OpenAI o3-mini vs Claude 3.5 Sonnet

New LLMs are being released all the time, and it’s exciting to see how they challenge the established players. This year, the focus has been on automating coding tasks, with models like o1, o1-mini, Qwen 2.5, DeepSeek R1, and others working to make coding easier and more efficient. One model that’s made a big name in the coding space is Claude Sonnet 3.5. It’s known for its ability to generate code and web applications, earning plenty of praise along the way. In this article, we’ll compare the coding champion – Claude Sonnet 3.5, with the new OpenAI’s o3-mini (high) model. Let’s see which one comes out on top!

OpenAI o3-mini vs Claude 3.5 Sonnet: Model Comparison

The landscape of AI language models is rapidly evolving, with OpenAI’s o3-mini and Anthropic’s Claude 3.5 Sonnet emerging as prominent players. This article delves into a detailed comparison of these models, examining their architecture, features, performance benchmarks, and practical applications.

Architecture and Design

Both o3-mini and Claude 3.5 Sonnet are built on advanced architectures that enhance their reasoning capabilities.

• o3-mini: Released in January 2024, it emphasizes software engineering and mathematical reasoning tasks, featuring enhanced safety testing protocols.
• Claude 3.5 Sonnet: Launched in October 2024, it boasts improvements in coding proficiency and multimodal capabilities, allowing for a broader range of applications.

Key Features

Performance Benchmarks

Performance benchmarks are crucial for evaluating the effectiveness of AI models across various tasks. Below is a comparison based on key metrics:

User Experience and Interface

The user experience of AI models depends on accessibility, ease of use, and API capabilities. While Claude 3.5 Sonnet offers a more intuitive interface with multimodal support, o3-mini provides a streamlined, text-only experience suitable for simpler applications.

Accessibility

Both models are accessible via APIs; however, Claude’s integration with platforms like AWS Bedrock and Google Cloud enhances its usability across different environments.

Ease of Use

• Users have reported that Claude’s interface is more intuitive for generating complex outputs due to its multimodal capabilities.
• o3-mini offers a straightforward interface that is easy to navigate for basic tasks.

API Capabilities

• Claude 3.5 Sonnet provides API endpoints suitable for large-scale integration, enabling seamless incorporation into existing systems.
• o3-mini also offers API access, but might require additional optimization for high-demand scenarios.

Integration Complexity

• Integrating Claude’s multimodal capabilities may involve additional steps to handle image processing, potentially increasing the initial setup complexity.
• o3-mini’s text-only focus simplifies integration for applications that do not require multimodal inputs.

Cost Efficiency Analysis

Below we will analyze the pricing models, token costs, and overall cost-effectiveness of OpenAI o3-mini and Claude 3.5 Sonnet to help users choose the most budget-friendly option for their needs.

Claude 3.5 Sonnet offers a balance between performance and cost, with pricing tiers that accommodate various usage patterns. o3-mini provides a cost-effective alternative, especially for tasks where high-level sophistication isn’t required, making it ideal for budget-conscious applications. When evaluating the total cost of ownership, consider factors such as development time, maintenance, and operational costs to make an informed decision that fits within budgetary constraints.

Prioritize Price (Choose OpenAI o3-mini)

• When minimizing costs is crucial. o3-mini is significantly cheaper for both input and output tokens.
• For applications where energy costs are a concern due to the computationally intensive nature of reasoning models.

When Price is Secondary (Choose Claude 3.5 Sonnet)

• If the application demands higher-quality output and/or nuanced insights, justifying a higher cost per token.

OpenAI o3-mini vs Claude 3.5 Sonnet: Application-based Comparison

In this section, we will explore how OpenAI o3-mini and Claude 3.5 Sonnet perform on different prompts.

Task 1: Write a Python Function 

Prompt: “Write a function in Python that performs topological sorting on a directed acyclic graph (DAG). The graph will be represented as a dictionary where keys are nodes and values are lists of their adjacent nodes. Include error handling to detect if the input graph contains a cycle.”

o3-mini (High) Response

# Example usage:
graph = {
    'A': ['B', 'C'],
    'B': ['D'],
    'C': ['D'],
    'D': []
}
try:
    sorted_nodes = topological_sort(graph)
    print("Topologically sorted nodes:", sorted_nodes)
except ValueError as e:
    print(e)

Output:

Topologically sorted nodes: ['A', 'C', 'B', 'D']

Claude 3.5 Sonnet Response

# Example usage
graph = {
    'A': ['B', 'C'],
    'B': ['D'],
    'C': ['D'],
    'D': []
}

try:
    result = sort_graph(graph)
    print(f"Topological order: {result}")  # Output: ['A', 'C', 'B', 'D']
except ValueError as e:
    print(f"Error: {e}")

# Example with a cycle
cyclic_graph = {
    'A': ['B'],
    'B': ['C'],
    'C': ['A']
}

try:
    result = sort_graph(cyclic_graph)
except ValueError as e:
    print(f"Error: {e}")  # Output: Error: Graph contains a cycle

Output:

Topological order: ['A', 'C', 'B', 'D']
Error: Graph contains a cycle

You can find the complete code generated by the models, here.

Summary of Output

• Algorithm: Claude 3.5 Sonnet uses Depth First Search (DFS) while o3-mini does not, this could provide better overall performance
• Error Handling: Claude 3.5 Sonnet includes graph validation which enhances the robustness of the function, o3-mini does not include this type of error handling.
• Function Decomposition: Claude 3.5 Sonnet is divided into clear and specific functions that can improve debugging
• Documentation: Claude 3.5 Sonnet has better overall documentation that includes Args, Returns and Raises

Verdict:

o3-mini (high) ❌ | Claude Sonnet 3.5 ✅

Task 2: Data Structure Manipulation

Prompt: “Write a Python class that implements a Bloom filter with configurable size and number of hash functions. Include methods to add elements to the filter and check if an element is present. The implementation should be memory-efficient and handle hash collisions effectively.”

o3-mini (High) Response

# Example usage:
bloom = BloomFilter(size=1000, num_hash_functions=3)

# Add elements
bloom.add("apple")
bloom.add("banana")

# Check if elements are present
print(bloom.contains("apple"))   # Output: True
print(bloom.contains("banana"))  # Output: True
print(bloom.contains("cherry"))  # Output: False (most likely, but might return True due to false positives)

Output:

True
True
False

Claude 3.5 Sonnet Response

class CollisionAwareBloomFilter(BloomFilter):
    """
    A Bloom filter that tracks hash collisions for analysis purposes.
    """
    
    def __init__(self, size, hash_count, *args, **kwargs):
        # Initialize the parent class with the size and hash_count
        super().__init__(size, hash_count, *args, **kwargs)
        
        # Store num_hashes and initialize collision tracking structures
        self.hash_count = hash_count
        self.collision_count = 0
        self.hash_positions = set()
    
    def add(self, item):
        """
        Add an item to the filter while tracking collisions.
        
        Args:
            item: Item to add
        """
        hash_values = self._get_hash_values(item)
        for hash_val in hash_values:
            if hash_val in self.hash_positions:
                self.collision_count += 1
            self.hash_positions.add(hash_val)
            self.bit_array[hash_val] = 1
        self.item_count += 1
    
    def get_collision_stats(self):
        """
        Get statistics about hash collisions.
        
        Returns:
            dict: Collision statistics
        """
        return {
            'total_collisions': self.collision_count,
            'unique_positions_used': len(self.hash_positions),
            'collision_rate': self.collision_count / max(len(self.hash_positions), 1)  # Avoid division by zero
        }

# Example usage:
# Initialize with size and hash_count
bloom_filter = CollisionAwareBloomFilter(size=1000, hash_count=3)

# Add items to the Bloom filter
items_to_add = ['item1', 'item2', 'item3']
for item in items_to_add:
    bloom_filter.add(item)

# Get collision statistics
collision_stats = bloom_filter.get_collision_stats()
print(collision_stats)

Output:

{'total_collisions': 0, 'unique_positions_used': 9, 'collision_rate': 0.0}

You can find the complete code generated by the models, here.

Summary of Output

• Hashing Algorithm: Claude 3.5 Sonnet uses the mmh3 hashing, O3 uses md5. Since md5 has known security issues for cryptography it would not be appropriate for the prompt.
• Configuration: Claude 3.5 Sonnet can be configured for different sizes and hash functions. In addition it can calculate optimal size and hash based on the error rate and item count. It is far more advanced.
• Memory: The bit array implementation uses the bitarray library for more efficient memory.
• Extensibility: The Bloom filter collision aware is implemented.

Verdict:

o3-mini (high) ❌ | Claude Sonnet 3.5 ✅

Task 3: Dynamic Web Component – HTML/JavaScript

Prompt: “Create an interactive physics-based animation using HTML, CSS, and JavaScript where different types of fruits (apples, oranges, and bananas) fall, bounce, and rotate realistically with gravity. The animation should include a gradient sky background, fruit-specific properties like color and size, and dynamic movement with air resistance and friction. Users should be able to add fruits by clicking buttons or tapping the screen, and an auto-drop feature should introduce fruits periodically. Implement smooth animations using requestAnimationFrame and ensure responsive canvas resizing.”

O3-mini Response

You can find the complete code generated by the models, here.

Claude 3.5 Sonnet Response

You can find the complete code generated by the models, here.

Summary

Claude 3.5 uses physics-based animation to create realistic fruit drops, with gravity, collision handling, and dynamic interactions that respond to user input. It offers a lifelike simulation with effects like acceleration, bounce, and rotation. In contrast, OpenAI o3-mini uses basic CSS keyframe animation for a simple falling fruit effect. While it provides smooth animations, it lacks real-time physics and interactivity, with fruits following predefined motion paths and consistent fall speeds.

Verdict:

o3-mini (high) ❌ | Claude Sonnet 3.5 ✅

Task 4: Interactive Form Validation – HTML/JavaScript

Prompt: “Create an HTML form with fields for name, email, and phone number. Use JavaScript to implement client-side validation for each field. Name should be non-empty, email should be a valid email format, and phone number should be a 10-digit number. Display appropriate error messages next to each field if the validation fails. Prevent form submission if any of the validations fail”.

O3-mini (High) Response:

• Basic Structure: The form is simple with basic HTML elements (inputs for name, email, and phone number).
• Validation: The JavaScript function validateForm() handles validation for:
  • Name: Checks if the name is provided.
  • Email: Checks if the email follows a valid format.
  • Phone: Validates that the phone number consists of 10 digits.
• Error Handling: Error messages appear next to the respective input field if validation fails.
• Form Submission: Prevents submission if validation fails, displaying error messages.

Claude 3.5 Sonnet Response

• Design and Styling: It includes a cleaner and more modern design using CSS. The form is contained in a centered card-like layout with input field styling and responsive design.
• Validation: The FormValidator class handles validation using:
  • Real-time Validation: As users type or blur the input fields, the form validates and provides feedback immediately.
  • Phone Formatting: The phone input automatically formats to a xxx-xxx-xxxx style as users type.
  • Field-Level Validation: Each field (name, email, phone) has its own validation rules and error messages.
• Submit Button: The submit button is disabled until all fields are valid.
• Success Message: Displays a success message when the form is valid and submitted, then resets the form after a few seconds.

You can find the complete code generated by the models, here.

Verdict:

o3-mini (high) ❌ | Claude Sonnet 3.5 ✅

Comparative Analysis

Safety and Ethical Considerations

Both models prioritize safety, bias mitigation, and data privacy, but Claude 3.5 Sonnet undergoes more rigorous fairness testing. Users should evaluate compliance with AI regulations and ethical considerations before deployment.

• Claude 3.5 Sonnet undergoes rigorous testing to mitigate biases and ensure fair and unbiased responses.
• o3-mini also employs similar safety mechanisms but may require additional fine-tuning to address potential biases in specific contexts.
• Both models prioritize data privacy and security; however, organizations should review specific terms and compliance standards to ensure alignment with their policies.

Realted Reads:

Conclusion

When comparing OpenAI’s o3-mini and Anthropic’s Claude 3.5 Sonnet, it’s clear that both models excel in different areas, depending on what you need. Claude 3.5 Sonnet really shines when it comes to language understanding, coding support, and handling complex, multimodal tasks—making it the go-to for projects that demand detailed output and versatility. On the other hand, o3-mini is a great choice if you’re looking for a more budget-friendly option that excels in mathematical problem-solving and simple text generation. Ultimately, the decision comes down to what you’re working on—if you need depth and flexibility, Claude 3.5 Sonnet is the way to go, but if cost is a priority and the tasks are more straightforward, o3-mini could be your best bet.

Frequently Asked Questions

My name is Ayushi Trivedi. I am a B. Tech graduate. I have 3 years of experience working as an educator and content editor. I have worked with various python libraries, like numpy, pandas, seaborn, matplotlib, scikit, imblearn, linear regression and many more. I am also an author. My first book named #turning25 has been published and is available on amazon and flipkart. Here, I am technical content editor at Analytics Vidhya. I feel proud and happy to be AVian. I have a great team to work with. I love building the bridge between the technology and the learner.