSON.
- The page ID is given as {page_id}.
no valid result| Fuzzy Score | F1 micro / macro | Micro precision/recall | Tue/False Positives | |||||
| n/a | 0.38 | 0.39 | 0.38 | 0.39 | 15 | 384 | 634 | 608 |
| Micro Precision | Micro Recall | Instances | TP | FP | FN | |||
| Pricing Date: 7 months ago, 2025-10-28. | Tokens: 17.3K IT + 13.6K OT = 31.0K TT | Cost: 0.007$ + 0.027$ = 0.034$ |
{'document-type': ['book-page'], 'writing': ['printed'], 'century': [20], 'language': ['en', 'de'], 'layout': ['list'], 'entry-type': ['company'], 'task': ['information-extraction']}
| Provider | genai |
| Model | gemini-2.5-flash-lite-preview-09-2025 |
| Temperature | 0.5 |
| Dataclass | ListPage |
| Normalized Score | 37.93 % |
| Test time | unknown seconds |
- Answer in valid JSON.
- The page ID is given as {page_id}.
no valid result| Fuzzy Score | F1 micro / macro | Micro precision/recall | Tue/False Positives | |||||
| n/a | 0.38 | 0.38 | 0.37 | 0.39 | 15 | 385 | 664 | 607 |
| Micro Precision | Micro Recall | Instances | TP | FP | FN | |||
| Pricing Date: 7 months ago, 2025-10-28. | Tokens: 4.2K IT + 16.9K OT = 21.1K TT | Cost: 0.000$ + 0.007$ = 0.007$ |
{'document-type': ['book-page'], 'writing': ['printed'], 'century': [20], 'language': ['en', 'de'], 'layout': ['list'], 'entry-type': ['company'], 'task': ['information-extraction']}
| Provider | openai |
| Model | gpt-5-nano |
| Temperature | 0.5 |
| Dataclass | ListPage |
| Normalized Score | 39.67 % |
| Test time | unknown seconds |
The image you are presented with stems from a digitized book containing lists of companies.
Your task is to extract structured information about each company listed on the page.
About the source:
- The image stems from a trade index of the British Swiss Chamber of Commerce.
- The image can show an alphabetical or a thematic list of companies.
- The companies are mostly located in Switzerland and the UK.
- The image stems from a trade index between 1925 and 1958.
- Most pages have one column but some years have two columns.
- The source itself is in English and German but the company names can be in English, German, French or Italian.
About the entries:
- Each entry describes a single company or person.
- Alphabetical entries have filling dots between the company name and the page number. Dots and page numbers are not part of the data and should be ignored.
- Alphabetical entries seldom to never have locations.
- Thematic entries often have locations.
- Thematic entries are listed under headings that describe the type of business.
- Some thematic headings are only references to other headings, e.g. "X, s. Y".
About the output:
- Answer in valid JSON. The JSON should be an array of objects with the following fields:
- The page ID is given as {page_id}.
- Do not add country information, if it is not directly written with the location.
{
"entry_id": "A unique identifier for the entry, e.g. '{page_id}-1'",
"company_name": "The name of the company or person",
"location": "The location of the company, e.g. 'Zurich' or 'London, UK'. If no location is given, set to null."
]
}
no valid result| Fuzzy Score | F1 micro / macro | Micro precision/recall | Tue/False Positives | |||||
| n/a | 0.40 | 0.40 | 0.44 | 0.36 | 15 | 355 | 450 | 637 |
| Micro Precision | Micro Recall | Instances | TP | FP | FN | |||
| Pricing Date: 7 months ago, 2025-10-28. | Tokens: 23.1K IT + 86.0K OT = 109.1K TT | Cost: 0.001$ + 0.034$ = 0.036$ |
{'document-type': ['book-page'], 'writing': ['printed'], 'century': [20], 'language': ['en', 'de'], 'layout': ['list'], 'entry-type': ['company'], 'task': ['information-extraction']}
| Provider | openrouter |
| Model | qwen/qwen3-vl-30b-a3b-instruct |
| Temperature | 0.5 |
| Dataclass | ListPage |
| Normalized Score | 25.60 % |
| Test time | unknown seconds |
- Answer in valid JSON.
- The page ID is given as {page_id}.
no valid result| Fuzzy Score | F1 micro / macro | Micro precision/recall | Tue/False Positives | |||||
| n/a | 0.24 | 0.26 | 0.24 | 0.24 | 15 | 242 | 754 | 750 |
| Micro Precision | Micro Recall | Instances | TP | FP | FN | |||
| Pricing Date: 7 months ago, 2025-10-28. | Tokens: 11.6K IT + 13.5K OT = 25.1K TT | Cost: 0.002$ + 0.009$ = 0.012$ |
{'document-type': ['book-page'], 'writing': ['printed'], 'century': [20], 'language': ['en', 'de'], 'layout': ['list'], 'entry-type': ['company'], 'task': ['information-extraction']}
| Provider | genai |
| Model | gemini-2.5-flash |
| Temperature | 0.5 |
| Dataclass | ListPage |
| Normalized Score | 50.07 % |
| Test time | unknown seconds |
The image you are presented with stems from a digitized book containing lists of companies.
Your task is to extract structured information about each company listed on the page.
About the source:
- The image stems from a trade index of the British Swiss Chamber of Commerce.
- The image can show an alphabetical or a thematic list of companies.
- The companies are mostly located in Switzerland and the UK.
- The image stems from a trade index between 1925 and 1958.
- Most pages have one column but some years have two columns.
- The source itself is in English and German but the company names can be in English, German, French or Italian.
About the entries:
- Each entry describes a single company or person.
- Alphabetical entries have filling dots between the company name and the page number. Dots and page numbers are not part of the data and should be ignored.
- Alphabetical entries seldom to never have locations.
- Thematic entries often have locations.
- Thematic entries are listed under headings that describe the type of business.
- Some thematic headings are only references to other headings, e.g. "X, s. Y".
About the output:
- Answer in valid JSON. The JSON should be an array of objects with the following fields:
- The page ID is given as {page_id}.
- Do not add country information, if it is not directly written with the location.
{
"entry_id": "A unique identifier for the entry, e.g. '{page_id}-1'",
"company_name": "The name of the company or person",
"location": "The location of the company, e.g. 'Zurich' or 'London, UK'. If no location is given, set to null."
]
}
no valid result| Fuzzy Score | F1 micro / macro | Micro precision/recall | Tue/False Positives | |||||
| n/a | 0.48 | 0.50 | 0.48 | 0.48 | 15 | 473 | 520 | 519 |
| Micro Precision | Micro Recall | Instances | TP | FP | FN | |||
| Pricing Date: 7 months ago, 2025-10-28. | Tokens: 9.8K IT + 16.1K OT = 25.9K TT | Cost: 0.003$ + 0.040$ = 0.043$ |