Top 6 Best Coding LLMs

Introduction 

In this comprehensive guide, we’ll not only rank the Top 6 Best Coding LLMs but also show how these coding LLMs can greatly improve your development process.

We investigate the best coding LLMs, looking at their uniqueness and how they are improving software development productivity with AI. Everyone can now access their own paired programming partner. Hopefully this list can provide you with enough information to make an informed decision on which coding LLM you can use in your daily coding workflow.

According to a research study by McKinsey developers who leverage AI coding assistants can experience productivity increases of up to 45%. Discover how you can join the growing cohort of developers leveraging AI our list of the best coding LLMs.

Understanding Large Language Models 

Large Language Models, commonly known as LLMs, are artificial intelligence systems developed to understand and generate human language and more specifically for the purpose of our use case creating useful working code.

The development of language models is quite long. It started with simple rule-based models and then moved to some complex machine-learning models. The leap came with transformers in 2017 and advanced models like OpenAI’s GPT models.

As language models have advanced, specialized LLMs are now designed specifically to assist software developers with coding, moving beyond simply generating sentences.

Ranking The Best Coding LLMs

Evaluation Metrics

Three essential parameters determining the rank of Large Language Models are processing power, complexity, and versatility.

Processing power determines how efficiently the model can handle data in abstract locations for intricate tasks.

Complexity measures the model depth, number of layers, and parameters that influence the capacity of the model to make fine distinctions between languages.

Versatility, regarding performance across a range of natural language processing tasks into more specialized applications like coding assistance, is how the model adapts.

Relevance

These evaluation metrics are relevant to understanding what an LLM can do. High processing power is essential for performance and speed, especially for real-time-responding applications.  

The intricacy of a model depends on the level at which it imitates human-like language perception and generation; hence, it is very instrumental for effective communication.

Versatility has been a critical factor in determining the model’s utility in many industries and applications, thus broadening its impact and usefulness.

Top 6 Best Coding LLMs

1. Claude 3.5 Sonnet 

Overview 

Claude 3.5 Sonnet, developed by Anthropic, is the successor to Claude 3. It boasts enhanced performance, longer responses, and better accessibility through an API and a new beta website. Beating GPT-4 and GPT-4o on our best coding LLM list, which has even surprised me as an OpenAI fanboy.

This model is designed to be user-friendly, with improved conversation skills, clearer explanations of its thought processes, and reduced potential for harmful outputs.

Key Features 

• Improved Performance: Claude 3.5 Sonnet has demonstrated significant improvements in various tests, including scoring 76.5% on the multiple-choice section of the Bar exam, up from 73% with its predecessor. 
• Enhanced Coding Skills: The model’s coding abilities have also seen notable advancements. It achieved a 71.2% score on the Codex HumanEval, a Python coding test, compared to the 56% scored by Claude 1.3.
• Superior Mathematical Ability: In solving grade-school-level math problems (GSM8K), Claude 3.5 Sonnet scored 88%, a 2.8 percentage point increase over Claude 1.3. 
• Expanded Token Context Window: Claude 3.5 Sonnet offers a massive 200K token context window, allowing it to work with extensive amounts of data, such as entire codebases or lengthy literary works. 
• Reduced Hallucination Rates: The model has shown a significant decrease in false statements, making it more reliable for enterprises to build high-performing AI applications. 

Development and Usage 

Claude 3.5 Sonnet was developed using a mix of websites, licensed datasets, and user-supplied data, of which approximately 10% were non-English.

This diverse training data contributed to its improved performance.

The model is designed for various applications, including document translation, business planning, and complex contract analysis.

Pros and Cons 

• Pros: 

• Enhanced reasoning and self-awareness capabilities. 
• Ability to handle multi-step instructions effectively. 
• Greater awareness of its limitations. 
• Large context window enabling detailed analysis of extensive documents. 

• Cons: 

• Still susceptible to generating inappropriate responses, despite improvements. 
• May not be suitable for high-stakes situations involving physical or mental health. 
• Development is iterative and ongoing, meaning the model is continuously evolving. 

Read our article about Claude 2, which we covered it in depth!

2. OpenAI GPT-4o 

Overview 

Previously number 1 on our best coding LLMs list, OpenAI’s GPT-4o has been knocked to second place by Claude 3.5 Sonnet. However, with the recent release of o1-preview we don’t expect OpenAI to be in second place for long. Once we have had more time to test the new OpenAI models o1-preview and o1-mini we will update our list. You can read more about the OpenAI o1-preview benchmarks on OpenAI’s blog.

Developed by OpenAI, GPT-4o represents a significant improvement in the field of large language models. It is a more advanced version of its predecessors, GPT-4 and GPT-3.5, offering enhanced text processing capabilities, including handling visual and auditory inputs.

This multimodal system is designed to process text, images, videos, and audio, opening many new applications and use cases.

Key Features 

• Multimodal Capabilities: Unlike previous versions, GPT-4o can process and respond to text and images, which allows it to handle a wider range of tasks, including interpreting and generating responses based on visual information. 
• Enhanced Language Processing: It demonstrates a marked improvement in handling complex language tasks and performing better in coding, mathematics, and reasoning. 
• Expanded Context Window: GPT-4o can process up to 25,000 words, providing more comprehensive solutions and analyses, especially for longer documents or conversations. 
• Internet Accessibility: A notable new feature is its ability to access the Internet, which was unavailable in its predecessors. This feature significantly broadens its knowledge base and potential applications. 
• Safety and Reliability: OpenAI has focused on making GPT-4o safer by reducing the model’s propensity for generating harmful or biased content and improving its ability to refuse to answer contentious questions. 

Development and Usage 

GPT-4o has been trained on a diverse dataset, including websites, licensed data sets, and user-contributed data.

This extensive training contributes to its improved performance and versatility.

It is used in a variety of applications, from language learning tools like Duolingo to financial wealth management at Morgan Stanley.

Pros and Cons 

• Pros: 

• Versatile and capable of handling complex and nuanced tasks. 
• Multimodal capabilities enhance its utility across different domains. 
• Improved safety features make it more reliable for diverse applications. 

• Cons: 

• Despite improvements, it may still generate inappropriate responses or suffer from “hallucinations”. 
• The complexity of the model might result in occasional bugs or errors in its outputs. 
• The full potential of its multimodal capabilities is still being explored and developed. 

3. DeepSeek Coder V2.5

Overview

DeepSeek Coder V2.5 is a part of the DeepSeek Coder series, a range of code language models developed by DeepSeek AI.

These models are notable for their significant size and comprehensive training data, which includes a blend of code and natural language, and one of cheaper coding models.

Key Features 

• Training Data: Trained on a vast dataset, the model covers multiple domains, including math, code, and reasoning, with context support up to 128K tokens.
• Model Variants: DeepSeek Coder includes a 236B parameter model optimized for various applications.
• Advanced Code Completion: Enhanced code completion capabilities with state-of-the-art performance in benchmarks like AlignBench and MT-Bench.
• Cost-Effectiveness: Competitive pricing at $0.14 per million input tokens and $0.28 per million output tokens.

Development and Usage 

Initially based on the foundational DeepSeek-Coder-Base models, DeepSeek-Coder-33b-instruct was further fine-tuned with an additional 2 billion tokens of instruction data.

This fine-tuning has enhanced its capabilities, particularly in instruction-based tasks. It is remarkable performance metrics indicate its suitability for a wide range of coding-related applications, including complex project-level coding tasks.

Pros and Cons 

• Pros: 

• Versatility in handling multiple programming languages. 
• High performance in code generation and problem-solving tasks. 
• Flexible model sizes for different computational needs. 

• Cons: 

• The substantial model size may require significant computational resources. 
• Complexities in fine-tuning for specific tasks or languages may be challenging for some users. 
• Terms of Use and Privacy Policy Concerns (Grant full license to use and reproduce inputs and outputs)

4. Phind-CodeLlama-34B-v2 

Overview 

Phind-CodeLlama-34B-v2 is a state-of-the-art code generation model developed as an enhanced version of its predecessor, Phind-CodeLlama-34B-v1.

This model represents a significant advancement in AI-driven coding assistance, offering remarkable capabilities in understanding and generating code across multiple programming languages.

Since writing this article, Code Llama received a significant update, with Meta releasing Code-Llama-70B, which performs better than OpenAI’s Chat GPT and the instruct model on par with GPT-4.

Key Features 

• Multilingual Proficiency: Phind-CodeLlama-34B-v2 is proficient in several programming languages, including Python, C/C++, TypeScript, Java, and more, making it highly versatile for diverse coding tasks. 
• Enhanced Training Data: The model is fine-tuned on a proprietary dataset of 1.5 billion tokens, comprising high-quality programming problems and solutions. This unique dataset consists of instruction-answer pairs, providing a robust foundation for the model’s advanced capabilities. 
• High Performance on HumanEval: Achieving a pass rate of 73.8% on HumanEval, Phind-CodeLlama-34B-v2 stands as a current leader among open-source models in this domain, indicating its exceptional code-solving efficiency. 
• Instruction-Tuned: The model has been tuned specifically to be steerable and easy to use, adopting the Alpaca/Vicuna instruction format for enhanced user interaction. 

Development and Usage 

This version of the model is a progression from Phind-CodeLlama-34B-v1, leveraging an additional 1.5 billion tokens of high-quality programming-related data for fine-tuning.

Its development involved using DeepSpeed ZeRO 3 and Flash Attention 2, which enabled the model to be trained efficiently on 32 A100-80GB GPUs over a period of 15 hours.

Pros and Cons 

• Pros: 

• Exceptional proficiency in multiple programming languages. 
• High accuracy and performance in coding tasks and problem-solving. 
• Steerable and user-friendly due to instruction tuning. 

• Cons: 

• The model’s large size (34B parameters) may necessitate substantial computational resources, making it less accessible for many users with limited hardware capabilities. 
• Its specialized focus on programming-related tasks might limit its applicability in other language processing domains. 

5. WizardCoder-Python-34B-V1.0

Overview 

WizardCoder-Python-34B-V1.0 is a highly advanced code generation model, part of the WizardCoder series developed by WizardLM.

It is specifically fine-tuned to understand and execute complex coding instructions, making it a significant tool in the coding LLMs space. 

Key Features 

• Advanced Coding Capabilities: WizardCoder-Python-34B-V1.0 excels in coding-related tasks like code generation, completion, and summarization. 
• Evol-Instruct Method: This model utilizes Evol-Instruct, an evolutionary algorithm, to generate a diverse set of complex instructions, enhancing the model’s performance in understanding and executing coding tasks. 
• High Performance: It has shown impressive results on code generation benchmarks such as HumanEval, surpassing many other models, including GPT-4 and ChatGPT-3.5. 
• Versatile Applications: It is suitable for various coding tasks, including automating DevOps scripts, data analysis, machine learning pipeline generation, web scraping, API development, and blockchain programming. 

Development and Usage 

The development of WizardCoder-Python-34B-V1.0 involved training on an extensive dataset, with the fine-tuning process designed to improve its ability to generate coherent and relevant responses to a range of coding instructions.

This model has been validated on several coding benchmarks and has demonstrated superior performance compared to other open-source and closed LLMs on these benchmarks. 

Pros and Cons 

• Pros: 

• Exceptional ability to handle complex coding instructions. 
• Versatility in various programming-related tasks. 
• High performance on multiple coding benchmarks. 

• Cons: 

• The complexity of the model may require significant computational resources for effective use. 
• It might have limitations outside the specific domain of code generation and completion. 

Read our article about Wizard Coder.

6. GitHub Copilot Chat

Overview 

GitHub Copilot Chat is a chat interface that integrates with GitHub Copilot. It offers developers a platform to ask and receive answers to coding-related questions.

It is designed to streamline access to coding information and support, enabling developers to get assistance without browsing extensive documentation or online forums.

Key Features 

• Integration with IDEs: Copilot Chat is supported in Visual Studio Code, Visual Studio, and JetBrains IDEs, making it accessible within the environments developers commonly use. 
• Range of Topics: It can answer various coding-related questions on syntax, programming concepts, debugging, and more. However, it is not meant for non-coding questions. 
• Language Model Analysis: Utilizes natural language processing and machine learning to understand and respond to queries. This includes analyzing code snippets or plain language inputs and generating code suggestions or explanations. 
• Output Formatting: The responses are well-formatted with syntax highlighting and indentation for clarity. 
• Learning and Improvement: Designed to learn from feedback and evolve to provide more accurate and relevant answers. 

Development and Usage 

Developed as part of the GitHub Copilot ecosystem, Copilot Chat is powered by GPT-4o, a contextually aware AI assistant.

It is designed for various development scenarios, offering real-time guidance in the user’s preferred natural language.

Pros and Cons 

• Pros: 

• Offers real-time coding assistance, enhancing developer productivity. 
• Seamlessly translates between programming languages, aiding in code standardization and performance optimization tasks. 
• Personalized to individual developer practices, aligning with unique coding styles and needs. 

• Cons: 

• It may not always provide optimal or complete solutions, requiring manual review and testing. 
• Limited to coding-related queries, not suitable for general knowledge or non-coding questions. 
• As with any AI tool, there is a learning curve in effectively integrating it into the workflow. 

Comparative Analysis

Comparative Overview of Coding LLMs 

Several models have stood out when comparing the best coding large language models (LLMs), each with distinct features and uses. 

• Claude 3.5 Sonnet – Anthropic shows improvements in coding abilities and conversation skills, with a large context window for extensive data handling. It is more user-friendly but may not suit high-stakes scenarios. 
• OpenAI’s Chat GPT-4o – A versatile, multimodal system known for advanced language processing and internet access, expanding its range of applications.
• DeepSeek-Coder-33b-instruct – stands out in code-specific tasks, trained on a massive dataset. It excels in code completion but requires substantial computational power. 
• Phind-CodeLlama-34B-v2 – Proficient in multiple programming languages and achieves high scores in coding benchmarks. However, it focuses on programming, limiting its use in broader language tasks. 
• WizardCoder-Python-34B-V1.0 – Specializes in understanding and executing complex coding instructions. It performs well in various coding benchmarks but, like others, demands significant resources for optimal functionality. 
• GitHub Copilot Chat – Integrated with popular IDEs, provides real-time coding assistance, especially useful for syntax and debugging. While it enhances productivity, it requires user verification for optimal solutions. 

Each model has its strengths: GPT-4o and Claude 3.5 Sonnet for broader applications, DeepSeek-Coder-33b-instruct and Phind-CodeLlama-34B-v2 for specialized coding tasks, WizardCoder for complex coding instructions, and GitHub Copilot Chat for integrated coding support.

I personally recommend Anthropic Claude 3.5 Sonnet or OpenAI’s Chat GPT-4o.

Conclusion

In concluding the analysis of the best coding LLMs, it’s evident that these advanced AI tools are revolutionizing software development.

Each model offers unique capabilities, catering to a wide range of coding needs and preferences.

This variety ensures that developers can find the most suitable LLM for their specific projects, enhancing efficiency and creativity in coding.

As the AI landscape evolves, these LLMs signify a significant shift towards more intelligent, versatile, and user-friendly coding environments, promising an exciting future for AI-driven software development.