Which LLM Dominates Coding? GPT‑4 vs CodeLlama vs Mixtral vs Gemini

Introduction

In the rapidly evolving landscape of artificial‑intelligence tools, software development assistants have secured a solid foothold, especially for coding tasks.

Scope of the Study

This article reports experimental results for four leading large language models (LLMs): OpenAI's GPT‑4, Meta's CodeLlama 70B, CodeLlama 7B, and Mistral's Mixtral 8x7B. The goal is to assess their capabilities as coding assistants and identify which model performs best across a range of programming tasks.

Test Setup

The comparison was conducted inside Visual Studio Code using the “Continue” plugin, which enables direct interaction with each LLM. The environment mirrors the functionality of other coding assistants such as GitHub Copilot and AWS CodeWhisperer, while also offering privacy controls (e.g., running LLMs on private servers) and the ability to switch between the most suitable or cost‑effective model.

The setup is illustrated in the following screenshots.

Evaluation Tasks

The LLMs were evaluated on eight key coding dimensions:

Code generation – ability to produce code snippets or full modules from specifications.

Code explanation and documentation – clarifying existing code and generating meaningful documentation.

Unit‑test generation – autonomously creating unit tests for given code.

Debugging and error correction – identifying, explaining, and fixing code defects.

Refactoring/optimization suggestions – proposing and applying improvements for quality and performance.

Code‑review assistance – spotting potential issues and recommending enhancements.

Security and best‑practice compliance – detecting vulnerabilities and enforcing standards.

Requirement analysis – interpreting natural‑language requirements and translating them into technical specifications.

Each model received a score from 0 to 3 for every task, with 19 repetitions per dimension to ensure fairness. The system prompt was kept minimal, simply assigning the LLM the role of a coding assistant and asking for concise responses.

Results Summary

Unsurprisingly, GPT‑4 emerged as the overall winner, delivering the most accurate and comprehensive assistance across all tasks.

CodeLlama 70B and Mixtral 8x7B performed closely, matching GPT‑4 in several specific areas.

CodeLlama 7B ranked last overall but showed promise in certain tasks, especially when fine‑tuned prompts were applied; its small size makes it runnable on consumer‑grade hardware.

Sample Tasks

Full task lists, prompts, and outputs are available on GitHub ( https://github.com/rdentato/compare_coding_AI ).

FEN counting – testing model knowledge of Forsyth‑Edwards Notation for chess positions; only GPT‑4 produced completely correct code.

Guide compliance – none of the models detected all style‑guide violations, highlighting the need for better prompting or retrieval‑augmented generation.

Ambiguity analysis – Mixtral 8x7B outperformed CodeLlama 70B in recognizing conflicting requirements.

Conclusions

The evaluated LLM setups, combined with personal coding assistants, can alleviate common data and code‑privacy concerns associated with cloud‑based solutions. Cost considerations remain important: self‑hosting an LLM or the higher token price of GPT‑4 may affect adoption.

Overall, GPT‑4 stands out for its comprehensive support, yet smaller models can serve as viable alternatives depending on specific user needs and resource constraints.

Additional Note: Google Gemini Advance

During the preparation of this article, Google released Gemini Advance, a new LLM that shows significant improvements over the previous Bard model. A preliminary comparison of Gemini Advance against GPT‑4 across the eight categories shows very close scores, positioning Gemini as a strong contender for the “best large model” title.

Author: 万能的大雄 Reference: https://dev.to/rdentato/choose-your-own-coding-assistant-11gi