Claude 3 Opus vs GPT-4: Comprehensive Comparison for Production Use

Anthropic released Claude 3 with three models: Haiku, Sonnet, and Opus. They claim Opus beats GPT-4. I spent 30 days testing both in production.

Results: Claude 3 Opus wins on reasoning and code quality. GPT-4 wins on speed and cost. Here’s the detailed breakdown.

Model Overview

Claude 3 Opus:

Context: 200K tokens
Cost: $15/1M input, $75/1M output
Strengths: Reasoning, code, analysis
Released: March 2024

GPT-4 Turbo:

Context: 128K tokens
Cost: $10/1M input, $30/1M output
Strengths: Speed, versatility
Released: November 2023

Setup

import anthropic
from openai import OpenAI

# Claude 3
claude_client = anthropic.Anthropic(api_key="your-claude-key")

def claude_completion(prompt, model="claude-3-opus-20240229"):
    message = claude_client.messages.create(
        model=model,
        max_tokens=4096,
        messages=[
            {"role": "user", "content": prompt}
        ]
    )
    return message.content[0].text

# GPT-4
openai_client = OpenAI(api_key="your-openai-key")

def gpt4_completion(prompt, model="gpt-4-turbo"):
    response = openai_client.chat.completions.create(
        model=model,
        messages=[
            {"role": "user", "content": prompt}
        ]
    )
    return response.choices[0].message.content

Code Generation Test

Task: Generate a REST API with authentication

prompt = """
Create a Python Flask REST API with:
1. User registration and login
2. JWT authentication
3. Protected endpoints
4. Input validation
5. Error handling
6. SQLAlchemy models
Include complete, production-ready code.
"""

claude_code = claude_completion(prompt)
gpt4_code = gpt4_completion(prompt)

Results:

Metric	Claude 3 Opus	GPT-4 Turbo	Winner
Code quality	9/10	8/10	Claude
Completeness	10/10	9/10	Claude
Best practices	10/10	8/10	Claude
Documentation	9/10	7/10	Claude
Time to generate	45s	28s	GPT-4

Claude generated more complete code with better error handling and security practices.

Reasoning Test

Complex logic problem:

prompt = """
You have 3 boxes. One contains only apples, one contains only oranges, 
and one contains both. All boxes are labeled incorrectly. You can pick 
one fruit from one box. How do you correctly label all boxes?

Explain your reasoning step by step.
"""

claude_answer = claude_completion(prompt)
gpt4_answer = gpt4_completion(prompt)

Claude 3 Opus:

Correct answer: Yes
Reasoning clarity: Excellent
Step-by-step breakdown: Very detailed
Explanation quality: 10/10

GPT-4 Turbo:

Correct answer: Yes
Reasoning clarity: Good
Step-by-step breakdown: Adequate
Explanation quality: 8/10

Claude provided more thorough reasoning with better explanations.

Long Context Handling

Test with 150K token document:

# Load large document
with open('large_document.txt', 'r') as f:
    document = f.read()  # ~150K tokens

prompt = f"""
Document:
{document}

Questions:
1. Summarize the main arguments
2. Find contradictions
3. Extract key statistics
4. Identify missing information
"""

# Claude 3 Opus (200K context)
claude_analysis = claude_completion(prompt, model="claude-3-opus-20240229")

# GPT-4 Turbo (128K context - need to truncate)
truncated_doc = document[:100000]  # Approximate token limit
gpt4_analysis = gpt4_completion(f"Document:\n{truncated_doc}\n\n{prompt}")

Results:

Claude: Analyzed full document, found all contradictions
GPT-4: Missed information in truncated portion

Winner: Claude (larger context window)

Cost Comparison

Real production workload (1 month):

# Calculate costs
def calculate_cost(input_tokens, output_tokens, model):
    if model == "claude-3-opus":
        return (input_tokens / 1_000_000 * 15) + (output_tokens / 1_000_000 * 75)
    elif model == "gpt-4-turbo":
        return (input_tokens / 1_000_000 * 10) + (output_tokens / 1_000_000 * 30)

# Our usage (monthly)
input_tokens = 50_000_000  # 50M
output_tokens = 10_000_000  # 10M

claude_cost = calculate_cost(input_tokens, output_tokens, "claude-3-opus")
gpt4_cost = calculate_cost(input_tokens, output_tokens, "gpt-4-turbo")

print(f"Claude 3 Opus: ${claude_cost:,.2f}")
print(f"GPT-4 Turbo: ${gpt4_cost:,.2f}")

Results:

Claude 3 Opus: $1,500/month
GPT-4 Turbo: $800/month

Winner: GPT-4 (47% cheaper)

Speed Comparison

Benchmark: 1000-word article generation

import time

def benchmark_speed(model_func, prompt, iterations=20):
    times = []
    for _ in range(iterations):
        start = time.time()
        model_func(prompt)
        times.append(time.time() - start)
    return sum(times) / len(times)

prompt = "Write a 1000-word article about climate change solutions."

claude_time = benchmark_speed(
    lambda p: claude_completion(p, "claude-3-opus-20240229"),
    prompt
)

gpt4_time = benchmark_speed(
    lambda p: gpt4_completion(p, "gpt-4-turbo"),
    prompt
)

print(f"Claude 3 Opus: {claude_time:.2f}s")
print(f"GPT-4 Turbo: {gpt4_time:.2f}s")

Results:

Claude 3 Opus: 42.3s
GPT-4 Turbo: 28.7s

Winner: GPT-4 (32% faster)

Real-World Use Cases

1. Code Review:

def review_code_with_both(code):
    prompt = f"""
    Review this code for:
    1. Security vulnerabilities
    2. Performance issues
    3. Best practices violations
    4. Potential bugs
    
    Code:
    ```python
    {code}
    ```
    """
    
    claude_review = claude_completion(prompt)
    gpt4_review = gpt4_completion(prompt)
    
    return {
        "claude": claude_review,
        "gpt4": gpt4_review
    }

# Test
code = """
def process_payment(amount, card_number):
    # Process payment
    db.execute(f"INSERT INTO payments VALUES ({amount}, '{card_number}')")
    return True
"""

reviews = review_code_with_both(code)

Claude found:

SQL injection vulnerability
Missing input validation
No error handling
Storing card numbers (PCI compliance issue)
Missing transaction management

GPT-4 found:

SQL injection vulnerability
Missing error handling
No input validation

Winner: Claude (more thorough)

2. Technical Documentation:

def generate_docs(code):
    prompt = f"""
    Generate comprehensive API documentation for this code:
    
    {code}
    
    Include:
    - Function descriptions
    - Parameters
    - Return values
    - Examples
    - Error cases
    """
    
    return {
        "claude": claude_completion(prompt),
        "gpt4": gpt4_completion(prompt)
    }

Results:

Claude: More detailed, better examples
GPT-4: Faster, good enough for most cases

Claude 3 Sonnet Alternative

For cost-sensitive workloads:

def claude_sonnet_completion(prompt):
    message = claude_client.messages.create(
        model="claude-3-sonnet-20240229",
        max_tokens=4096,
        messages=[{"role": "user", "content": prompt}]
    )
    return message.content[0].text

# Cost: $3/1M input, $15/1M output (5x cheaper than Opus)

Claude 3 Sonnet vs GPT-4 Turbo:

Speed: Similar
Cost: Sonnet 60% cheaper
Quality: Sonnet slightly better
Context: Sonnet 200K vs GPT-4 128K

Production Recommendations

Use Claude 3 Opus for:

Complex reasoning tasks
Code generation and review
Long document analysis (>128K tokens)
High-stakes decisions
When quality > cost

Use GPT-4 Turbo for:

High-volume workloads
Real-time applications
Cost-sensitive projects
General-purpose tasks
When speed > quality

Use Claude 3 Sonnet for:

Best balance of cost/quality
Most production workloads
Long context needs
Better than GPT-4 Turbo at lower cost

Hybrid Approach

Use both strategically:

def smart_completion(prompt, task_type):
    if task_type == "code_review":
        return claude_completion(prompt, "claude-3-opus-20240229")
    elif task_type == "chat":
        return gpt4_completion(prompt, "gpt-4-turbo")
    elif task_type == "analysis":
        return claude_completion(prompt, "claude-3-sonnet-20240229")
    else:
        return gpt4_completion(prompt, "gpt-4-turbo")

# Route based on task
code_review = smart_completion(code, "code_review")  # Uses Claude Opus
chat_response = smart_completion(message, "chat")  # Uses GPT-4

Results Summary

Category	Claude 3 Opus	GPT-4 Turbo	Winner
Code Quality	9/10	8/10	Claude
Reasoning	10/10	8/10	Claude
Speed	7/10	9/10	GPT-4
Cost	6/10	8/10	GPT-4
Context Window	200K	128K	Claude
Documentation	9/10	7/10	Claude
Versatility	8/10	9/10	GPT-4

Overall: Depends on use case

Our Production Setup

class AIRouter:
    def __init__(self):
        self.claude = anthropic.Anthropic(api_key=CLAUDE_KEY)
        self.openai = OpenAI(api_key=OPENAI_KEY)
    
    def complete(self, prompt, priority="balanced"):
        if priority == "quality":
            # Use Claude 3 Opus for best quality
            return self.claude_opus(prompt)
        elif priority == "speed":
            # Use GPT-4 Turbo for speed
            return self.gpt4_turbo(prompt)
        else:
            # Use Claude 3 Sonnet for balance
            return self.claude_sonnet(prompt)
    
    def claude_opus(self, prompt):
        msg = self.claude.messages.create(
            model="claude-3-opus-20240229",
            max_tokens=4096,
            messages=[{"role": "user", "content": prompt}]
        )
        return msg.content[0].text
    
    def claude_sonnet(self, prompt):
        msg = self.claude.messages.create(
            model="claude-3-sonnet-20240229",
            max_tokens=4096,
            messages=[{"role": "user", "content": prompt}]
        )
        return msg.content[0].text
    
    def gpt4_turbo(self, prompt):
        response = self.openai.chat.completions.create(
            model="gpt-4-turbo",
            messages=[{"role": "user", "content": prompt}]
        )
        return response.choices[0].message.content

# Usage
router = AIRouter()

# High-quality code review
review = router.complete(code_prompt, priority="quality")

# Fast customer support
response = router.complete(support_query, priority="speed")

# Balanced general use
result = router.complete(general_prompt, priority="balanced")

Lessons Learned

No clear winner - Depends on use case
Claude excels at reasoning - Better for complex tasks
GPT-4 wins on speed/cost - Better for high volume
Claude 3 Sonnet is sweet spot - Best balance
Use hybrid approach - Route intelligently

Conclusion

Both models are excellent. Choose based on your priorities:

Quality-first: Claude 3 Opus
Cost-first: GPT-4 Turbo
Balanced: Claude 3 Sonnet

Key takeaways:

Claude 3 Opus: Best reasoning and code quality
GPT-4 Turbo: Faster and cheaper
Claude 3 Sonnet: Best overall value
Use hybrid routing for optimal results
Test both with your specific workload

Don’t pick one. Use both strategically.

Table of Contents

Model Overview

Setup

Code Generation Test

Reasoning Test

Long Context Handling

Cost Comparison

Speed Comparison

Real-World Use Cases

Claude 3 Sonnet Alternative

Production Recommendations

Hybrid Approach

Results Summary

Our Production Setup

Lessons Learned

Conclusion