I use occasinally LLMs as well and mostly the open access versions. From a few latest reddits (which I also verified personally) it seems that currently Claude Opus and ChatGPT-4 perform better. Although I haven't tried the Gemini Pro yet.

https://www.reddit.com/r/LocalLLaMA/comments/1do4m47/best_coding_llm_right_now/
https://www.reddit.com/r/ChatGPTCoding/comments/1cqwfiq/what_coding_llm_is_the_best/
https://lmarena.ai/?leaderboard (in the 'Category' drop-down there is a 'Coding' option which shows the best performing)

When it comes to coding and debugging, several large language models (LLMs) stand out for their value. Here are a few of the most valuable LLMs for these tasks:

1. OpenAI's Codex: This model is specifically trained for programming tasks, making it excellent for generating code snippets, suggesting improvements, and even debugging existing code. It powers tools like GitHub Copilot, which developers find immensely helpful.

2. Google's PaLM: Known for its versatility, PaLM excels in understanding complex queries, making it suitable for coding-related tasks as well. Its ability to generate and refine code snippets is particularly useful for developers.

3. Meta's LLaMA: This model is designed to be adaptable and can be fine-tuned for specific coding tasks. Its open-source nature allows developers to customize it according to their needs, making it a flexible option for coding and debugging.

4. Mistral: Another emerging model that shows promise in various tasks, including programming. It’s being recognized for its capabilities in generating and understanding code.

These LLMs are gaining traction not just for their coding capabilities but also for their potential to streamline the debugging process, saving developers time and effort. If you want to dive deeper into the features and strengths of these models, you can check out the full article here: Best Open Source Large Language Models  LLMs