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The Top LLMs For Code Generation: 2024 Edition

Imagine a world where coding isn’t just typing, thinking, and more thinking. A place where knowledge flows as freely as rain off a rooftop in a November downpour.

Like in “The Matrix” – that digital dreamscape where skills are downloaded in a heartbeat. You want Kung Fu? You got it. Helicopter piloting? Just a plug-in away.

Now, snap back to reality, the world of ones and zeroes. Today, Large Language Models (LLMs) are playing the part of that Matrix fantasy. They are the unseen, cerebral sidekicks to these street-smart developers, offering the coding chops on a silver platter. No more sifting through the dusty corners of memory for that elusive syntax.

With LLMs, it’s like the code writes itself, whispered into existence by a digital muse.

These models are not just smart. They are like a thousand coders, a thousand experiences, all rolled into one. They see the curves and corners of programming languages with the ease of a seasoned hunter spotting a trail.

In this brave new world, the developer’s role is evolving. They are the strategists, the big-picture thinkers, while their LLM partners handle the nitty-gritty of code. It is a partnership, a dance of human ingenuity and AI efficiency.

Let us take a brief journey to the matrix of code generation with LLMs – a realm where the future of software development is being rewritten, one line of code at a time.

The Transformative Potential of LLMs in Software Development

In the 1950s, it was a world of assembly language – raw, unrefined, and brutally honest. Programmers, the unsung detectives of their era, pieced together logic like a jigsaw puzzle.

Fast forward to the ’60s and ’70s, high-level languages like FORTRAN and C stepped into the spotlight. They were the new players in town, cutting through the complexity but still leaving plenty of legwork for the coders.

The 90s brought a new ally – Integrated Development Environments (IDEs). These were the corner diners where coders met, mulling over lines of code with tools like auto-completion and syntax highlighting lighting up the path.

Parallel to this was the open-source movement, a bustling bazaar of code where ideas were currency. It wasn’t just about sharing. It was about building on each other’s work, a prelude to the collaborative nature of LLMs.

LLMs are not just another chapter in the coding saga – they are a whole new volume. They starkly contrast to what came before, a leap as profound as the shift from telegraph to telephone.

Imagine a room where every great coder from the past few decades has left a piece of wisdom. LLMs are that room.

The Real-World Benefits of LLM Code Generation

  1. Enhanced Efficiency: LLMs streamline the coding process, significantly reducing the time spent on writing and debugging code. It’s like having a fast-forward button in the development cycle.
  2. Improved Code Quality: By suggesting optimized and cleaner code, LLMs contribute to the overall quality of the software, reducing errors and enhancing performance.
  3. Rapid Learning and Adaptation: LLMs serve as dynamic learning tools for new and seasoned developers alike, exposing them to various coding styles and best practices.
  4. Error Detection and Correction: LLMs excel at spotting and fixing bugs, turning the tedious task of debugging into a more manageable, even educational, experience.
  5. Creative Problem Solving: With the ability to suggest multiple approaches to a problem, LLMs foster a more creative and innovative problem-solving environment.
  6. Accessibility for Non-Experts: LLMs democratize coding by assisting those with less expertise in writing functional code, bridging skill gaps in teams.
  7. Time-Saving on Routine Tasks: Automating routine coding tasks frees up developers to focus on more complex and creative aspects of software development.
  8. Customization and Personalization: LLMs can adapt to individual coding styles and preferences, offering a personalized coding experience.
  9. Scalability of Projects: LLMs manage the heavy lifting in large-scale projects, making it easier to scale and manage complex coding tasks.
  10. Language Agnosticism: With their ability to understand and generate code in multiple languages, LLMs are versatile tools in a multi-language development environment.

The Best Code Generation LLMs of 2024: A Rundown

In this rundown, we will explore some of the best code-generation LLMs of 2024, examining their features, strengths, and how they compare to each other.

OpenAI Codex

OpenAI Codex, a descendant of GPT-3, is a powerful AI model that generates code from natural language. It powers GitHub Copilot, providing autocompletion in various IDEs. Codex can create code in multiple languages, with Python being its strongest.

It was trained on a vast corpus of code from millions of GitHub repositories, enabling it to generate relevant code snippets. Beyond code generation, Codex assists with transpilation, code explanation, and refactoring. It has been used in diverse applications, from creating browser games to generating data science charts.

OpenAI Codex vs GitHub Copilot

GitHub Copilot is a direct application of Codex. Copilot provides a user-friendly interface and is well-integrated into developers’ workflows, especially within Visual Studio Code. Codex, however, offers a broader range of applications and can be integrated into custom tools, providing more flexibility.

OpenAI Codex vs ChatGPT

OpenAI Codex and ChatGPT differ in their focus areas. Codex specializes in code generation, while ChatGPT excels in conversational text generation. While both can be used for coding tasks, Codex’s specialized training makes it a better choice for precise and contextually appropriate code generation.

Code LLama

Code LLama is Meta’s latest AI programming tool, designed to assist developers in generating code. It represents a significant upgrade in AI-assisted code generation, leveraging a model with 70 billion parameters.

Code LLama’s capabilities include understanding complex programming tasks, generating code snippets, and solving coding problems. It is trained on a diverse dataset, which allows it to work with various programming languages and frameworks, making it a versatile tool for developers.

The tool aims to streamline the coding process, reduce errors, and improve productivity. Code LLama’s advanced algorithms enable it to understand context and intent, which helps in generating more accurate and functional code.

Code LLama vs GPT-4

Code LLama and GPT-4 are both advanced AI models, but Code LLama is specifically optimized for code generation. While GPT-4 has a wide range of language understanding capabilities, Code LLama’s focus on programming makes it potentially more efficient in generating code. GPT-4 can handle various tasks, but Code LLama’s specialized training could offer more precise coding assistance.

Code LLama vs Copilot

Code LLama and GitHub Copilot both aim to enhance the coding experience, but Code LLama’s 70 billion parameter model suggests a more powerful code generation capability. Copilot provides real-time coding suggestions within an IDE. Code LLama, however, may offer more advanced features and a broader understanding of complex coding tasks due to its larger model size.

Codet5 and Codet5+

Codet5 and Codet5+ are AI programming tools developed by Salesforce. They are part of a family of encoder-decoder language models designed for code generation and understanding. They are trained on a diverse dataset, enabling them to work with various programming languages and frameworks. They can understand complex programming tasks, generate code snippets, and provide solutions to coding problems.

Codet5 is based on Google’s T5 framework but incorporates better code-specific knowledge. It can perform operations like code completion, summarization, and translation between different programming languages. Despite its capabilities, Codet5 is not as widely available as other AI programming tools like GitHub Copilot or OpenAI Codex.

Codet5+ is an improved version of Codet5, with flexible model architecture and diverse learning objectives. It can operate as an encoder-only, decoder-only, or unified model, depending on the task. Codet5+ has achieved state-of-the-art performance on many challenging tasks, including zero-shot evaluation on the code generation benchmark HumanEval.

Codet5 vs Codex

In terms of cost, OpenAI Codex is more expensive compared to Codet5, making Codet5 a more affordable choice for budget-conscious users. In terms of performance, Codet5 achieves top performance on fourteen subtasks in a code intelligence benchmark. It significantly outperforms the previous state-of-the-art model on all generation tasks including text-to-code generation, code-to-code translation, and code refinement. On the other hand, Codex, which powers GitHub Copilot, provides real-time coding suggestions within an IDE and has gained additional popularity by its incorporation into ChatGPT.


Tabnine is an AI-powered code completion tool that helps developers write code faster and with fewer errors. It uses a proprietary language model trained on a vast array of high-quality, secure code repositories.

Tabnine is context-aware, offering recommendations based on the developer’s code and patterns. It supports all major programming languages and integrates with most modern IDEs. The tool is designed to understand and apply coding standards and can be expanded with bespoke models based on an organization’s codebase.

Tabnine stands out for its ability to generate blocks of code based on natural language comments and for its enterprise-grade security. Its personalized AI models can be fine-tuned to an organization’s specific codebase, providing tailored code completion suggestions that align with the team’s practices and guidelines.

Tabnine vs Copilot

Tabnine and GitHub Copilot both offer AI-powered code completion, but they differ in their underlying technology and features. Copilot provides real-time coding suggestions and is integrated into Visual Studio Code. Tabnine, with its proprietary model, can be customized to an organization’s codebase, potentially offering more personalized code suggestions. While Copilot is based on a more powerful GPT-4 model, Tabnine’s ability to train on private code repositories may give it an edge in providing organization-specific coding assistance.

Tabnine vs ChatGPT

ChatGPT, while capable of generating code from natural language requests, operates on a dedicated website and requires developers to provide detailed instructions and adapt the code to their environment. Tabnine’s model is aware of organizational coding practices, which is reflected in the accuracy of the code suggestions, whereas ChatGPT functions more as a replacement for search and knowledge bases.


Polycoder is an open-source AI code generation tool developed by researchers at Carnegie Mellon University. Its 2.7 billion parameter model is trained on a 249 GB codebase spanning 12 programming languages, including C, C#, Java, Python, and more.

Polycoder is designed to generate code for various applications such as web development, machine learning, and natural language processing. Its performance is particularly notable in the C programming language, where it achieves lower perplexity and outperforms all other models, including OpenAI Codex.

Despite its strengths in C, Polycoder does not consistently match the performance of the best AI code generators in every task. However, its open-source nature democratizes research in the field of AI code generation, allowing broader access and customization.

Polycoder vs Codex

Polycoder and OpenAI Codex are both powerful code-generation tools, but they serve different segments of the developer community. Codex, with its closed-source model, is integrated into commercial products like GitHub Copilot and offers a wide range of language support. Polycoder, on the other hand, is open-source and particularly excels in the C language, making it a valuable resource for researchers and developers who require an open-source solution or are working extensively with C.

Replit Ghostwriter

Replit Ghostwriter is an AI code generation tool that is integrated into the Replit online IDE. It is designed to automate repetitive coding tasks, generate, and improve code, identify, and fix errors at runtime, and provide easy-to-understand summaries of code.

Ghostwriter offers several features including Complete Code, Generate Code, Edit Code, and Explain Code

  • Complete Code provides in-line code suggestions as you type.
  • Generate Code allows you to give Ghostwriter a natural language prompt, and it will return the code.
  • Edit Code is a unique feature that allows Ghostwriter to refactor your code to run.
  • Explain Code is a feature that provides a step-by-step explanation of the code in plain English, which is especially useful when trying to understand other people’s code or code you wrote in the past.

Replit Ghostwriter vs GitHub Copilot

While both Ghostwriter and Copilot are AI-driven tools that assist with coding tasks, they have different strengths and use cases. One of Ghostwriter’s unique features compared to Copilot is Edit Code, which allows Ghostwriter to refactor your code, translate it into another language, and make your code easier to read.

Ghostwriter can also generate large chunks of code at once, making it useful when you want to write small programs or several related functions in one shot. On the other hand, Copilot is more consistent and is better suited for real-time coding tasks, providing autocomplete-style suggestions directly within the IDE.

Copilot also integrates into multiple editors, including Visual Studio, VS code, & Neovim, making it more versatile in terms of the environments it can be used.

Replit Ghostwriter vs ChatGPT

Ghostwriter is integrated into the Replit platform, providing a seamless experience for developers using the Replit IDE. While ChatGPT is better suited for broader tasks and is not integrated into an IDE, which may require developers to adapt the code to their environment. Ghostwriter is more practical if you need to rewrite an entire code or add comments, tasks that ChatGPT may not handle as well.


AI2SQL is an AI-powered SQL query builder that enables users to build SQL queries without any knowledge of SQL. It is designed to simplify the process of creating SQL queries by allowing users to enter a few keywords about the data, and then automatically generating optimized queries. This can save time and reduce the risk of errors, making it a useful tool for developers, database administrators, and others who need to work with SQL.

AI2SQL is compatible with various databases, including SQL, MySQL, PostgreSQL, Microsoft SQL Server, MongoDB, and Pandas. It also provides a SQL code beautification feature, which enhances code readability, boosts productivity, and prevents syntax errors.

It offers several key features:

  • Playground for generating SQL queries based on predefined datasets.
  • Table management with DDL import and manual table addition.
  • Customizable workspace for generating SQL queries for specific database engines and tables.
  • SQL formatting and error fixing.
  • Formula Bot for data analysis and developer assistance.
  • Database connectors for easy integration with your databases.

This tool also supports multiple natural languages for prompts, such as English, Spanish, French, and German. This means users can ask questions in plain language, and AI2SQL will return the answer as SQL code.

While AI2SQL has been trained on a diverse and substantial dataset, it may not account for all possible SQL dialects or database structures, so carefully reviewing the generated queries is recommended.


StarCoder is a state-of-the-art LLM for code, developed by Hugging Face and ServiceNow as part of the BigCode Initiative. It is trained on permissively licensed data from over 80 programming languages and text from GitHub repositories, including documentation and Jupyter programming notebooks. It can generate code from natural language descriptions, making it a powerful tool for developers.

StarCoder offers several key features:

  • It can process more input than any other open LLM, with a context length of over 8,000 tokens.
  • It can function as a technical assistant, autocomplete code, and modify code via instructions.
  • It outperforms existing open-code LLMs on popular programming benchmarks.
  • It is available as a VS Code extension called StarCoderEx.

StarCoder vs Copilot

When comparing StarCoder and Copilot, several factors come into play, such as functionality, ease of use, and cost.

  • Functionality: Both StarCoder and Copilot offer valuable assistance for developers, providing highly accurate code suggestions and completions. However, StarCoder offers more customization options, while Copilot offers real-time code suggestions as you type.
  • Ease of Use: Both tools are user-friendly and intuitive. However, Copilot is reported to be faster than StarCoder when autocompleting code.
  • Cost: Copilot is a paid service, costing $10 per month or $100 per year, while StarCoder is available for free.

For users who value highly accurate code suggestions and completions and prefer a free, customizable tool, StarCoder may be the best choice. On the other hand, for users who value real-time code suggestions and integration with Visual Studio Code, Copilot may be the best choice.


We are standing at the brink of a coding renaissance. These AI marvels are not just tools but gateways to uncharted realms of efficiency and creativity.

From automating routine coding tasks in software development to innovating in fields like data analysis and AI research, their impact is profound. These models empower developers to build more robust, error-free applications faster than ever.

The future of coding with LLMs seems boundless. We are moving towards a world where coding is more accessible, more intuitive, and, importantly, more aligned with our human way of thinking and solving problems. The barrier between idea and implementation is thinning, opening up a universe of possibilities.

So, here is a call to action for dreamers, innovators, and builders – Dive into the world of LLMs. Let these tools amplify your creativity and efficiency. Whether you’re a seasoned developer or just starting, the time is ripe to harness the power of LLMs and turn your brightest ideas into reality. The future of coding is here, and it is yours to shape.

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