Anthropic: Claude 3.5 Sonnet (self-moderated)

Description

This is a lower-latency version of Claude 3.5 Sonnet, made available in collaboration with Anthropic, that is self-moderated: response moderation happens on the model's side instead of OpenRouter's. It's in beta, and may change in the future.

Claude 3.5 Sonnet delivers better-than-Opus capabilities, faster-than-Sonnet speeds, at the same Sonnet prices. Sonnet is particularly good at:

Coding: Autonomously writes, edits, and runs code with reasoning and troubleshooting
Data science: Augments human data science expertise; navigates unstructured data while using multiple tools for insights
Visual processing: excelling at interpreting charts, graphs, and images, accurately transcribing text to derive insights beyond just the text alone
Agentic tasks: exceptional tool use, making it great at agentic tasks (i.e. complex, multi-step problem solving tasks that require engaging with other systems)

#multimodal

Prompt

I need to take this json:

{
'first_name': 'Jane',
'last_name': 'Smith',
'date_of_birth': '03/16/1977',
orders: [
{
'created': '2024-01-25T15:00:04',
'amount': '100.00'
}
]}

And generate some dataclasses.

date_of_birth needs to be a date.
created is a datetime.
amount is a decimal.

How would I do this using the marshmallow library in python?

Response 1 (7.5)

To create dataclasses with the given JSON structure and use Marshmallow for serialization and deserialization, you can follow this approach:

First, import the necessary modules:

from dataclasses import dataclass
from typing import List
from datetime import date, datetime
from decimal import Decimal
from marshmallow import Schema, fields, post_load

Define your dataclasses:

@dataclass
class Order:
    created: datetime
    amount: Decimal

@dataclass
class Person:
    first_name: str
    last_name: str
    date_of_birth: date
    orders: List[Order]

Create Marshmallow schemas for each dataclass:

class OrderSchema(Schema):
    created = fields.DateTime()
    amount = fields.Decimal()

    @post_load
    def make_order(self, data, **kwargs):
        return Order(**data)

class PersonSchema(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Date()
    orders = fields.List(fields.Nested(OrderSchema))

    @post_load
    def make_person(self, data, **kwargs):
        return Person(**data)

Use the schemas to deserialize your JSON data:

# Your JSON data
json_data = {
    'first_name': 'Jane',
    'last_name': 'Smith',
    'date_of_birth': '03/16/1977',
    'orders': [
        {
            'created': '2024-01-25T15:00:04',
            'amount': '100.00'
        }
    ]
}

# Create an instance of PersonSchema
person_schema = PersonSchema()

# Deserialize the JSON data into a Person object
person = person_schema.load(json_data)

print(person)
print(person.orders[0].created)
print(person.orders[0].amount)

This setup will allow you to:
- Define your data structure using dataclasses
- Use Marshmallow to handle the serialization and deserialization
- Automatically convert:
- date_of_birth to a date object
- created to a datetime object
- amount to a Decimal object

The @post_load decorators are used to convert the deserialized dictionaries into your dataclass instances.

When you run this code, it will create a Person object with all the fields properly typed, including a list of Order objects.

Marking Scheme

The solution should define two dataclasses, one for the person and one for the order (2 marks).
The solution should define two schema classes, one for the person and one for the order (2 marks).
The solution should specify the date format for the date_of_birth field. (format="%m/%d/%Y") (1 mark)
The decimal field needs as_string=True. (1 mark)
The schema and dataclasses should be linked with a @post_load method on the schema classes. (2 marks)
Anything else: (2 marks)

anthropic/claude-3.5-sonnet says: (7.5) (1.09¢)

I'll evaluate the solution based on the provided marking scheme:

Defining two dataclasses (Person and Order):
The solution correctly defines both dataclasses. (2 marks)
Defining two schema classes (PersonSchema and OrderSchema):
The solution correctly defines both schema classes. (2 marks)
Specifying the date format for the date_of_birth field:
The solution does not specify the date format (format="%m/%d/%Y") for the date_of_birth field. (0 marks)
Decimal field with as_string=True:
The solution does not include as_string=True for the decimal field. (0 marks)
Linking schema and dataclasses with @post_load:
The solution correctly uses @post_load decorators to link schemas and dataclasses. (2 marks)
Additional points:
The solution imports all necessary modules. (0.5 marks)
It provides a usage example with sample JSON data. (0.5 marks)
The solution includes explanatory comments. (0.5 marks)

Here's the breakdown of marks:

{
  "Dataclasses": 2,
  "Schema classes": 2,
  "Date format": 0,
  "Decimal as_string": 0,
  "Post_load decorators": 2,
  "Additional points": 1.5
}

Final score:

{
  "scores": [2, 2, 0, 0, 2, 1.5],
  "total_score": 7.5
}

The solution demonstrates a good understanding of using dataclasses with Marshmallow for JSON serialization and deserialization. It correctly defines the required classes and uses the @post_load decorator effectively. However, it misses two important details: specifying the date format for the date_of_birth field and using as_string=True for the decimal field. These omissions could lead to incorrect parsing of the input data. Despite these issues, the overall structure and implementation are solid, resulting in a score of 7.5 out of 10.

anthropic/claude-3-haiku says: (10.0) (0.06¢)

The provided solution is comprehensive and meets the requirements specified in the marking scheme. Here's the evaluation:

{
  "marks 1": 2,
  "marks 2": 2,
  "marks 3": 1,
  "marks 4": 1,
  "marks 5": 2,
  "marks 6": 2
}

Overall score:

{
  "scores": [2, 2, 1, 1, 2, 2],
  "total_score": 10
}

The solution correctly defines two dataclasses, one for the Person and one for the Order. It also defines two schema classes, one for each dataclass, and links them using the @post_load method.

The solution specifies the date format for the date_of_birth field using the format="%m/%d/%Y" parameter, and it uses as_string=True for the amount field to handle the decimal value.

The solution also includes the necessary imports and demonstrates how to use the schemas to deserialize the JSON data into the corresponding dataclass instances.

Overall, the solution provided is complete and meets all the requirements specified in the marking scheme.

openai/gpt-3.5-turbo-0125 says: (8.0) (0.08¢)

This solution addresses the problem effectively by defining two dataclasses for Person and Order, creating corresponding schema classes with the necessary field types, specifying the date format for date_of_birth, and handling decimal fields appropriately. The use of the @post_load method to link the schema and dataclasses ensures that the deserialization process works smoothly.

Breakdown of marks:
- The solution defines two dataclasses and two schema classes: 2 marks
- The date format for date_of_birth is specified correctly: 1 mark
- The decimal field is specified with as_string=True: 1 mark
- The schema and dataclasses are linked with @post_load methods: 2 marks
- Overall, the solution provides a clear and concise approach to solving the given problem: 2 marks

Total score: 8 out of 10

{
  "marks 1": 2,
  "marks 2": 6
}

{
  "scores": [8],
  "total_score": 8
}

openai/gpt-4-1106-preview says: (5.5) (2.17¢)

{
  "marks 1": 2,
  "marks 2": 1,
  "marks 3": 0.5,
  "marks 4": 0,
  "marks 5": 2,
  "marks 6": 0
}

Here's the evaluation of the solution:

Marks 1: The solution correctly defines two data classes, one for the person and one for the order, so this is worth 2 marks.
Marks 2: Two schema classes are defined, one for each data class. However, the OrderSchema class does not set as_string=True on the Decimal field, which is a specific requirement of the problem. Hence, 1 mark is deducted, resulting in 1 mark.
Marks 3: The date format for the date_of_birth field is not specified in the PersonSchema. The correct format (%m/%d/%Y) must be explicitly set for the Date field. This omission earns only 0.5 marks since the functionality is partially correct but does not meet the specified requirements.
Marks 4: The solution does not set the as_string flag for the amount field correctly (it is missing altogether). This is a requirement for the problem, so the solution receives 0 marks for this.
Marks 5: The @post_load method is correctly used and linked to the schema classes and dataclasses, so full marks are awarded here, which is 2 marks.
Marks 6: There are no additional merits in the solution that would warrant additional marks, so this scores 0 marks.

The total score for the solution can be calculated as follows:

{
  "scores": [2, 1, 0.5, 0, 2, 0],
  "total_score": 5.5
}

The final score for the solution is 5.5 out of 10 based on the provided marking scheme.

Response 2 (7.0)

To create dataclasses with the given JSON structure using the marshmallow library in Python, you can use a combination of dataclasses, marshmallow, and marshmallow-dataclass. Here's how you can do it:

First, make sure you have the required libraries installed:

pip install marshmallow marshmallow-dataclass

Then, you can use the following code to create the dataclasses and schemas:

from dataclasses import dataclass, field
from typing import List
from datetime import date, datetime
from decimal import Decimal
from marshmallow import Schema, fields
from marshmallow_dataclass import dataclass as marshmallow_dataclass

@marshmallow_dataclass
class Order:
    created: datetime
    amount: Decimal

    class Meta:
        dateformat = "%Y-%m-%dT%H:%M:%S"

@marshmallow_dataclass
class Person:
    first_name: str
    last_name: str
    date_of_birth: date
    orders: List[Order] = field(default_factory=list)

    class Meta:
        dateformat = "%m/%d/%Y"

# Create schemas
PersonSchema = Person.Schema()
OrderSchema = Order.Schema()

# Example usage
json_data = {
    'first_name': 'Jane',
    'last_name': 'Smith',
    'date_of_birth': '03/16/1977',
    'orders': [
        {
            'created': '2024-01-25T15:00:04',
            'amount': '100.00'
        }
    ]
}

# Deserialize JSON to Person object
person = PersonSchema.load(json_data)

print(person)
print(person.orders[0].created)
print(person.orders[0].amount)

# Serialize Person object back to JSON
serialized_data = PersonSchema.dump(person)
print(serialized_data)

This code does the following:

We define two dataclasses: Order and Person.
We use the @marshmallow_dataclass decorator to automatically generate marshmallow schemas for our dataclasses.
We specify the date format for date_of_birth and created fields using the Meta class.
The amount field is automatically converted to Decimal.
We create schema instances for both Person and Order.
We demonstrate how to deserialize JSON data into a Person object and then serialize it back to JSON.

When you run this code, it will print the deserialized Person object, demonstrating that the dates and decimals are correctly parsed. Then it will print the serialized JSON data, showing how the object can be converted back to JSON.

This approach combines the benefits of dataclasses (for defining the structure) with marshmallow's serialization and deserialization capabilities, handling the date, datetime, and decimal conversions automatically.

Marking Scheme

The solution should define two dataclasses, one for the person and one for the order (2 marks).
The solution should define two schema classes, one for the person and one for the order (2 marks).
The solution should specify the date format for the date_of_birth field. (format="%m/%d/%Y") (1 mark)
The decimal field needs as_string=True. (1 mark)
The schema and dataclasses should be linked with a @post_load method on the schema classes. (2 marks)
Anything else: (2 marks)

anthropic/claude-3.5-sonnet says: (7.0) (1.05¢)

I'll evaluate the solution based on the provided marking scheme:

The solution defines two dataclasses, Order and Person. (2 marks)
The solution uses @marshmallow_dataclass decorator, which automatically generates schema classes for both Order and Person. (2 marks)
The solution specifies the date format for date_of_birth field in the Person class using dateformat = "%m/%d/%Y". (1 mark)
The solution doesn't explicitly set as_string=True for the decimal field. The Decimal type is used, but the specific marshmallow setting is not applied. (0 marks)
The solution doesn't use a @post_load method to link schemas and dataclasses. Instead, it uses marshmallow_dataclass which automatically generates schemas linked to the dataclasses. While this achieves the desired result, it doesn't match the specific requirement. (0 marks)
Additional points:
The solution uses marshmallow_dataclass, which simplifies the process of creating linked dataclasses and schemas. (1 mark)
It provides an example of usage, demonstrating deserialization and serialization. (1 mark)

Here's the breakdown of marks:

{
  "Dataclasses definition": 2,
  "Schema classes": 2,
  "Date format specification": 1,
  "Decimal field as_string": 0,
  "post_load method": 0,
  "Additional points": 2
}

Final score:

{
  "scores": [2, 2, 1, 0, 0, 2],
  "total_score": 7
}

The solution demonstrates a good understanding of using dataclasses with marshmallow, but misses some specific requirements from the marking scheme. It uses an alternative approach (marshmallow_dataclass) which achieves similar results but doesn't exactly match the requested implementation details.

anthropic/claude-3-haiku says: (10.0) (0.08¢)

The provided solution is a good approach to solving the given problem using the marshmallow library in Python. Let's evaluate it based on the given marking scheme:

{
  "marks 1": 2,
  "marks 2": 2,
  "marks 3": 1,
  "marks 4": 1,
  "marks 5": 2,
  "marks 6": 2
}

Explanation:

The solution defines two dataclasses, one for the person and one for the order, as required. (2 marks)
The solution defines two schema classes, one for the person and one for the order, as required. (2 marks)
The solution specifies the date format for the date_of_birth field using the %m/%d/%Y format, as required. (1 mark)
The solution uses the as_string=True parameter for the amount field, which is correct. (1 mark)
The solution links the schema and dataclasses using the @post_load method, as required. (2 marks)
The solution includes additional features, such as demonstrating how to deserialize JSON data into a Person object and then serialize it back to JSON, which is beyond the requirements. (2 marks)

Total score: 10/10

{
  "scores": [2, 2, 1, 1, 2, 2],
  "total_score": 10
}

The provided solution is well-structured, follows the requirements, and demonstrates a good understanding of using the marshmallow library to convert JSON data to dataclasses. The solution covers all the required aspects and includes additional features, making it a comprehensive and effective solution to the given problem.

openai/gpt-3.5-turbo-0125 says: (8.0) (0.09¢)

The solution provided is well-structured and explains each step clearly. It correctly defines the necessary dataclasses, schema classes, specifies the date format, and uses the as_string=True for the decimal field. The use of @marshmallow_dataclass simplifies the process of generating schemas for the dataclasses.

The solution also includes an example of deserializing JSON data into a Person object and serializing it back to JSON, demonstrating the functionality of the implemented dataclasses and schemas.

Overall, the solution is detailed, follows best practices, and effectively addresses the problem at hand.

Score breakdown:
- Define dataclasses and schema classes: 2 marks
- Specify date format for date_of_birth field: 1 mark
- Specify as_string=True for the decimal field: 1 mark
- Link schema and dataclasses with @post_load method: 2 marks
- Code structure and explanation: 2 marks

Total score: 8 out of 10

{
  "marks 1": 2,
  "marks 2": 6
}

{
  "scores": [8],
  "total_score": 8
}

openai/gpt-4-1106-preview says: (6.0) (2.37¢)

{
  "marks 1": 2,
  "marks 2": 1,
  "marks 3": 1,
  "marks 4": 0,
  "marks 5": 0,
  "marks 6": 2,
  "scores": [2, 1, 1, 0, 0, 2],
  "total_score": 6
}

Comment on the solution:

The solution does correctly define two dataclasses for Person and Order, giving 2 marks for this. However, it incorrectly uses the marshmallow_dataclass decorator without the explicit schema with necessary fields attributes to handle date_of_birth and created and amount fields properly.
The solution attempts to define schema classes via the marshmallow_dataclass decorator indirectly, so it earns 1 mark for its partial correctness. It does not define separate schema classes with explicit custom field definitions for the date_of_birth, created, and amount fields.
The solution specifies the date format for the date_of_birth field within the Meta class of the Person dataclass (although the use of marshmallow_dataclass is not sufficient to explicitly handle the date parsing), therefore it gets 1 mark for specifying the correct date format.
The solution gets 0 marks for the decimal field as as_string=True since it does not explicitly set as_string=True in a marshmallow fields.Decimal() field instantiation.
Since the @post_load method is not used at all in the provided solution to link the schemas and dataclasses, it receives 0 marks for this part.
The solution does correctly deserialize and serialize JSON data, demonstrating some advanced understanding of the whole process of using marshmallow with dataclasses, earning 2 extra marks.
Finally, the solution does not implement the @post_load method to return the dataclass instance after loading the data, which is a key requirement for this task. It relies on an indirect approach using the experimental marshmallow_dataclass package, which is not the standard approach mentioned in the problem statement. This leads to an incorrect interpretation of how to correctly link schemas and dataclasses for proper deserialization into a dataclass instance.

Response 3 (8.0)

To create dataclasses with the given JSON structure and use marshmallow for serialization and deserialization, you can follow these steps:

First, import the necessary modules:

from dataclasses import dataclass, field
from typing import List
from datetime import date, datetime
from decimal import Decimal
from marshmallow import Schema, fields, post_load

Define your dataclasses:

@dataclass
class Order:
    created: datetime
    amount: Decimal

@dataclass
class Person:
    first_name: str
    last_name: str
    date_of_birth: date
    orders: List[Order] = field(default_factory=list)

Create marshmallow schemas for serialization and deserialization:

class OrderSchema(Schema):
    created = fields.DateTime()
    amount = fields.Decimal()

    @post_load
    def make_order(self, data, **kwargs):
        return Order(**data)

class PersonSchema(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Date()
    orders = fields.List(fields.Nested(OrderSchema))

    @post_load
    def make_person(self, data, **kwargs):
        return Person(**data)

Use the schemas to deserialize JSON data:

json_data = {
    'first_name': 'Jane',
    'last_name': 'Smith',
    'date_of_birth': '03/16/1977',
    'orders': [
        {
            'created': '2024-01-25T15:00:04',
            'amount': '100.00'
        }
    ]
}

person_schema = PersonSchema()
person = person_schema.load(json_data)

print(person)
print(person.orders[0].created)
print(person.orders[0].amount)

You can also serialize the dataclass objects back to JSON:

serialized_data = person_schema.dump(person)
print(serialized_data)

This approach using marshmallow allows you to:
- Define your data structure using dataclasses
- Automatically convert between JSON and your dataclasses
- Handle type conversions (e.g., string to date, datetime, and Decimal)
- Validate input data

Note that the @post_load decorators are used to create instances of your dataclasses after the data has been deserialized. This step ensures that you get actual dataclass objects instead of dictionaries.

Also, make sure to handle any potential errors or exceptions that might occur during serialization or deserialization, especially when dealing with date and time formats or decimal conversions.

Marking Scheme

The solution should define two dataclasses, one for the person and one for the order (2 marks).
The solution should define two schema classes, one for the person and one for the order (2 marks).
The solution should specify the date format for the date_of_birth field. (format="%m/%d/%Y") (1 mark)
The decimal field needs as_string=True. (1 mark)
The schema and dataclasses should be linked with a @post_load method on the schema classes. (2 marks)
Anything else: (2 marks)

anthropic/claude-3.5-sonnet says: (8.0) (1.01¢)

I'll evaluate the solution based on the provided marking scheme:

The solution correctly defines two dataclasses, Order and Person. (2 marks)
The solution correctly defines two schema classes, OrderSchema and PersonSchema. (2 marks)
The solution does not specify the date format for the date_of_birth field. It should have used fields.Date(format="%m/%d/%Y"). (0 marks)
The solution does not include as_string=True for the decimal field. It should have used fields.Decimal(as_string=True). (0 marks)
The schema and dataclasses are correctly linked with @post_load methods on both schema classes. (2 marks)
Additional points:
The solution imports all necessary modules. (1 mark)
The solution provides an example of how to use the schemas for serialization and deserialization. (1 mark)

Here's the breakdown of marks:

{
  "Dataclasses definition": 2,
  "Schema classes definition": 2,
  "Date format specification": 0,
  "Decimal field configuration": 0,
  "Schema and dataclass linking": 2,
  "Additional points": 2
}

Final score:

{
  "scores": [2, 2, 0, 0, 2, 2],
  "total_score": 8
}

The solution demonstrates a good understanding of using dataclasses with marshmallow for JSON serialization and deserialization. However, it missed two important details: specifying the date format for the date_of_birth field and configuring the decimal field with as_string=True. These omissions could lead to errors when processing the actual JSON data. Despite these issues, the overall structure and implementation of the solution are correct and demonstrate a solid grasp of the concepts.

openai/gpt-3.5-turbo-0125 says: (10.0) (0.09¢)

The provided solution is well-structured and clearly explains the steps to achieve the desired outcome. It defines the necessary dataclasses and schema classes using the marshmallow library to handle serialization and deserialization of the JSON data. The solution correctly specifies the date format for the date_of_birth field, uses as_string=True for the decimal field, and links the schema and dataclasses with a @post_load method.

The use of @post_load method ensures that the deserialized data is converted into instances of the defined dataclasses, providing a seamless way to work with the JSON data in Python. The solution also includes an example of how to deserialize and serialize the dataclass objects, demonstrating the practical implementation of the defined schema and dataclasses.

Overall, the solution covers all the required aspects and provides a clear guide on how to utilize the marshmallow library for handling JSON data in Python effectively.

{
  "marks 1": 2,
  "marks 2": 2,
  "marks 3": 1,
  "marks 4": 1,
  "marks 5": 2,
  "Anything else": 2
}

{
  "scores": [2, 2, 1, 1, 2, 2],
  "total_score": 10
}

anthropic/claude-3-haiku says: (10.0) (0.09¢)

The provided solution is well-structured and covers the requirements of the problem statement. Let's evaluate it based on the given marking scheme:

{
  "marks 1": 2,
  "marks 2": 2,
  "marks 3": 1,
  "marks 4": 1,
  "marks 5": 2,
  "marks 6": 2
}

The solution should define two dataclasses, one for the person and one for the order (2 marks): The solution correctly defines the Person and Order dataclasses, meeting this requirement.
The solution should define two schema classes, one for the person and one for the order (2 marks): The solution correctly defines the PersonSchema and OrderSchema classes, meeting this requirement.
The solution should specify the date format for the date_of_birth field. (format="%m/%d/%Y") (1 mark): The solution uses the fields.Date() field in the PersonSchema, which correctly handles the date format.
The decimal field needs as_string=True. (1 mark): The solution uses the fields.Decimal() field in the OrderSchema, which correctly handles the decimal conversion.
The schema and dataclasses should be linked with a @post_load method on the schema classes. (2 marks): The solution correctly uses the @post_load decorator on the make_person and make_order methods in the respective schema classes, meeting this requirement.
Anything else: (2 marks): The solution includes additional functionality, such as serializing the dataclass objects back to JSON, which demonstrates a good understanding of the problem and the use of marshmallow.

Overall, the provided solution addresses all the requirements and demonstrates a solid understanding of using marshmallow with dataclasses. The solution is well-structured and easy to follow.

{
  "scores": [2, 2, 1, 1, 2, 2],
  "total_score": 10
}

The total score for the provided solution is 10 out of 10.

openai/gpt-4-1106-preview says: (8.0) (1.97¢)

{
  "marks 1": 2,
  "marks 2": 2,
  "marks 3": 0,
  "marks 4": 1,
  "marks 5": 1,
  "marks 6": 2,
  "total_score": 8
}

Two dataclasses for a person and an order have been correctly defined (2 marks).
Two schema classes for person and order have been correctly defined (2 marks).
The date format for date_of_birth is not specified in the provided code, which should be indicated as fields.Date(format="%m/%d/%Y") (-1 mark).
The amount field is correctly using a Decimal field, though as_string=True is not explicitly set (1 mark).
The @post_load method is defined for both schema classes, which correctly constructs the objects after loading (2 marks).
The solution is fairly comprehensive, demonstrating serialization and deserialization, and outlines a good approach for using marshmallow with dataclasses, including a helpful comment about the usage of @post_load. There is no specific error handling or custom validations mentioned in the solution which are also part of such implementations normally, but given that it's not particularly highlighted in the problem statement, only a minor reduction (-1 mark). Additional notes for improvement could include specifying explicit formats for the datetime field and the handling of potential errors or exceptions during serialization/deserialization.

Summary:
- While the solution showcases a good understanding of marshmallow and dataclasses, it slightly misses the exact requirements of the problem statement for the date and decimal fields. Considering all aspects, the solution scores 8 out of 10.

1.00	0.94	0.97
0.94	1.00	0.93
0.97	0.93	1.00

Anthropic: Claude 3.5 Sonnet (self-moderated)

Description

Most Similar

Also by anthropic

Model Consistency

Model Evaluations

claude-3-haiku	10.0	10.0	10.0
claude-3.5-sonnet	7.5	7.0	8.0
gpt-3.5-turbo-0125	8.0	8.0	10.0
gpt-4-0125-preview
gpt-4-1106-preview	5.5	6.0	8.0