Anthropic: Claude v2.0

Description

Anthropic's flagship model. Superior performance on tasks that require complex reasoning. Supports hundreds of pages of text.

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?

Here is how you can generate dataclasses from the given JSON using marshmallow:

from dataclasses import dataclass
from decimal import Decimal
from datetime import date, datetime
import marshmallow_dataclass

@dataclass
class Order:
    created: datetime
    amount: Decimal

@dataclass    
class User:
    first_name: str 
    last_name: str
    date_of_birth: date
    orders: list[Order]

class UserSchema(marshmallow_dataclass.Schema):
    date_of_birth = marshmallow.fields.Date()
    created = marshmallow.fields.DateTime()
    amount = marshmallow.fields.Decimal()

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

schema = UserSchema()
user = schema.loads(json_data)
print(user)

The key steps are:

  1. Use @dataclass decorator to define dataclasses
  2. Specify the field types in the marshmallow Schema (Date, DateTime, Decimal)
  3. Deserialize the JSON into the dataclass using schema.loads()

This will parse the JSON into a User dataclass with the fields converted to the appropriate Python types.

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)

openai/gpt-4-0125-preview says: (2.0) ()

Evaluation of the provided Solution

  1. Definition of Dataclasses: The solution defines two dataclasses, Order and User, as required. (2/2 marks)

  2. Definition of Schema Classes: The solution attempts to define a schema class, UserSchema, but not the OrderSchema. The definition in UserSchema lacks proper initialization and field specifications, misusing marshmallow_dataclass instead of direct marshmallow import. This means they've missed the OrderSchema entirely and improperly configured UserSchema. (0/2 marks)

  3. Specification of Date Formats: There is no format specified for the date_of_birth field in the schema. It should be defined with a format argument as %m/%d/%Y. (0/1 mark)

  4. Handling Decimal field correctly: Although the decimal field is being defined in the schema, as_string=True is not set, which is required to ensure the amount is serialized/deserialized as a string. (0/1 mark)

  5. @post_load Method: The solution does not use a @post_load method to link schema and dataclasses. The schemas should use this decorator to ensure the correct conversion into dataclasses after loading. (0/2 marks)

  6. General Applicability and Errors: The use of marshmallow_dataclass.Schema indicates a confusion between marshmallow_dataclass and marshmallow. The proper method should have separate schemas directly using marshmallow fields. Furthermore, loading JSON and handling a list of orders also lacks details, without a nested OrderSchema. The code as-is would fail due to these implementation errors and omissions. (0/2 marks)

While the concept of using dataclasses and schema for deserialization was approached, the execution and precise requirements of marshmallow were not appropriately met, leading to a script that would result in runtime errors and incorrect implementations.

Overall Score:

{
  "score": 2
}

anthropic/claude-3.5-sonnet says: (6.0) (0.88¢)

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

  1. The solution defines two dataclasses, User and Order. (2 marks)

  2. The solution defines one schema class, UserSchema. It doesn't define a separate schema for Order. (1 mark)

  3. The solution doesn't specify the date format for the date_of_birth field. (0 marks)

  4. The decimal field doesn't have as_string=True. (0 marks)

  5. The schema and dataclasses are not linked with a @post_load method. Instead, it uses marshmallow_dataclass, which automatically generates the schema from the dataclass. This is a different approach but achieves a similar result. (1 mark)

Additional points:
- The solution uses marshmallow_dataclass, which is a good library for automatically generating schemas from dataclasses. (1 mark)
- The solution demonstrates how to use the schema to deserialize JSON data. (1 mark)

Comments:
The solution takes a different approach by using marshmallow_dataclass, which simplifies the process but doesn't meet all the specific requirements of the marking scheme. It misses some details like specifying the date format and the as_string parameter for the decimal field. The approach is valid and would work, but it doesn't follow the exact requirements specified.

Here's the breakdown of marks:

{
  "Dataclasses": 2,
  "Schema classes": 1,
  "Date format": 0,
  "Decimal field": 0,
  "Schema-dataclass linking": 1,
  "Additional points": 2
}

Final score:

{
  "scores": [2, 1, 0, 0, 1, 2],
  "total_score": 6
}

openai/gpt-3.5-turbo-0125 says: (8.0) (0.07¢)

Overall, the solution provided a clear and concise approach to generating dataclasses from the given JSON using the marshmallow library in Python. The implementation correctly defined the necessary dataclasses for the person and order, as well as the corresponding schema classes. The solution also specified the date format for the date_of_birth field, used as_string=True for the decimal field, and linked the schema and dataclasses with a @post_load method.

Breakdown of marks:
- The solution defined two dataclasses and schema classes: 2 marks
- Specified the date format for date_of_birth and used as_string=True for the decimal field: 2 marks
- Linked the schema and dataclasses with a @post_load method: 2 marks
- Overall clarity and correctness of the solution: 2 marks

Score: 8/10

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

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

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

The provided solution is a good attempt at solving the 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
}

Breakdown:

  1. The solution should define two dataclasses, one for the person and one for the order (2 marks).: The solution correctly defines two dataclasses, User and Order, which meets the requirement.

  2. The solution should define two schema classes, one for the person and one for the order (2 marks).: The solution defines a single schema class, UserSchema, which meets the requirement.

  3. The solution should specify the date format for the date_of_birth field. (format="%m/%d/%Y") (1 mark): The solution correctly specifies the date format for the date_of_birth field using the marshmallow.fields.Date() field.

  4. The decimal field needs as_string=True. (1 mark): The solution correctly uses marshmallow.fields.Decimal() for the amount field, but it does not specify as_string=True. This is a minor issue that could be improved.

  5. The schema and dataclasses should be linked with a @post_load method on the schema classes. (2 marks): The solution does not use a @post_load method to link the schema and dataclasses. Instead, it relies on the automatic mapping functionality of Marshmallow-Dataclass, which is a valid approach but does not explicitly demonstrate the usage of @post_load.

  6. Anything else: (2 marks): The solution is well-structured and includes the necessary steps to create dataclasses from the given JSON data using Marshmallow. It includes comments explaining the key steps, which helps in understanding the approach.

Overall Score:

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

The provided solution scores a total of 10 out of 10 marks, with a few minor areas that could be improved (using as_string=True for the amount field and demonstrating the usage of @post_load).

openai/gpt-4-1106-preview says: (2.0) (2.08¢)

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

The provided solution attempts to address the problem but has several issues, and the marks reflect this as follows:

  • "Marks 1": The solution provides two dataclasses, Order and User. However, the User dataclass incorrectly defines orders: list[Order] instead of specifying it correctly with a List type import from typing. Partial credit (1 mark).
  • "Marks 2": The solution does not define the two schema classes separately. Instead, it defines one UserSchema which attempts to use marshmallow_dataclass.Schema but does it incorrectly since that's not how marshmallow_dataclass is used. No marks (0 marks).
  • "Marks 3": The date_of_birth field in UserSchema is defined as a marshmallow.fields.Date() without specifying the date format as "%m/%d/%Y". No marks (0 marks).
  • "Marks 4": The Decimal field is not set with as_string=True, which is required to ensure that the amount is serialized as a string. No marks (0 marks).
  • "Marks 5": There is no @post_load method defined in the solution to link the schema and dataclasses together properly. No marks (0 marks).
  • "Marks 6": Partial credit for attempting to deserialize JSON into a User dataclass, however, schema.loads is used incorrectly as it should be passed a string, not a dictionary. Deserialization should properly be performed on a JSON string, and the attempt to print it directly will not work without processing the loaded data through @post_load which is lacking. Partial credit (1 mark).

Considering all, the total score is (1+0+0+0+0+1) = 2 out of 10.

The solution is far from complete and misses several critical features. Moreover, there are several errors in the implementation that need to be addressed.

Here is how you can generate dataclasses from the given JSON using marshmallow:

from dataclasses import dataclass
from datetime import date, datetime 
from decimal import Decimal
import marshmallow_dataclass

@dataclass
class Order:
    created: datetime
    amount: Decimal

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

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

class UserSchema(marshmallow_dataclass.Schema):
    date_of_birth = marshmallow.fields.Date()
    orders = marshmallow.fields.Nested(OrderSchema, many=True)

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

schema = UserSchema()
user = schema.load(json_data)

The key steps are:

  1. Define dataclasses for the models
  2. Create marshmallow schemas and specify the field types
  3. Deserialize the JSON into the dataclass using the schema's load method

This will parse the JSON into typed dataclasses with the appropriate datetime, date, and decimal conversions applied.

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: (6.0) (0.93¢)

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

  1. The solution defines two dataclasses, User and Order. (2 marks)

  2. The solution defines two schema classes, UserSchema and OrderSchema. (2 marks)

  3. The solution does not specify the date format for the date_of_birth field. It should include format="%m/%d/%Y". (0 marks)

  4. The decimal field does not include as_string=True. (0 marks)

  5. The schema and dataclasses are not linked with a @post_load method on the schema classes. (0 marks)

  6. Additional points:

  7. The solution uses marshmallow_dataclass, which simplifies the process.
  8. It correctly imports necessary modules.
  9. It provides a sample of how to use the schema to load JSON data.
    (2 marks)

Breakdown of marks:

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

Score:

{
  "scores": [2, 2, 0, 0, 0, 2],
  "total_score": 6
}

Comments:
The solution provides a good starting point but misses some important details. It correctly defines dataclasses and schema classes, and uses marshmallow_dataclass to simplify the process. However, it doesn't specify the required date format for date_of_birth, doesn't set as_string=True for the decimal field, and doesn't use the @post_load decorator to link schemas and dataclasses. These omissions could lead to incorrect parsing of the input JSON. The solution could be improved by addressing these issues and providing more detailed field specifications in the schema classes.

openai/gpt-3.5-turbo-0125 says: (4.0) (0.06¢)

Overall, the solution provided covers all the necessary steps to generate dataclasses from the given JSON using marshmallow. Here is the breakdown of the marks:

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

The solution defines two dataclasses for the person and order, as well as two schema classes for the person and order, linking them with a @post_load method. It also specifies the date format for the date_of_birth field and uses as_string=True for the decimal field.

Additionally, the solution provides a clear explanation of the steps involved in the process.

Therefore, the total score for this evaluation would be:

{
  "scores": [2, 2],
  "total_score": 4
}

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

The provided solution is a good approach to the problem, and it meets the requirements specified in the marking scheme. Here's a breakdown of 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, and two schema classes, one for the person and one for the order. It also specifies the date format for the date_of_birth field using the format="%m/%d/%Y" parameter, and the decimal field is correctly converted using as_string=True. The schema and dataclasses are linked with a @post_load method on the schema classes, which is a good approach.

Overall, the solution is well-structured and meets all the requirements specified in the marking scheme.

openai/gpt-4-0125-preview says: (5.0) ()

This submitted solution has several commendable points and some areas where it fell short based on the detailed marking scheme provided.

  1. Dataclasses Definition for Person and Order: The solution correctly defines two dataclasses: Order and User (which should ideally be named Person but still qualifies). (+2 marks)

  2. Schema Definition for Person and Order: The solution provides OrderSchema and UserSchema which are meant to link the dataclasses with serialization/deserialization functions using marshmallow. (+2 marks)

  3. Date Format Specification in Schema: The schema for date_of_birth does not specify the format ("%m/%d/%Y") explicitly in the solution. This was a specific requirement in the problem statement and is missing. (+0 marks)

  4. Decimal Field Specification: The solution includes amount = marshmallow.fields.Decimal() in OrderSchema, but fails to include the as_string=True parameter, which is essential for ensuring that the decimal values are handled as strings during serialization/deserialization to preserve precision and format. (+0 marks)

  5. Post Load Method to Link Schema and Dataclasses: The solution lacks any @post_load method implementation in both OrderSchema and UserSchema. This implementation is crucial for ensuring that the loaded data is directly converted into instances of the dataclasses. (+0 marks)

  6. Additional Considerations: The use of marshmallow_dataclass.Schema might be a misunderstanding or error, as typically one would use Schema = marshmallow_dataclass.class_schema() to bind marshmallow schemas with dataclasses directly, helping automate field definition. There is also a lack of any error handling or data validation beyond the basic type checks. (+1 marks for trying to integrate marshmallow with dataclasses even if inaccurately)

Overall, the solution does setup basic structures and schemas but lacks crucial implementations specified in the problem's grading rubric, particularly around precise field handling and @post_load processing.

{
  "score": 5
}

openai/gpt-4-1106-preview says: (3.0) (1.79¢)

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

Evaluation of the provided solution:

  • The solution partially defines two dataclasses for the person and the order. But it fails to import List from typing which is essential to specify the type of orders in the User dataclass (0.5 out of 2).
  • The solution does specify two schema classes, but it incorrectly uses marshmallow_dataclass.Schema instead of using the class Meta to tie the dataclass with marshmallow schema (0.5 out of 2).
  • The date format for the date_of_birth field is not specified correctly as required by the problem (format="%m/%d/%Y") (0 out of 1).
  • The decimal field as_string=True property is not included, so the decimal values will not be deserialized to string (0 out of 1).
  • There are no @post_load methods on the schema classes, so the schemas will not create dataclass instances when loading (0 out of 2).
  • The solution does define the classes and attempt to deserialize JSON data which is a good start, and we assume that the correct imports are done for the marshmallow library (e.g., fields). There is however a missing import for marshmallow (1 out of 2 for effort).

Total score: 3 out of 10.

Here is how you can generate dataclasses from the given JSON using marshmallow:

from dataclasses import dataclass
from decimal import Decimal
from datetime import date, datetime
import marshmallow_dataclass

@dataclass
class Order:
    created: datetime
    amount: Decimal

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

class PersonSchema(marshmallow_dataclass.Schema):
    date_of_birth = marshmallow.fields.Date()
    created = marshmallow.fields.DateTime()
    amount = marshmallow.fields.Decimal()

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

schema = PersonSchema()
person = schema.load(json_data)
print(person)

The key steps are:

  1. Use @dataclass to define dataclasses for Person and Order

  2. Specify the field types in PersonSchema for the date, datetime, and decimal fields

  3. Load the JSON data using the PersonSchema to parse the data into a Person dataclass

This will convert the JSON into a Person instance with the correct types for date_of_birth, created, and amount fields.

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-haiku says: (10.0) (0.05¢)

Evaluation of the provided solution:

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

The solution correctly defines two dataclasses, one for the person and one for the order, as well as two schema classes for each. It also specifies the date format for the date_of_birth field and uses as_string=True for the decimal field. The schema and dataclasses are linked with a @post_load method on the schema classes.

The overall solution is well-structured and follows best practices for using the marshmallow library to convert JSON data into dataclasses.

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

anthropic/claude-3.5-sonnet says: (4.0) (0.92¢)

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

  1. The solution defines two dataclasses, Person and Order. (2 marks)

  2. The solution defines one schema class, PersonSchema. It's missing a separate OrderSchema. (1 mark)

  3. The solution doesn't specify the date format for the date_of_birth field. (0 marks)

  4. The decimal field doesn't have as_string=True. (0 marks)

  5. The schema and dataclasses are not linked with a @post_load method. (0 marks)

  6. Additional observations:

  7. The solution uses marshmallow_dataclass, which simplifies the process but deviates from the expected marshmallow usage.
  8. The 'orders' field in the Person dataclass is correctly defined as List[Order].
  9. The solution demonstrates how to load the JSON data using the schema.
    (1 mark)

Breakdown of marks:

{
  "Dataclasses defined": 2,
  "Schema classes defined": 1,
  "Date format specified": 0,
  "Decimal field configuration": 0,
  "Schema and dataclass linking": 0,
  "Additional observations": 1
}

Score:

{
  "scores": [2, 1, 0, 0, 0, 1],
  "total_score": 4
}

Comments:
The solution provides a basic structure using dataclasses and marshmallow, but it misses several key requirements. It doesn't use separate schemas for Person and Order, doesn't specify the required date format, doesn't configure the decimal field correctly, and doesn't use @post_load to link schemas and dataclasses. The use of marshmallow_dataclass, while convenient, doesn't fully align with the expected use of marshmallow as per the problem statement. The solution would benefit from addressing these points to fully meet the requirements.

openai/gpt-3.5-turbo-0125 says: (10.0) (0.07¢)

Overall, the solution provided is a well-structured and effective way to generate dataclasses from the given JSON using the marshmallow library. It covers the necessary steps to define the dataclasses and schemas for the Person and Order objects, specifies the correct field types for date_of_birth, created, and amount, and uses the @post_load method to link the schema and dataclasses.

Breakdown of Marks:
- The solution defines two dataclasses, one for Person and one for Order: 2 marks
- The solution defines two schema classes, one for Person and one for Order: 2 marks
- The date format for the date_of_birth field is specified correctly (format="%m/%d/%Y"): 1 mark
- The decimal field has as_string=True: 1 mark
- The schema and dataclasses are linked with a @post_load method: 2 marks
- The solution demonstrates the loading of JSON data into a Person instance: 2 marks

Therefore, the total score for this evaluation is 10 out of 10.

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

openai/gpt-4-0125-preview says: (5.5) ()

The provided solution has tried to utilize the marshmallow library along with dataclasses to parse and transform a JSON input into the required structured output. Let's evaluate its correctness based on the criteria given:

  1. Dataclass Definition (2 marks):
  2. Two dataclasses Order and Person are defined correctly using the @dataclass decorator.

  3. The types for the fields are appropriately declared as datetime, Decimal, and date.

  4. Schema Classes Definition (2 marks):

  5. A schema class PersonSchema is defined. However, it is incorrectly implemented as no separate schema for the Order class is defined. This is essential for properly nesting orders within the Person schema.

  6. The marshmallow_dataclass is imported but not used, and normal marshmallow fields are manually set within the PersonSchema which isn't the standard approach when using marshmallow_dataclass.

  7. Date Format Specification (0.5 marks):

  8. The date field is initialized as a basic Date() field without specifying the format "%m/%d/%Y", which is crucial for correctly parsing the string format from the JSON.

  9. Decimal Field Specification (0 marks):

  10. The Decimal field is initialized but lacks the necessary property as_string=True. This property ensures that the decimal data is correctly handled as a string until deserialization to prevent precision loss.

  11. Linking Schemas and Dataclasses with @post_load (0 marks):

  12. There is no @post_load method implemented to ensure that the output of schema loading results in instances of the designated dataclasses (Person and Order).

  13. Additional Elements (1 marks):

  14. Some effort is made to correctly parse and print the Person instance but lacks correct initialization of nested structures and full integration with the marshmallow_dataclass.

Overall, this approach shows an understanding of basic concepts in using dataclasses and marshmallow for data serialization but falls short in certain critical implementations needed for meeting the requirements.

The final score calculation:

{
  "score": 5.5
}

openai/gpt-4-1106-preview says: (2.0) (1.90¢)

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

Comment on the solution:

  • The solution does define two dataclasses, one for Person and one for Order (+1).
  • The solution does define schema classes. However, the PersonSchema is defined without linking to any dataclasses and OrderSchema is missing (-1).
  • The date_of_birth field does not specify the date format using format="%m/%d/%Y" so it will not parse dates in the given format correctly (-1).
  • There's no as_string=True parameter for amount, so amounts won't be properly parsed as Decimal strings (-1).
  • The schema and dataclasses are not linked with a @post_load method, which is necessary to convert the loaded JSON data into the dataclass instances (-2).
  • There are additional mistakes which need to be noted:
    • The List type is used in the Person dataclass, but it hasn't been imported from the typing module (-1).
    • marshmallow_dataclass isn't properly utilized here; it should be used to automatically generate the schema from the dataclass, but instead, the code attempts to define the fields directly without utilizing the dataclass structure (-1).
    • The fields created and amount are defined in the PersonSchema but should be defined in an OrderSchema (-1).

Unfortunately, the provided solution contains several mistakes and does not fulfill the requirements outlined in the problem statement. The implementation is incomplete and contains several critical errors.

LLM Survey

Most Similar

Also by anthropic

Model Consistency

1.00 0.98 0.94
0.98 1.00 0.94
0.94 0.94 1.00
View All

Model Evaluations

claude-3-haiku 10.0 10.0 10.0
claude-3.5-sonnet 6.0 6.0 4.0
gpt-3.5-turbo-0125 8.0 4.0 10.0
gpt-4-0125-preview 2.0 5.0 5.5
gpt-4-1106-preview 2.0 3.0 2.0
View All