September 4, 2024 4 min read

Choosing the Right Model: GPT-4o vs Claude 3.5 Sonnet for Different Tasks

With multiple frontier models available, choosing the right one for your task matters. Let’s break down when to use GPT-4o, Claude 3.5 Sonnet, and other options.

Current Model Landscape

from dataclasses import dataclass

@dataclass
class ModelCapabilities:
    model: str
    provider: str
    context_window: int
    vision: bool
    streaming: bool
    function_calling: bool
    cost_per_1m_input: float
    cost_per_1m_output: float

gpt_4o = ModelCapabilities(
    model="gpt-4o",
    provider="OpenAI",
    context_window=128000,
    vision=True,
    streaming=True,
    function_calling=True,
    cost_per_1m_input=2.50,
    cost_per_1m_output=10.00
)

claude_35_sonnet = ModelCapabilities(
    model="claude-3-5-sonnet-20240620",
    provider="Anthropic",
    context_window=200000,
    vision=True,
    streaming=True,
    function_calling=True,
    cost_per_1m_input=3.00,
    cost_per_1m_output=15.00
)

gpt_4o_mini = ModelCapabilities(
    model="gpt-4o-mini",
    provider="OpenAI",
    context_window=128000,
    vision=True,
    streaming=True,
    function_calling=True,
    cost_per_1m_input=0.15,
    cost_per_1m_output=0.60
)

Performance Benchmarks

Based on published benchmarks:

Task	GPT-4o	Claude 3.5 Sonnet
MMLU	88.7%	88.7%
HumanEval	90.2%	92.0%
MATH	76.6%	71.1%
Graduate Reasoning	~65%	59.4%

Practical Comparison

from openai import OpenAI
from anthropic import Anthropic
import time

openai_client = OpenAI()
anthropic_client = Anthropic()

def compare_models(test_cases: list) -> dict:
    """Compare model performance across different task types"""

    results = []

    for test in test_cases:
        # Test GPT-4o
        start = time.time()
        gpt4_response = openai_client.chat.completions.create(
            model="gpt-4o",
            messages=[{"role": "user", "content": test["prompt"]}]
        )
        gpt4_time = time.time() - start

        # Test Claude 3.5 Sonnet
        start = time.time()
        claude_response = anthropic_client.messages.create(
            model="claude-3-5-sonnet-20240620",
            max_tokens=4096,
            messages=[{"role": "user", "content": test["prompt"]}]
        )
        claude_time = time.time() - start

        results.append({
            "task": test["name"],
            "gpt4o_time": gpt4_time,
            "claude_time": claude_time,
            "gpt4o_tokens": gpt4_response.usage.total_tokens,
        })

    return results

# Example test cases
test_cases = [
    {
        "name": "Simple math",
        "prompt": "What is 15% of 240?"
    },
    {
        "name": "Complex reasoning",
        "prompt": "A snail climbs 3 feet during the day but slides back 2 feet at night. How many days to climb a 10-foot wall?"
    },
    {
        "name": "Code generation",
        "prompt": "Write a function to find the longest palindromic substring in a string."
    }
]

Cost Analysis

def calculate_cost(model: str, input_tokens: int, output_tokens: int) -> float:
    """Calculate the cost of an API call"""

    pricing = {
        "gpt-4o": {"input": 2.50, "output": 10.00},
        "gpt-4o-mini": {"input": 0.15, "output": 0.60},
        "claude-3-5-sonnet": {"input": 3.00, "output": 15.00},
        "claude-3-haiku": {"input": 0.25, "output": 1.25}
    }

    p = pricing.get(model, pricing["gpt-4o"])
    return (input_tokens * p["input"] + output_tokens * p["output"]) / 1_000_000

# Example comparison for 1000 input, 500 output tokens
for model in ["gpt-4o", "gpt-4o-mini", "claude-3-5-sonnet"]:
    cost = calculate_cost(model, 1000, 500)
    print(f"{model}: ${cost:.4f}")

Decision Framework

def select_model(
    task_type: str,
    requires_long_context: bool = False,
    requires_function_calling: bool = False,
    budget_constrained: bool = False,
    provider_preference: str = None
) -> str:
    """
    Select the appropriate model based on requirements
    """

    # Long context needs
    if requires_long_context:
        return "claude-3-5-sonnet"  # 200K context

    # Budget constraints
    if budget_constrained:
        return "gpt-4o-mini"

    # Provider preference
    if provider_preference == "anthropic":
        return "claude-3-5-sonnet"

    # Task-based selection
    if task_type == "coding":
        return "claude-3-5-sonnet"  # Slightly better on HumanEval

    if task_type == "math":
        return "gpt-4o"  # Better on MATH benchmark

    # Default
    return "gpt-4o"

Practical Examples

Use GPT-4o For:

# Chatbots with function calling
response = openai_client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What's the weather in Seattle?"}
    ],
    tools=[weather_function],
    tool_choice="auto",
    stream=True
)

# Vision + reasoning
response = openai_client.chat.completions.create(
    model="gpt-4o",
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "Analyze this chart"},
            {"type": "image_url", "image_url": {"url": image_url}}
        ]
    }]
)

Use Claude 3.5 Sonnet For:

# Long document analysis
response = anthropic_client.messages.create(
    model="claude-3-5-sonnet-20240620",
    max_tokens=4096,
    messages=[{
        "role": "user",
        "content": f"Analyze this 150-page document:\n{long_document}"
    }]
)

# Code generation
response = anthropic_client.messages.create(
    model="claude-3-5-sonnet-20240620",
    max_tokens=4096,
    messages=[{
        "role": "user",
        "content": "Implement a red-black tree with insert, delete, and search operations"
    }]
)

Looking Ahead

The model landscape continues to evolve. OpenAI has hinted at improved reasoning capabilities in future releases. Build your architecture to be model-agnostic:

class ModelAgnosticClient:
    """Abstraction that can switch models easily"""

    def __init__(self, preferred_provider: str = "openai"):
        self.openai = OpenAI()
        self.anthropic = Anthropic()
        self.preferred = preferred_provider

    def chat(self, prompt: str, **kwargs) -> str:
        if self.preferred == "openai":
            response = self.openai.chat.completions.create(
                model=kwargs.get("model", "gpt-4o"),
                messages=[{"role": "user", "content": prompt}]
            )
            return response.choices[0].message.content
        else:
            response = self.anthropic.messages.create(
                model=kwargs.get("model", "claude-3-5-sonnet-20240620"),
                max_tokens=4096,
                messages=[{"role": "user", "content": prompt}]
            )
            return response.content[0].text

Conclusion

Both GPT-4o and Claude 3.5 Sonnet are excellent choices. Select based on:

Context length: Claude for very long documents
Code generation: Claude has a slight edge
Math/reasoning: GPT-4o performs well
Cost: GPT-4o-mini for budget-conscious applications
Ecosystem: GPT-4o for Azure integration

The best strategy is often multi-provider, using each model for its strengths.