Best Open Source AI Models in 2025: Llama, Mistral, and Qwen Compared

The open source model ecosystem has matured enormously over the last two years. Today it's perfectly viable to build production applications with models you can run on your own infrastructure, without depending on an external API or paying per token.

Llama (Meta), Mistral (Mistral AI), and Qwen (Alibaba) are the three most widely used open source models today. This guide explains what each offers, how to run them, and when it makes sense to choose them over GPT-4o or Claude.

Why Consider Open Source Models

Before diving into the comparison, it's worth being honest about when open source models make sense and when they don't.

They make sense when:

  • Data privacy is critical and you can't send data to an external API
  • Usage volume is high and per-token costs of commercial models are prohibitive
  • You need to customize the model with fine-tuning on your own data
  • You want full control over infrastructure with no third-party dependency

They don't make sense when:

  • You're prototyping and development speed matters more than cost
  • You don't have GPU infrastructure available
  • The commercial model quality is significantly better for your specific use case

With that clear, let's look at the three main models.

Llama 3 (Meta)

Llama is Meta's family of open source models. Llama 3, released in 2024, marked a significant quality leap over previous versions and directly competes with mid-range commercial models.

The Llama 3 family includes several sizes:

  • Llama 3.2 1B and 3B: very small models designed for resource-constrained devices or edge computing
  • Llama 3.1 8B: the most popular entry point, a good balance between quality and hardware requirements
  • Llama 3.1 70B: quality close to commercial models, requires significant hardware
  • Llama 3.1 405B: Meta's largest model, comparable to GPT-4 on many tasks

You can download the weights directly from huggingface.co/meta-llama after accepting the usage license. The license allows commercial use with some restrictions — read it carefully if your project is commercial.

Llama 3 strengths:

  • Enormous community: thousands of fine-tunes, adaptations, and tools built on top of it
  • Very good English performance for its size
  • Wide support in frameworks like Ollama, llama.cpp, and vLLM
  • The de facto reference for comparing other open source models

Limitations:

  • Performance in languages other than English is inferior to Qwen
  • Large models (70B+) require serious GPU hardware

Mistral (Mistral AI)

Mistral AI is a French company that has published several high-quality open source models with permissive licenses. Their philosophy is to release smaller but highly efficient models.

The most relevant models currently:

  • Mistral 7B: the model that put Mistral on the map. Outperforms Llama 2 13B on many tasks with half the parameters. Apache 2.0 license — completely free for commercial use.
  • Mixtral 8x7B: Mixture of Experts (MoE) architecture. Has 47B total parameters but activates only 13B per token, making it far more efficient than a dense model of the same size.
  • Mistral Small and Mistral Large: more recent models available both via API and in partial open source versions.

You can access the models at huggingface.co/mistralai.

Mistral strengths:

  • Exceptional efficiency: very good quality for its size
  • Apache 2.0 license on main models — no commercial restrictions
  • Mixtral 8x7B offers large-model quality at medium-model inference cost
  • Strong performance on code and reasoning

Limitations:

  • Smaller community than Llama
  • The most capable models (Mistral Large) are not fully open source

Qwen (Alibaba)

Qwen is Alibaba's model family, and it's probably the most underutilized in the Western world despite offering very competitive performance. Qwen2.5, released in late 2024, surprised with results comparable to much larger models.

The Qwen2.5 family includes:

  • Qwen2.5 0.5B, 1.5B, 3B: very small models for resource-limited devices
  • Qwen2.5 7B: competitive with Llama 3.1 8B on most benchmarks
  • Qwen2.5 14B and 32B: excellent quality-to-size ratio
  • Qwen2.5 72B: one of the best open source models currently available
  • Qwen2.5-Coder: a code-specialized variant, very competitive with GitHub Copilot on specific tasks

You can access them at huggingface.co/Qwen.

Qwen strengths:

  • Far superior multilingual performance — especially in Chinese, but also Spanish, French, and other languages
  • Qwen2.5-Coder is one of the best open source options for code generation
  • 128K token context on main models
  • Apache 2.0 license on most models

Limitations:

  • Documentation is primarily in Chinese for more advanced resources
  • Smaller Western community
  • Some models have restrictions on use in military or surveillance applications

How to Run Them Locally with Ollama

Ollama is the simplest way to run open source models locally. It works on Mac (with Apple Silicon), Linux, and Windows.

Installation:

# Mac and Linux
curl -fsSL https://ollama.com/install.sh | sh

# Windows: download the installer from ollama.com

Running models:

# Llama 3.1 8B
ollama run llama3.1

# Mistral 7B
ollama run mistral

# Qwen2.5 7B
ollama run qwen2.5

# Qwen2.5-Coder 7B
ollama run qwen2.5-coder

Ollama downloads the model automatically on first run and exposes a local API compatible with the OpenAI API at http://localhost:11434. This means you can use the OpenAI Python library pointing to your local server:

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:11434/v1",
    api_key="ollama"  # Can be any string
)

response = client.chat.completions.create(
    model="llama3.1",
    messages=[{"role": "user", "content": "Explain what RAG is in 3 sentences."}]
)

print(response.choices[0].message.content)

Hardware Requirements

Required hardware depends on model size. As an approximate reference for acceptable performance:

7B-8B models: 8 GB RAM (CPU) or 6 GB VRAM (GPU). Runs on a MacBook with Apple Silicon or a mid-range GPU like an RTX 3060.

13B-14B models: 16 GB RAM or 10 GB VRAM.

32B models: 32 GB RAM or 20 GB VRAM. Requires dedicated hardware.

70B+ models: multiple GPUs or servers with large RAM. Not practical on consumer hardware.

If you don't have a local GPU, you can run open source models in the cloud with services like Together AI, Replicate, or Groq, which offer open source model inference per token at prices below commercial models.

Summary: Which One to Choose

Choose Llama 3.1 if you want the model with the most community resources, the most fine-tunes available, and the widest ecosystem. It's the reference standard of the open source ecosystem.

Choose Mistral if efficiency is the priority — you need the best possible model with the least hardware. Mixtral 8x7B in particular offers a quality-to-inference-cost ratio that's hard to beat.

Choose Qwen2.5 if you work in languages other than English, need a specialized code model, or want the best absolute performance in the 7B-32B parameter range.

Public Benchmarks for Reference

To compare open source models objectively, the most widely used resources are:

  • Open LLM Leaderboard by Hugging Face: the most comprehensive ranking of open source models with multiple standardized benchmarks
  • LMSYS Chatbot Arena: human preference evaluation through direct model comparisons
  • EvalPlus: a benchmark specifically for code generation

These resources are updated continuously and are more reliable than benchmarks published by the model creators themselves.