Which LLM is Better at Coding?

Large Language Models (LLMs) are advanced AI systems trained to understand and generate human-like text. In simple terms, an LLM is a massive neural network (often based on the transformer architecture) that has been fed huge amounts of text data—from books, websites, and code—and learned to predict and produce text in response to prompts. By analyzing patterns in this massive dataset, LLMs learn grammar, context, and even some reasoning abilities, allowing them to generate coherent paragraphs or even write code. Essentially, an LLM can be seen as a predictive text engine on a grand scale – given some input, it predicts the most likely continuation, one token at a time, but with such sophistication that it can carry on meaningful conversations or solve complex tasks.

Under the hood, modern LLMs use deep learning techniques with hundreds of billions (or more) of parameters that encode the vast amount of learned information. Models like GPT-3.5, GPT-4, and Llama 2 were trained on enormous datasets covering a wide variety of subjects—including programming languages—enabling them to understand context and produce human-like results. This includes writing plausible code, which has made LLMs popular as coding assistants.

What is an Open-Source LLM?

Not all LLMs are created equal—some models are proprietary systems accessible only via an API or chat interface, while others are released as open-source models that anyone can run and modify. An open-source LLM’s architecture and learned parameters are publicly available under a license that allows reuse and modification. This means you can download an open-source LLM, run it on your own hardware, fine-tune it with your own data, or integrate it into your applications without needing special permission.

Open-source LLMs have gained significant attention because of their accessibility, transparency, and community-driven development. Since the model’s inner workings are fully exposed, developers can adapt them to specific needs, such as customizing the model for a domain or enforcing a particular coding style. Models like LLaMA 2, CodeLlama, and StarCoder are prominent examples. Their benefits include cost savings, the ability to fine-tune, enhanced data privacy, and rapid community innovation.

Why Use LLMs for Coding?

Using LLMs for coding can significantly boost productivity and improve the quality of software. Here are several reasons why these models are so valuable:

• Code Generation and Autocompletion: LLMs generate code snippets or entire functions from a simple description. This “autocomplete on steroids” feature reduces the time spent writing boilerplate code, letting developers focus on complex logic.
• Improved Code Quality and Optimization: By leveraging vast training data, LLMs often suggest code that is cleaner or more efficient, acting like an experienced pair programmer who nudges you toward better solutions.
• Debugging Assistance: LLMs analyze error messages and problematic code to pinpoint bugs or suggest fixes, effectively serving as an on-demand debugger.
• Learning and Exploration: They serve as on-demand tutors, explaining programming concepts and demonstrating code examples in various languages.
• Multiple Approaches and Creativity: LLMs can generate several solutions to a problem, fostering creativity and offering alternative approaches.
• Time Savings on Routine Tasks: Automating tasks like unit test creation, documentation, and data transformation lets developers focus on high-level design.
• Code Translation and Migration: They can translate code from one programming language to another, saving time on manual rewrites.

In essence, LLMs act as AI pair programmers that augment human capabilities rather than replace them. They speed up routine tasks, help debug, and even educate—leading to more efficient development cycles.

Emerging New Models in 2025

While established models like GPT-4, Claude 2, CodeLlama, and StarCoder have long dominated the scene, the field is rapidly evolving with several new entrants that push the boundaries even further. Among the notable new models are:

• GPT-4 Turbo: An enhanced version of GPT-4 that offers faster response times and improved cost efficiency without compromising on quality. It’s designed for real-time applications and offers extended context capabilities.
• Claude 3: The next iteration from Anthropic, Claude 3 builds upon the strong performance of Claude 2 with improved accuracy, reasoning, and even more refined natural language understanding.
• Mistral 7B: A new open-source model that has generated buzz for its performance relative to its size. Mistral 7B promises state-of-the-art results for its parameter count and is designed for deployment on more modest hardware.
• Gemini: Google’s next-generation model, Gemini, is expected to integrate cutting-edge advancements in context handling and multi-modal capabilities. Although still emerging, Gemini is poised to be a strong competitor in both natural language and coding tasks.
• Grok by xAI: A newer entrant designed to rival existing chat and coding assistants, Grok integrates advanced reasoning capabilities with efficient performance, making it a model to watch in the near future.

These new models are starting to change the dynamics of AI coding assistance, offering improvements in speed, cost, and context handling while also broadening the range of applications.

Top LLMs for Coding

In addition to the emerging models mentioned above, here’s a look at the current landscape combining established and new LLMs:

GPT-4 / GPT-4 Turbo (OpenAI)

GPT-4 remains a gold standard for coding tasks due to its exceptional accuracy and reasoning. Its latest variant, GPT-4 Turbo, provides faster responses and improved cost efficiency, making it more suitable for real-time applications and extended interactions.

Strengths:
• Industry-leading accuracy and problem-solving abilities.
• Extensive language and library knowledge.
• Large context window, with Turbo offering enhanced speed.
Weaknesses:
• Closed-source with high usage costs.
• Requires API access and may be slower for very simple tasks.

Claude 3 (Anthropic)

Claude 3 is the latest iteration from Anthropic and builds on Claude 2’s strengths with improved natural language understanding and coding performance. It continues to offer a massive context window, which is ideal for large projects.

Strengths:
• Very large context window (up to 100K tokens).
• Excellent coding accuracy and clear explanations.
• Competitive pricing and improved performance in its new version.
Weaknesses:
• Closed-source with limited self-hosting options.

CodeLlama 2 (Meta)

An evolution of the original CodeLlama, CodeLlama 2 is designed with further optimizations for code generation and better instruction following. It is fully open-source and available in multiple sizes, making it an excellent choice for customization and offline use.

Strengths:
• Open-source and highly customizable.
• Improved performance over its predecessor, especially in Python.
• Offers multiple model sizes to suit different hardware capabilities.
Weaknesses:
• Larger variants require significant compute resources.

StarCoder 2 (BigCode Project)

StarCoder 2 is the next iteration from the BigCode initiative, offering improved accuracy and efficiency over the original StarCoder. It maintains support for 80+ programming languages and is optimized for self-hosting and local deployment.

Strengths:
• Open-source with broad multilingual support.
• Optimized for code completion and efficient inference.
• Ideal for developers who need a lightweight, self-hosted solution.
Weaknesses:
• Raw accuracy is still behind that of larger models like GPT-4 Turbo.

Mistral 7B (Mistral)

Mistral 7B is a new, open-source model that has quickly garnered attention for its performance relative to its compact size. Designed to be efficient and highly capable, it is an appealing option for developers with modest hardware who still need robust coding assistance.

Strengths:
• State-of-the-art performance for its parameter size.
• Very efficient and deployable on lower-end hardware.
• Fully open-source for customization.
Weaknesses:
• Being newer, it might have less community fine-tuning and integration compared to longer-established models.

Google Gemini (Google)

Gemini represents Google’s next-generation effort in large language models. Although still emerging, Gemini is expected to offer significant advancements in multi-modal understanding and context handling, potentially making it a formidable competitor for coding tasks in the near future.

Strengths:
• Promising advancements in context and multi-modal capabilities.
• Expected to integrate deeply with Google’s ecosystem.
Weaknesses:
• Details are still emerging; availability and performance metrics are yet to be fully verified.

Grok (xAI)

Grok, developed by xAI, is another new entrant aiming to rival established models. It combines advanced reasoning with efficient performance and is designed to offer competitive coding assistance in a conversational format.

Strengths:
• Designed for natural, conversational interactions.
• Competitive performance and fast responses.
Weaknesses:
• Still in the early stages of deployment and integration.

Comparison of LLMs for Coding

Below is a comparison table that now includes both established and new models:

Model	Source	Size (parameters)	Code Benchmark (Accuracy)	Context Length	Open for Fine-Tuning	Access & Cost
GPT-4 / GPT-4 Turbo	Proprietary (OpenAI)	Est. ~1.7T	~67% pass@1 (base), with Turbo optimized for speed	8K (up to 32K/128K extended)	No	Paid API
GPT-3.5 (ChatGPT)	Proprietary (OpenAI)	~175B	~50% pass@1 (est.)	4K (up to 16K available)	No	Free (ChatGPT) / Paid API
Claude 3	Proprietary (Anthropic)	Not publicly confirmed	~71% pass@1 (est.) with improvements	100K tokens	No	Paid API (cost-effective)
CodeLlama 2	Open-Source (Meta)	Various (7B, 13B, 34B, 70B)	~53-70% pass@1 depending on fine-tuning	~4K tokens (typical)	Yes	Free (self-hosted)
StarCoder 2	Open-Source (BigCode)	15.5B	~33-40% pass@1 (base); improved in fine-tuned variants	8K tokens	Yes	Free (self-hosted)
Mistral 7B	Open-Source (Mistral)	7B	State-of-the-art for its size	~4K tokens	Yes	Free (self-hosted; efficient)
Google Gemini	Proprietary (Google)	Details emerging	Anticipated to rival top models	Likely high (TBD)	No	Paid API / Integrated within Google services
Grok	Proprietary (xAI)	Details emerging	Competitive performance (TBD)	Moderate to high (TBD)	No	Paid API / Free trial options

Each model offers distinct strengths and trade-offs in accuracy, efficiency, customization, and cost. Your choice depends on your specific requirements—whether you need the absolute highest performance, the ability to handle massive codebases, or the flexibility of a self-hosted solution.

Pros and Cons of Each Model

GPT-4 / GPT-4 Turbo (OpenAI)

Pros:
• Industry-leading code generation and reasoning.
• Extensive knowledge and large context support.
• GPT-4 Turbo offers improved speed and cost efficiency.

Cons:
• Closed-source and expensive.
• Limited customization and dependent on API access.

GPT-3.5 / Codex (OpenAI)

Pros:
• Fast, cost-effective, and widely accessible.
• Excellent for routine coding queries.

Cons:
• Less accurate on complex tasks.
• Limited customization.

Claude 3 (Anthropic)

Pros:
• Massive 100K-token context and excellent accuracy.
• Clear explanations and reliable performance.
• More cost-effective than some competitors.

Cons:
• Closed-source and limited fine-tuning options.

CodeLlama 2 (Meta)

Pros:
• Fully open-source and highly customizable.
• Improved performance over previous iterations.
• Available in multiple sizes.

Cons:
• Larger variants require substantial hardware resources.

StarCoder 2 (BigCode)

Pros:
• Open-source with strong multilingual support.
• Optimized for code completion and local deployment.

Cons:
• Raw accuracy remains lower than that of the largest models.

Mistral 7B (Mistral)

Pros:
• State-of-the-art performance for its compact size.
• Highly efficient and suitable for modest hardware.

Cons:
• Newer model with less extensive community integration.

Google Gemini (Google)

Pros:
• Promising advancements in multi-modal and context handling capabilities.
• Deep integration with Google’s ecosystem.

Cons:
• Details are still emerging, and performance metrics are not fully verified.

Grok (xAI)

Pros:
• Designed for natural, conversational interactions with coding tasks.
• Competitive performance and fast responses.

Cons:
• Still in early deployment stages with evolving features.

Use Cases of Coding LLMs

LLMs for coding have diverse applications across the software development lifecycle:

• Intelligent Code Autocompletion: LLMs suggest entire lines or blocks of code in real time, dramatically speeding up development.
• Chat-Based Programming Help: Developers can ask coding questions and receive detailed explanations and code examples.
• Debugging Assistance: Paste error messages or code snippets to get potential fixes or identify bugs.
• Refactoring and Optimization: LLMs can improve code readability and efficiency by rewriting or optimizing code.
• Documentation and Comment Generation: Automatically generate docstrings, inline comments, or README content.
• Learning and Exploration: Use LLMs as tutors to learn new languages or frameworks with step-by-step examples.
• Code Translation: Convert code from one programming language to another, easing the migration of projects.
• Test Case Generation: Generate unit tests and edge cases to improve code robustness.
• Security and Code Analysis: Identify potential vulnerabilities or outdated practices for improved security.
• Automated Refactoring Across Files: Apply similar code updates or changes across multiple files seamlessly.
• DevOps and Configuration Scripting: Create Dockerfiles, Kubernetes configurations, or CI/CD workflows quickly.
• Data Transformation and Scripting: Write scripts to transform data formats based on provided examples.

In summary, LLMs serve as versatile assistants that enhance productivity, code quality, and learning by handling routine tasks and offering on-demand expertise.

Which LLM is the Best for Coding?

Determining the “best” LLM for coding depends on your specific needs:

• Top Performance: For the highest accuracy and reliability on complex tasks, GPT-4 (and GPT-4 Turbo) remains the gold standard.
• Large Context Handling: For projects involving massive codebases or extended files, Claude 3’s 100K-token window is ideal.
• Cost and Accessibility: For everyday coding queries with minimal cost, GPT-3.5 or Google Bard offer great value.
• Customization and Data Privacy: For offline use or custom fine-tuning, open-source models like CodeLlama 2 or StarCoder 2 provide full control.
• Emerging Innovations: New entrants like Mistral 7B, Gemini, and Grok promise exciting advancements and may be the future leaders in coding assistance.

In essence:

• GPT-4 / GPT-4 Turbo is the best for high-end, complex coding tasks.
• Claude 3 excels at large-context and comprehensive code analysis.
• CodeLlama 2 and StarCoder 2 are the top open-source choices for customization and privacy.
• GPT-3.5 and Bard deliver excellent everyday assistance.
• Mistral 7B, Gemini, and Grok are emerging models to watch for future improvements.

Ultimately, the “best” model depends on your workflow, project requirements, and budget. Many developers choose a hybrid approach—using a mix of cloud-based and self-hosted models—to meet all their needs.

Conclusion

AI-powered coding assistants have transformed software development. Established models like GPT-4 and Claude 2, along with their newer iterations (GPT-4 Turbo, Claude 3), continue to push the boundaries of code generation and debugging. Meanwhile, open-source models such as CodeLlama 2, StarCoder 2, and the emerging Mistral 7B offer unprecedented customization and privacy.

Key takeaways:

• LLMs are advanced AI models that generate and understand code, enabling them to serve as powerful coding assistants.
• Both proprietary and open-source models have unique advantages—proprietary models lead in performance and context, while open-source models excel in customization and cost savings.
• New models like GPT-4 Turbo, Claude 3, Mistral 7B, Gemini, and Grok are shaping the future of AI coding assistance with improved speed, efficiency, and context handling.
• Use cases range from intelligent code autocompletion and debugging to documentation, translation, and test generation, significantly boosting productivity.
• The best model for you depends on your specific needs—whether it’s raw performance, handling large codebases, cost efficiency, or offline customization.

Embracing these tools is not about replacing developers but augmenting our capabilities. With AI-powered coding assistants, developers can write cleaner code, debug more effectively, and focus on the creative and complex aspects of software development. The future of coding is a collaborative effort between human ingenuity and continually evolving AI, ensuring that whichever model you choose today will only be part of an exciting, ever-improving ecosystem.