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': ['letter'], 'writing': ['typed', 'handwritten'], 'century': [20], 'language': ['de'], 'layout': ['prose'], 'entry-type': ['person', 'location'], 'task': ['information-extraction']}
| Provider | openai |
| Model | gpt-5.6-sol |
| Temperature | 0.0 |
| Dataclass | Document |
| Normalized Score | 54.00 % |
| Test time | unknown seconds |
IDENTITY and PURPOSE:
You are presented with a series of images constituting a historical letter. Your task is to extract the
values of the following keys from the letter and return them in a JSON file where the values
corresponding to each key should be stored as a list, even if there is only a single value for a
key:
- letter_title: Title of the letter.
- sender_persons: Name(s) of the person(s) who wrote the letter.
- send_date: The exact or approximate date the letter was written.
- receiver_persons: Name(s) of the person(s) who received the letter.
Take a deep breath and think step by step about how to best accomplish this goal. Map out all the claims
and implications on a virtual whiteboard in your mind. Do not use OCR. Use the ISO format YYYY-MM-DD for
dates. If a piece of information is not included in the letter, set the value for the corresponding key
to "null". Do not return anything except the JSON file.
EXAMPLE:
{
"letter_title": ["Petition for Environmental Protection"],
"send_date": ["1993-03-12"],
"sender_persons": ["Lisa Simpson"],
"receiver_persons": ["Mayor Joe Quimby", "Seymour Skinner"],
}
OUTPUT:
no valid result| Fuzzy Score | F1 micro / macro | Micro precision/recall | Tue/False Positives | |||||
| n/a | 0.67 | 0.54 | 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: 125.6K IT + 4.1K OT = 129.7K TT | Cost: 0.628$ + 0.123$ = 0.751$ |
{'document-type': ['letter'], 'writing': ['typed', 'handwritten'], 'century': [20], 'language': ['de'], 'layout': ['prose'], 'entry-type': ['person', 'location'], 'task': ['information-extraction']}
| Provider | openai |
| Model | gpt-5.6-terra |
| Temperature | 0.0 |
| Dataclass | Document |
| Normalized Score | 56.00 % |
| Test time | unknown seconds |
IDENTITY and PURPOSE:
You are presented with a series of images constituting a historical letter. Your task is to extract the
values of the following keys from the letter and return them in a JSON file where the values
corresponding to each key should be stored as a list, even if there is only a single value for a
key:
- letter_title: Title of the letter.
- sender_persons: Name(s) of the person(s) who wrote the letter.
- send_date: The exact or approximate date the letter was written.
- receiver_persons: Name(s) of the person(s) who received the letter.
Take a deep breath and think step by step about how to best accomplish this goal. Map out all the claims
and implications on a virtual whiteboard in your mind. Do not use OCR. Use the ISO format YYYY-MM-DD for
dates. If a piece of information is not included in the letter, set the value for the corresponding key
to "null". Do not return anything except the JSON file.
EXAMPLE:
{
"letter_title": ["Petition for Environmental Protection"],
"send_date": ["1993-03-12"],
"sender_persons": ["Lisa Simpson"],
"receiver_persons": ["Mayor Joe Quimby", "Seymour Skinner"],
}
OUTPUT:
no valid result| Fuzzy Score | F1 micro / macro | Micro precision/recall | Tue/False Positives | |||||
| n/a | 0.52 | 0.56 | 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: 219.7K IT + 5.6K OT = 225.3K TT | Cost: 0.549$ + 0.084$ = 0.633$ |
{'document-type': ['letter'], 'writing': ['typed', 'handwritten'], 'century': [20], 'language': ['de'], 'layout': ['prose'], 'entry-type': ['person', 'location'], 'task': ['information-extraction']}
| Provider | openrouter |
| Model | moonshotai/kimi-k3 |
| Temperature | 0.0 |
| Dataclass | Document |
| Normalized Score | 62.00 % |
| Test time | unknown seconds |
IDENTITY and PURPOSE:
You are presented with a series of images constituting a historical letter. Your task is to extract the
values of the following keys from the letter and return them in a JSON file where the values
corresponding to each key should be stored as a list, even if there is only a single value for a
key:
- letter_title: Title of the letter.
- sender_persons: Name(s) of the person(s) who wrote the letter.
- send_date: The exact or approximate date the letter was written.
- receiver_persons: Name(s) of the person(s) who received the letter.
Take a deep breath and think step by step about how to best accomplish this goal. Map out all the claims
and implications on a virtual whiteboard in your mind. Do not use OCR. Use the ISO format YYYY-MM-DD for
dates. If a piece of information is not included in the letter, set the value for the corresponding key
to "null". Do not return anything except the JSON file.
EXAMPLE:
{
"letter_title": ["Petition for Environmental Protection"],
"send_date": ["1993-03-12"],
"sender_persons": ["Lisa Simpson"],
"receiver_persons": ["Mayor Joe Quimby", "Seymour Skinner"],
}
OUTPUT:
no valid result| Fuzzy Score | F1 micro / macro | Micro precision/recall | Tue/False Positives | |||||
| n/a | 0.57 | 0.62 | 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: 263.7K IT + 78.3K OT = 342.0K TT | Cost: 0.766$ + 1.156$ = 1.965$ |
{'document-type': ['letter'], 'writing': ['typed', 'handwritten'], 'century': [20], 'language': ['de'], 'layout': ['prose'], 'entry-type': ['person', 'location'], 'task': ['information-extraction']}
| Provider | anthropic |
| Model | claude-opus-5 |
| Temperature | 0.0 |
| Dataclass | Document |
| Normalized Score | 65.00 % |
| Test time | unknown seconds |
IDENTITY and PURPOSE:
You are presented with a series of images constituting a historical letter. Your task is to extract the
values of the following keys from the letter and return them in a JSON file where the values
corresponding to each key should be stored as a list, even if there is only a single value for a
key:
- letter_title: Title of the letter.
- sender_persons: Name(s) of the person(s) who wrote the letter.
- send_date: The exact or approximate date the letter was written.
- receiver_persons: Name(s) of the person(s) who received the letter.
Take a deep breath and think step by step about how to best accomplish this goal. Map out all the claims
and implications on a virtual whiteboard in your mind. Do not use OCR. Use the ISO format YYYY-MM-DD for
dates. If a piece of information is not included in the letter, set the value for the corresponding key
to "null". Do not return anything except the JSON file.
EXAMPLE:
{
"letter_title": ["Petition for Environmental Protection"],
"send_date": ["1993-03-12"],
"sender_persons": ["Lisa Simpson"],
"receiver_persons": ["Mayor Joe Quimby", "Seymour Skinner"],
}
OUTPUT:
no valid result| Fuzzy Score | F1 micro / macro | Micro precision/recall | Tue/False Positives | |||||
| n/a | 0.66 | 0.65 | 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: 450.6K IT + 8.3K OT = 458.9K TT | Cost: 2.253$ + 0.208$ = 2.461$ |
{'document-type': ['letter'], 'writing': ['typed', 'handwritten'], 'century': [20], 'language': ['de'], 'layout': ['prose'], 'entry-type': ['person', 'location'], 'task': ['information-extraction']}
| Provider | openai |
| Model | gpt-5.6-luna |
| Temperature | 0.0 |
| Dataclass | Document |
| Normalized Score | 56.00 % |
| Test time | unknown seconds |
IDENTITY and PURPOSE:
You are presented with a series of images constituting a historical letter. Your task is to extract the
values of the following keys from the letter and return them in a JSON file where the values
corresponding to each key should be stored as a list, even if there is only a single value for a
key:
- letter_title: Title of the letter.
- sender_persons: Name(s) of the person(s) who wrote the letter.
- send_date: The exact or approximate date the letter was written.
- receiver_persons: Name(s) of the person(s) who received the letter.
Take a deep breath and think step by step about how to best accomplish this goal. Map out all the claims
and implications on a virtual whiteboard in your mind. Do not use OCR. Use the ISO format YYYY-MM-DD for
dates. If a piece of information is not included in the letter, set the value for the corresponding key
to "null". Do not return anything except the JSON file.
EXAMPLE:
{
"letter_title": ["Petition for Environmental Protection"],
"send_date": ["1993-03-12"],
"sender_persons": ["Lisa Simpson"],
"receiver_persons": ["Mayor Joe Quimby", "Seymour Skinner"],
}
OUTPUT:
no valid result| Fuzzy Score | F1 micro / macro | Micro precision/recall | Tue/False Positives | |||||
| n/a | 0.54 | 0.56 | 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: 219.7K IT + 9.8K OT = 229.4K TT | Cost: 0.220$ + 0.059$ = 0.278$ |
{'document-type': ['letter'], 'writing': ['typed', 'handwritten'], 'century': [20], 'language': ['de'], 'layout': ['prose'], 'entry-type': ['person', 'location'], 'task': ['information-extraction']}
| Provider | openai |
| Model | gpt-5.6-luna |
| Temperature | 0.0 |
| Dataclass | Document |
| Normalized Score | 54.00 % |
| Test time | unknown seconds |
IDENTITY and PURPOSE:
You are presented with a series of images constituting a historical letter. Your task is to extract the
values of the following keys from the letter and return them in a JSON file where the values
corresponding to each key should be stored as a list, even if there is only a single value for a
key:
- letter_title: Title of the letter.
- sender_persons: Name(s) of the person(s) who wrote the letter.
- send_date: The exact or approximate date the letter was written.
- receiver_persons: Name(s) of the person(s) who received the letter.
Take a deep breath and think step by step about how to best accomplish this goal. Map out all the claims
and implications on a virtual whiteboard in your mind. Do not use OCR. Use the ISO format YYYY-MM-DD for
dates. If a piece of information is not included in the letter, set the value for the corresponding key
to "null". Do not return anything except the JSON file.
EXAMPLE:
{
"letter_title": ["Petition for Environmental Protection"],
"send_date": ["1993-03-12"],
"sender_persons": ["Lisa Simpson"],
"receiver_persons": ["Mayor Joe Quimby", "Seymour Skinner"],
}
OUTPUT:
no valid result| Fuzzy Score | F1 micro / macro | Micro precision/recall | Tue/False Positives | |||||
| n/a | 0.66 | 0.54 | 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: 125.6K IT + 5.8K OT = 131.4K TT | Cost: 0.126$ + 0.035$ = 0.161$ |
{'document-type': ['letter'], 'writing': ['typed', 'handwritten'], 'century': [20], 'language': ['de'], 'layout': ['prose'], 'entry-type': ['person', 'location'], 'task': ['information-extraction']}
| Provider | genai |
| Model | gemini-3.5-flash-lite |
| Temperature | 0.0 |
| Dataclass | Document |
| Normalized Score | 56.00 % |
| Test time | unknown seconds |
IDENTITY and PURPOSE:
You are presented with a series of images constituting a historical letter. Your task is to extract the
values of the following keys from the letter and return them in a JSON file where the values
corresponding to each key should be stored as a list, even if there is only a single value for a
key:
- letter_title: Title of the letter.
- sender_persons: Name(s) of the person(s) who wrote the letter.
- send_date: The exact or approximate date the letter was written.
- receiver_persons: Name(s) of the person(s) who received the letter.
Take a deep breath and think step by step about how to best accomplish this goal. Map out all the claims
and implications on a virtual whiteboard in your mind. Do not use OCR. Use the ISO format YYYY-MM-DD for
dates. If a piece of information is not included in the letter, set the value for the corresponding key
to "null". Do not return anything except the JSON file.
EXAMPLE:
{
"letter_title": ["Petition for Environmental Protection"],
"send_date": ["1993-03-12"],
"sender_persons": ["Lisa Simpson"],
"receiver_persons": ["Mayor Joe Quimby", "Seymour Skinner"],
}
OUTPUT:
no valid result| Fuzzy Score | F1 micro / macro | Micro precision/recall | Tue/False Positives | |||||
| n/a | 0.57 | 0.56 | 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: 126.2K IT + 5.0K OT = 131.2K TT | Cost: 0.038$ + 0.013$ = 0.050$ |
{'document-type': ['letter'], 'writing': ['typed', 'handwritten'], 'century': [20], 'language': ['de'], 'layout': ['prose'], 'entry-type': ['person', 'location'], 'task': ['information-extraction']}
| Provider | anthropic |
| Model | claude-opus-5 |
| Temperature | 0.0 |
| Dataclass | Document |
| Normalized Score | 66.00 % |
| Test time | unknown seconds |
IDENTITY and PURPOSE:
You are presented with a series of images constituting a historical letter. Your task is to extract the
values of the following keys from the letter and return them in a JSON file where the values
corresponding to each key should be stored as a list, even if there is only a single value for a
key:
- letter_title: Title of the letter.
- sender_persons: Name(s) of the person(s) who wrote the letter.
- send_date: The exact or approximate date the letter was written.
- receiver_persons: Name(s) of the person(s) who received the letter.
Take a deep breath and think step by step about how to best accomplish this goal. Map out all the claims
and implications on a virtual whiteboard in your mind. Do not use OCR. Use the ISO format YYYY-MM-DD for
dates. If a piece of information is not included in the letter, set the value for the corresponding key
to "null". Do not return anything except the JSON file.
EXAMPLE:
{
"letter_title": ["Petition for Environmental Protection"],
"send_date": ["1993-03-12"],
"sender_persons": ["Lisa Simpson"],
"receiver_persons": ["Mayor Joe Quimby", "Seymour Skinner"],
}
OUTPUT:
no valid result| Fuzzy Score | F1 micro / macro | Micro precision/recall | Tue/False Positives | |||||
| n/a | 0.80 | 0.66 | 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: 166.9K IT + 3.4K OT = 170.4K TT | Cost: 0.835$ + 0.086$ = 0.921$ |
{'document-type': ['letter'], 'writing': ['typed', 'handwritten'], 'century': [20], 'language': ['de'], 'layout': ['prose'], 'entry-type': ['person', 'location'], 'task': ['information-extraction']}
| Provider | openrouter |
| Model | moonshotai/kimi-k3 |
| Temperature | 0.0 |
| Dataclass | Document |
| Normalized Score | 56.00 % |
| Test time | unknown seconds |
IDENTITY and PURPOSE:
You are presented with a series of images constituting a historical letter. Your task is to extract the
values of the following keys from the letter and return them in a JSON file where the values
corresponding to each key should be stored as a list, even if there is only a single value for a
key:
- letter_title: Title of the letter.
- sender_persons: Name(s) of the person(s) who wrote the letter.
- send_date: The exact or approximate date the letter was written.
- receiver_persons: Name(s) of the person(s) who received the letter.
Take a deep breath and think step by step about how to best accomplish this goal. Map out all the claims
and implications on a virtual whiteboard in your mind. Do not use OCR. Use the ISO format YYYY-MM-DD for
dates. If a piece of information is not included in the letter, set the value for the corresponding key
to "null". Do not return anything except the JSON file.
EXAMPLE:
{
"letter_title": ["Petition for Environmental Protection"],
"send_date": ["1993-03-12"],
"sender_persons": ["Lisa Simpson"],
"receiver_persons": ["Mayor Joe Quimby", "Seymour Skinner"],
}
OUTPUT:
no valid result| Fuzzy Score | F1 micro / macro | Micro precision/recall | Tue/False Positives | |||||
| n/a | 0.58 | 0.56 | 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: 414.8K IT + 131.9K OT = 546.7K TT | Cost: 1.244$ + 1.979$ = 3.223$ |
{'document-type': ['letter'], 'writing': ['typed', 'handwritten'], 'century': [20], 'language': ['de'], 'layout': ['prose'], 'entry-type': ['person', 'location'], 'task': ['information-extraction']}
| Provider | genai |
| Model | gemini-3.6-flash |
| Temperature | 0.0 |
| Dataclass | Document |
| Normalized Score | 82.00 % |
| Test time | unknown seconds |
IDENTITY and PURPOSE:
You are presented with a series of images constituting a historical letter. Your task is to extract the
values of the following keys from the letter and return them in a JSON file where the values
corresponding to each key should be stored as a list, even if there is only a single value for a
key:
- letter_title: Title of the letter.
- sender_persons: Name(s) of the person(s) who wrote the letter.
- send_date: The exact or approximate date the letter was written.
- receiver_persons: Name(s) of the person(s) who received the letter.
Take a deep breath and think step by step about how to best accomplish this goal. Map out all the claims
and implications on a virtual whiteboard in your mind. Do not use OCR. Use the ISO format YYYY-MM-DD for
dates. If a piece of information is not included in the letter, set the value for the corresponding key
to "null". Do not return anything except the JSON file.
EXAMPLE:
{
"letter_title": ["Petition for Environmental Protection"],
"send_date": ["1993-03-12"],
"sender_persons": ["Lisa Simpson"],
"receiver_persons": ["Mayor Joe Quimby", "Seymour Skinner"],
}
OUTPUT:
no valid result| Fuzzy Score | F1 micro / macro | Micro precision/recall | Tue/False Positives | |||||
| n/a | 0.89 | 0.82 | 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: 46.6K IT + 1.0K OT = 47.6K TT | Cost: 0.070$ + 0.008$ = 0.077$ |