Open-source LLM Roundup 2025 - Llama 3, Mistral, Gemma

Introduction

In recent years, the development of open-source Large Language Models (LLMs) has revolutionized AI research and practice. As of 2025, Llama 3, Mistral, and Gemma have established themselves as leaders among these open-source LLMs. In this blog post, we will thoroughly analyze the features, usage, and pros and cons of each model to help readers choose the most suitable model for their projects.

Basic Concepts Explained

Open-source LLMs are language models that are free to use and can be modified and distributed by anyone. These models can be utilized in various fields such as natural language processing, coding, and multilingual translation. Notable models for 2025 include:

  • Llama 3: A model developed by Meta, strong in reasoning and coding, and excellent in multilingual processing.
  • Mistral: A model with excellent speed and efficiency, suitable for real-time text and code tasks.
  • Gemma: A lightweight model developed by Google, effective for general knowledge and reasoning tasks.

Practical Usage/Setup Methods

All three models are provided under the Apache 2.0 license and can be easily accessed and used.

  • Llama 3: Available for download via Hugging Face or Torchtune, with multimodal support allowing integrated use of images and text.
    • 
pip install torch transformers
model = AutoModel.from_pretrained("meta-llama/Llama-3.1-8B")
  • Mistral: Can be used on the Mistral AI platform or Hugging Face, supporting low-latency inference via GQA.
  • Gemma: Can be easily run on Google Colab or a single GPU, suitable for small-scale projects.
    • 
model = AutoModel.from_pretrained("google/gemma-2-27b")

Practical Application Examples

Each model has various practical application cases:

  • Llama 3: Suitable for complex coding tasks or mathematical problem-solving, and can be utilized in multilingual translation projects.
  • Mistral: Ideal for projects requiring fast text generation or where real-time responses in mobile environments are crucial.
  • Gemma: Suitable for knowledge-based answer generation or efficient inference tasks in lightweight applications.

Pros, Cons, and Alternatives Comparison

The pros and cons of each model are as follows:

  • Llama 3 Pros: Shows high performance in various tasks and provides deep understanding with vast data.
  • Llama 3 Cons: Large models require high-performance hardware.
  • Mistral Pros: Excellent speed and memory efficiency, suitable for real-time tasks.
  • Mistral Cons: Has weaknesses in mathematical problem-solving.
  • Gemma Pros: Lightweight, highly accessible, and can be deployed at low cost.
  • Gemma Cons: Performance may be lower compared to other models for complex code tasks.

It is important to choose the appropriate model based on the characteristics of your project. If complex coding or multilingual processing is required, we recommend Llama 3; if speed is important, Mistral; and for lightweight tasks, Gemma.

Conclusion and Recommendation

The advancement of open-source LLMs enhances AI accessibility and provides innovative solutions across various fields. It is crucial to understand the characteristics and pros and cons of each model and choose according to your project's requirements. These models are expected to continue evolving beyond 2025, and there is great anticipation for new versions like the upcoming Llama 4.