AI-powered language models (LLMs) have become an essential part of the coding world, helping developers:

1. Improve productivity
2. Reduce errors, and
3. Generate high-quality code. 

But how do you choose the right LLM for your team? 

In this article, we’ll break down how developers are deciding between different models and explore the most popular open-source and commercial LLMs being used today.

As we navigate these options, we’ll highlight important considerations to guide your choice.

Open-Source or Commercial LLM? Here’s How to Decide

When deciding on a coding-focused LLM, the first question you'll typically face is whether to choose an open-source or a commercial model. 

Both have advantages, but the choice often depends on your team's needs and resources.

When to Choose an Open-Source LLM

• Privacy is a priority: You can keep all data and infrastructure in-house, ideal for sensitive information.
• You have available memory: Open-source models often require significant computing resources to run.
• Cost is a concern: The models are free, but hardware and operational costs still apply.
• Customization is key: Full control to fine-tune and modify the model for specific needs.

When to Choose a Commercial LLM

• You want top-tier performance: Commercial LLMs like GPT-4o are cutting-edge and optimized.
• Easy setup matters: Commercial models offer simple APIs and minimal setup effort.
• You have limited resources: No need for expensive hardware; infrastructure is handled by the provider.‍
• External data handling is acceptable: Data is processed by third-party servers, which may raise privacy concerns for sensitive data.

Now that we’ve explored the key considerations between open-source and commercial LLMs, let’s dive deeper into the world of open-source LLMs for coding. 

These models offer flexibility and cost efficiency, making them a strong choice for businesses that prioritize customization and control. 

Open-Source LLMs for Coding

If you decide to go with an open-source LLM, your next decision is whether to host it locally or use a hosted provider. 

Local hosting offers more control, but using a hosted provider can ease the burden on your infrastructure. 

Here's a breakdown of some of the most popular open-source LLMs for coding in 2024.

1. Phind

Phind is an advanced AI coding assistant that outperforms GPT-4 in speed and matches or exceeds its capabilities in many coding tasks. It achieves up to 82.3% pass@1 on HumanEval, processes 100 tokens per second (5x faster than GPT-4), and supports a 16K token context window.

Phind-70B also demonstrates a substantial speed advantage over GPT-4 Turbo in text generation. While GPT-4 Turbo processes approximately 20 tokens per second, Phind-70B can generate more than 80 tokens in the same timeframe, showcasing a fourfold increase in processing speed.

• Use: Code generation, debugging, technical Q&A, and pair programming for developers seeking fast, high-quality coding assistance.

2. Qwen

Qwen is a large language model developed by Alibaba with strong coding capabilities. The Qwen-72B version has 72 billion parameters, while Qwen-1.5 is an updated version with improved coding and language understanding abilities.

• Use: General-purpose coding tasks and language processing for developers looking for a versatile AI assistant.

3. Flan-T5 / flan-ul2

Flan-T5 and flan-ul2 are enhanced versions of Google's T5 model, fine-tuned on a variety of tasks, including code generation. These models are particularly useful for translating natural language into code and automating text-based coding tasks. Their open-source nature makes them a flexible option for developers looking to integrate code generation into their workflows.

• Use: Code generation, & natural language to code translation.

4. Mistral

Despite its compact size of just 7 billion parameters, Mistral has garnered significant attention for its impressive performance on coding tasks. They released a new version in September 2024 and have both premier and free models. Released by Mistral AI, this model is relatively new but has already received strong early feedback, particularly for its efficiency and capability in handling code generation and other programming-related tasks.

• Use: Code generation, efficient handling of coding tasks relative to size.

5. DeepSeek Coder V2.5 

DeepSeek Coder V2.5 is an upgraded version that combines DeepSeek-V2-Chat and DeepSeek-Coder-V2-Instruct. DeepSeek Coder V2.5 is an open-source coding model developed by Chinese AI startup DeepSeek. 

The model integrates the general and coding abilities of the two previous versions and better aligns with human preferences. It has been optimized in various aspects, including writing and instruction following.

The DeepSeek Coder series consists of code language models built from the ground up, using a dataset that is 87% code and 13% natural language in both English and Chinese. Each model is pre-trained on 2 trillion tokens.

Use: In addition to coding, the model excels in writing, general reasoning, and language understanding.

6. Code Llama

Code Llama is a cutting-edge large language model (LLM) designed for coding tasks, built on top of Llama 2. Released on August 24, 2023, and further updated on January 29, 2024, with the release of Code Llama 70B, it is available in three specialized versions: 

1. Code Llama (the foundational model), 
2. Code Llama - Python (optimized for Python), and 
3. Code Llama - Instruct (fine-tuned for understanding natural language instructions). 

Code Llama supports various programming languages, including Python, Java, C++, and JavaScript, and excels in code generation, code completion, and debugging. It has been benchmarked against top publicly available LLMs, outperforming them in coding tasks such as HumanEval and MBPP, and is available for free for both research and commercial use.

• Use: Code Llama enhances developer workflows by automating repetitive coding tasks, making it a valuable productivity tool for writing well-documented, robust software.

7. Falcon LLM

With 40 billion parameters, Falcon LLM is a large-scale open-source model, trained on a massive 1 trillion tokens. It is well-suited for complex coding tasks, such as generating technical documentation and handling large codebases, making it a valuable tool for developers working on more advanced coding applications. 

• Use: Code generation & technical documentation.

‍8. FastChat-T5

FastChat-T5 is a fine-tuned, open-source chatbot model that assists with code-related queries. It provides conversational coding help, making it useful for developers who need real-time assistance with their coding tasks, whether in a Q&A format or interactive debugging. 

• Use: Code assistance & chatbot or Q&A-style coding help.

Commercial LLMs for Coding

Commercial LLMs often lead the market with cutting-edge performance, but they come with trade-offs in privacy and cost. 

For businesses seeking high-impact solutions in software development, these models offer robust capabilities, especially in automating and optimizing coding tasks. Below are the top commercial LLMs for coding in 2024.

1. GPT-4o

GPT-4o by OpenAI remains the gold standard in large language models, especially for coding tasks. Renowned for its exceptional ability to generate, refine, and debug code, it is widely adopted across industries for automating and accelerating software development processes. 

However, with great power comes higher costs—GPT-4o can be expensive, and it requires sending your code to OpenAI's servers, raising potential privacy concerns for sensitive projects. Despite this, it’s the go-to choice for enterprises seeking unparalleled performance in code generation and optimization.

• Use: Code generation, debugging, code refinement, & automation of coding workflows.
• Privacy: Code is processed on OpenAI's servers (potential privacy considerations).

2. Cohere Command R+

Cohere’s newest large language model, Command R+ 08 2024, is optimized for conversational interactions and long-context tasks. This model aims for exceptional performance, enabling companies to transition from proof of concept to production effectively.

Command R+ 08 2024 is recommended for workflows utilizing complex retrieval-augmented generation (RAG) functionality and multi-step agents. In contrast, its predecessor, Command R, is better suited for simpler RAG tasks and applications where speed or cost is crucial.

Businesses can leverage Command R+ for various purposes, including code generation, analyzing code patterns, and automating technical documentation. It offers robust natural language processing (NLP) capabilities that improve coding workflows and enhance the overall efficiency of development teams.

• Use:
  Code generation, analyzing code patterns, automating technical documentation.
• Privacy:
  Code is processed via Cohere's API (data privacy depends on provider policies).

3. Claude 3

Claude 3 is a revolutionary AI system developed by Anthropic, designed to enhance programming through natural language processing and machine learning. It enables developers to communicate requirements in plain English, automates code generation, and optimizes code for efficiency and readability. With capabilities like code analysis, debugging assistance, and seamless integration into development environments, Claude 3 aims to streamline the software development lifecycle while fostering collaboration and knowledge sharing among teams.

Use: Developers can use Claude 3 by integrating it into their preferred IDEs, providing natural language instructions to generate and optimize code, and leveraging its analysis tools for debugging and documentation.

Privacy: Claude 3 adheres to strict ethical principles, ensuring data privacy and security while respecting intellectual property rights, promoting responsible use of AI technologies, and maintaining transparency about its capabilities and decision-making processes.

4. PaLM 2

PaLM 2, by Google, is a powerful tool in public preview that offers businesses robust functionality for code generation and analysis. As Google refines its offering, PaLM 2 is becoming increasingly popular for developers interested in exploring alternative LLMs for coding.

• Use: Code generation, experimentation with coding capabilities
• Privacy: Code is sent to Google’s servers for processing

When deciding between these powerful commercial LLMs, it’s essential to consider factors like performance, cost, and data privacy to find the right fit for your business. With various options available, how do you choose the best LLM for your coding needs? Let’s explore the key criteria to help guide your decision-making process.

How Do You Choose the Right LLM for Coding?

Selecting an LLM for your team boils down to a few key factors:

• Privacy vs. Performance: Are you comfortable sharing code externally, or do you need to keep everything in-house?
• Cost: What’s your budget? Open-source models are free, but commercial models might offer better performance.
• Memory and Resources: Do you have the necessary hardware to run large models locally, or would a hosted provider be better?

Choosing the right LLM for your coding needs depends on several factors, including the specific use cases, the resources at your disposal, and your organization’s priorities in terms of privacy, cost, and performance. Here are some key considerations to guide you through this decision-making process.

The decision for which LLMs to incorporate in your data stack boils down to your company’s specific use cases and their respective financial implications. 

Budget for Your Top Use Cases for LLM in Your Coding Process

Start by identifying the main tasks you want to accomplish with an LLM in your software development process. 

Different models excel at different things:

• Automated Code Completion: If you need an LLM to assist with code completion or to suggest coding improvements in real time, models like GPT-4o or Claude 2 may be ideal due to their advanced language prediction capabilities.
• Generating Boilerplate Code: For automating the generation of repetitive or foundational code, models like Mistral or Flan-T5 can handle this effectively, saving developers significant time and effort.
• Debugging and Error Correction: If your focus is on troubleshooting and reducing the time spent on debugging, models like GPT-4o or PaLM 2 offer robust capabilities to analyze code and suggest fixes.
• Learning and Educational Support: If your team is learning a new programming language or framework, models that excel in contextual understanding, such as Claude 2, can offer in-depth examples and explanations to speed up the learning curve.

If you’re still figuring out your use cases, you can explore our use cases to help you identify the right LLM for your coding needs.

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Explore our resources to learn how Shakudo can enhance your coding efficiency and drive business growth. For personalized insights tailored to your organization's needs, contact one of our Shakudo experts today!