Open Source AI Models 2026: Llama 3 vs Mistral vs DeepSeek

You don't have to pay Anthropic or OpenAI to use powerful AI. In 2026, open source AI models have reached a level where the best free models match or beat closed models from 2023. Llama 3, Mistral, and DeepSeek are the three most important open source LLMs you need to know about.

This guide compares them on speed, accuracy, cost, and best use cases — so you can pick the right model for your project without spending hours on research.

What is an Open Source AI Model?

When you use ChatGPT or Claude, you're sending your data to a company's server and paying per token. With open source models, you download the model weights, run them on your own computer or GPU server, and pay only for compute — not per-query licensing.

Key benefits of open source models:

• No per-token API fees
• Full data privacy — data never leaves your server
• Customizable — fine-tune on your own data
• No usage limits or rate limits
• Works offline

Llama 3 — Meta's Flagship Open Source Model

Meta's Llama 3 family (released 2024, updated through 2026) is the most widely used open source LLM. The flagship Llama 3.3 70B model matches or beats GPT-3.5 Turbo on most benchmarks.

Llama 3 Model Sizes

Llama 3 strengths: Long context (128K tokens), multilingual support, strong coding, best-in-class open source benchmark scores.

Llama 3 weakness: Larger models require significant GPU RAM to run locally.

Mistral — The Efficiency Champion

Mistral AI is a French startup that proved smaller models can punch far above their weight. Mistral 7B — at just 7 billion parameters — outperforms Llama 2 13B on most tasks.

Mistral Model Lineup

Mistral strengths: Fastest inference per dollar, excellent at code with Codestral, efficient architecture (MoE) means lower compute for high quality.

Mistral weakness: Smaller base models lag behind Llama 3.3 70B on complex reasoning.

DeepSeek — The Cost Disruptor

DeepSeek shocked the AI world in January 2025. Their DeepSeek V3 model matched GPT-4o performance at a claimed training cost of under $6 million — 50x cheaper than comparable models. DeepSeek R1 is their reasoning-focused model, designed for math, science, and complex logic.

DeepSeek strengths: Exceptional coding and reasoning, extremely cheap API pricing (around $0.27 per 1M input tokens via API), fully open source weights.

DeepSeek weakness: Trained in China — data privacy concerns for sensitive enterprise use cases. May have content restrictions on certain topics.

Full Comparison Table

How to Run Open Source Models Locally (Ollama)

Ollama is the easiest way to run open source models locally. Install it and run any model in two commands.

# Install Ollama (Mac/Linux)
curl -fsSL https://ollama.ai/install.sh | sh

# Windows: download from https://ollama.ai/download

# Pull and run Llama 3.1 8B (needs 8GB RAM)
ollama pull llama3.1
ollama run llama3.1

# Run Mistral 7B (needs 8GB RAM)
ollama pull mistral
ollama run mistral

# Run DeepSeek R1 7B distill (needs 8GB RAM)
ollama pull deepseek-r1:7b
ollama run deepseek-r1:7b

# Use via API (compatible with OpenAI format)
# Start server: ollama serve
# Then call: http://localhost:11434/api/chat

import requests

# Call local Ollama model via API
response = requests.post("http://localhost:11434/api/chat", json={
    "model": "llama3.1",
    "messages": [{"role": "user", "content": "Explain RAG in simple terms."}],
    "stream": False
})

print(response.json()["message"]["content"])

Run via API — No GPU Needed

Don't have a powerful GPU? Use these providers to run open source models via API:

Which One Should You Use?

Once you have a model running, pair it with a RAG system. See our RAG vs Fine-Tuning guide to understand when and how to add your own documents to any of these models.