In May 2024, OpenAI released GPT-4o—a faster, more efficient version of the regular GPT-4. Anthropic responded soon with (a better) Claude 3.5 Sonnet in June, the first release in the upcoming Claude 3.5 AI model series. Both these models drew comparisons for their high-end AI code generation abilities. And then there’s Mistral’s Codestral, an open-weight generative AI model designed for complex code generation tasks.
We’ll discuss it in this article, comparing all three models across various metrics, explaining where each model excels, and providing actionable insights for developers to choose the best model for AI code generation.
What is AI Code Generation?
AI code generation is when an artificial intelligence model automatically generates code for you based on your commands. These models are trained to understand natural language prompts and convert them into executable code snippets. They significantly reduce the time and effort required for programming tasks.
Take this analogy as an example: you have a friend who is an excellent cook. You tell them what kind of dish you want in simple words, like “make noodles with tomato sauce.” Without needing a detailed recipe, your friend cooks you a delicious dish quickly and perfectly. Similarly, you give the AI clear instructions in plain language, and it instantly writes the correct code for you.
How Does AI Code Generation Work?
AI code generation models use large datasets of programming languages and natural language instructions to learn patterns and relationships. They use techniques like neural networks, reinforcement learning, and transfer learning to understand the context behind a given prompt, enabling them to generate accurate and functional code. The primary goal is to create code that not only works but is also optimized and adheres to best practices.
AI code generation models are accessible via several user-friendly interfaces such as Bind AI, Github Copilot, ChatGPT, Meta AI. These are some of the best AI code generator in 2024, with dedicated features for developers such as code completion, generation, compiler, execution, preview.
These are the best models for AI Code Generation
Best Overall: Claude 3.5 Sonnet
Anthropic’s Claude 3.5 Sonnet represents a significant leap forward in AI technology. It offers a 200K token context window and operates at twice the speed of its predecessor, Claude 3 Opus. The model is designed to handle complex instructions with nuanced understanding, making it exceptionally proficient in advanced reasoning and coding tasks.
Internal tests have shown Claude 3.5 Sonnet solving 64% of coding problems, a substantial improvement over previous models.
Test the model on: Bind AI, Claude AI
- Advanced Reasoning: Claude 3.5 Sonnet excels in tasks that require deep contextual understanding and complex reasoning. This makes it ideal for intricate coding tasks that involve multi-step problem-solving and nuanced instructions.
- Coding Abilities: With a high success rate in solving coding problems, Claude 3.5 Sonnet is well-suited for generating detailed and sophisticated code snippets.
- Speed and Cost-Efficiency: Operating at twice the speed of its predecessor with improved cost efficiency, Claude 3.5 Sonnet is a great choice for projects that require rapid prototyping and iterative development.
Best for ChatGPT users: GPT-4o
OpenAI’s GPT-4o is the fastest version of the GPT-4 model, capable of processing and generating text, code, and other data formats. Its multimodal nature allows for a more comprehensive understanding of coding problems and provides more contextually relevant solutions. While matching the performance of GPT-4 Turbo on text and code tasks, GPT-4o offers a cost-effective option through the API.
Based on several benchmarks, Claude 3.5 sonnet is a better model, however if you are using ChatGPT plus or using OpenAI APIs in your tools, then GPT-4o is the best available option.
Test the model on: Bind AI, ChatGPT
- Multimodal Capabilities: GPT-4o’s ability to handle text, audio, image, and video inputs simultaneously makes it uniquely versatile. It excels in projects that require a combination of different types of media.
- Efficiency: GPT-4o provides fast and cost-effective solutions, making it suitable for a wide range of applications from simple text-based tasks to complex multimodal interactions.
- Human-like Interaction: The model’s advanced understanding of natural language and ability to generate human-like responses make it ideal for interactive applications, such as chatbots and virtual assistants.
Best Open Source: Mistral Codestral
Mistral Codestral is a model specifically designed for code generation and trained on 80+ programming languages. It is known for its strong performance in code generation, particularly in producing clean and efficient code. While specific benchmark details across various languages are less publicized, the model is recognized for its ability to handle complex programming tasks and debugging with ease.
Mistral Codestral’s focus on generating optimized code makes it a reliable choice for large-scale software development projects.
Test model on: Bind AI, Mistral AI
- Clean Code Generation: Mistral Codestral is particularly known for producing clean and efficient code, which is crucial for maintaining high-quality software development standards.
- Complex Programming Tasks: The model performs exceptionally well in handling complex programming and debugging tasks, making it a reliable tool for developers working on large-scale projects.
- Optimized Code: Mistral Codestral’s focus on optimization ensures that the generated code is not only functional but also efficient and maintainable.
Which model should you use for coding?
Choosing a model depends on your specific use cases and needed. If you are using APIs of these models, the cost efficiency makes a huge difference. As an example, Claude 3.5 Sonnet can be up to 5X expensive as compared to Mistral Codestral. If you need simple boiler plate code generation a cheaper model may be a better choice, however if you have complex coding tasks which require understanding existing code and enhancing it, using models with larger context window and reasoning abilities is a better choice.
Let’s compare models on their strengths and their ideal use cases.
| Model | Strengths | Ideal Use Cases |
| Claude 3.5 Sonnet | Complex problem-solving, deep contextual understanding, speed, efficiency | Complex coding tasks, quick prototyping, detailed analysis, data extraction |
| GPT-4o | Multimodal input handling, cost-efficiency, advanced natural language understanding | Multimodal projects, cost-effective solutions, interactive applications |
| Mistral Codestral | Clean and efficient code generation, complex debugging, reliability | Clean code, complex debugging, large-scale software development |
| Llama 3 | Enhanced performance, efficiency, and scalability, strong in various programming languages | Versatile code generation, complex tasks, multi-step problem solving |
| Jurassic-1 Jumbo | Speed, accuracy, versatility, ability to handle large codebases | Rapid prototyping, code completion, large-scale code refactoring |
| StarCoder | Specialized in code understanding and generation, strong in Python and C++ | Code search, code summarization, code translation |
| Code Llama | Efficient code generation, strong in Python, Java, and C++, versatile | Code completion, code generation, code debugging |
Example Prompts to Try
Python Code Generation:
- Prompt: Write a script to generate email addresses from names and domains. Use Elon Musk as an example, with Tesla.com as the domain.
- Link: Try with Bind AI Copilot
- Claude 3.5 Sonnet: Generated multiple patterns, such as elon@tesla.com, musk@tesla.com, elon.musk@tesla.com, emusk@tesla.com.
- GPT-4o: Generated elon.musk@tesla.com.
Web Page Creation:
- Prompt: Create an HTML file that displays a simple personal portfolio webpage. The webpage should include a header with your name, a profile picture, a brief introduction about yourself, and a list of your skills.
- Link: Try with Bind AI Copilot
- Claude 3.5 Sonnet: Created a visually appealing webpage with minimal instructions.
- GPT-4o: Checked all the boxes but required additional instructions for styling.
API Query Generation:
- Prompt: Write a cURL command to call the DALL-E 3 API and generate an image of a Unicorn with a rainbow horn.
- Link: Try with GPT-4o
- Claude 3.5 Sonnet: Directly generated a cURL command and returned the expected result.
- GPT-4o: Generated a bash script which required additional steps but also worked.
What about GPT-4 and Claude 3 Opus?
While this article focuses on the latest models, it’s worth mentioning that both GPT-4 and Claude 3 Opus were strong predecessors. GPT-4, when it launched, set the benchmark for AI code generation with its comprehensive capabilities, while Claude 3 Opus was notable for its precision and extensive context handling. It’s also interesting to see how Claude 3.5 Sonnet outperforms Claude 3 Opus on various metrics despite potentially being the weakest of the Claude 3.5 lineup. It’ll be interesting to see what a maximized Claude 3.5 will look like.
The Bottom Line
Core performance results put Claude 3.5 Sonnet at the top, but that doesn’t paint the whole picture. While Sonnet excels in advanced reasoning and complex coding tasks, GPT-4o is unmatched in multimodal capabilities and cost efficiency. Meanwhile, Mistral Codestral is best for generating clean, efficient code and handling complex debugging for large-scale software development. So, how should you decide?
Try to experiment with example prompts and consider the unique strengths of each model. That will help you choose the best one for your needs.
We also highly recommend you check our 2024 list of the best AI code generators. It provides deeper context, information, and pricing for numerous platforms