Open Source LLM Comparison 2026: Which is Best
Llama 3.3, Mistral, Qwen, Gemma 2 — open language models competing with GPT-4. We compare quality, speed, and requirements.
Open Source LLM Comparison 2026
The open-source language model world has changed radically. What was only possible with GPT-4 is now available for free on your own server. This comparison puts the numbers in front of you.
Why a Local Open-Source LLM?
GPT-4o via API:
├── $0.005/1K token input
├── $0.015/1K token output
├── Data goes to OpenAI
└── No control over model or settings
Local LLM (Llama 3 70B):
├── Free after setup
├── Data stays on your server
├── Full control over settings
└── Works without internet
Comprehensive Comparison (2026)
Llama 3 (Meta)
The strongest in the open-source model category in 2024-2025.
| Version | Parameters | Use Case | VRAM |
|---|---|---|---|
| Llama 3 8B | 8 billion | Light applications | 4-6 GB |
| Llama 3 70B | 70 billion | Professional applications | 35-40 GB |
| Llama 3.1 405B | 405 billion | Enterprise | 200+ GB |
# Run Llama 3 8B via Ollama
import ollama
response = ollama.chat(
model='llama3',
messages=[{'role': 'user', 'content': 'Write an executive summary for Q1 performance'}]
)
print(response['message']['content'])
Mistral (Mistral AI)
European origin, Apache 2.0 license (free commercial use).
| Version | Parameters | Notes |
|---|---|---|
| Mistral 7B | 7 billion | Very fast, good for production |
| Mixtral 8x7B | 46.7B (MoE) | Higher quality at Mistral speed |
| Mistral Large | 123 billion | Direct GPT-4 competitor |
DeepSeek
The Chinese model that shocked the world in December 2024.
DeepSeek R1:
├── Quality matching OpenAI o1 in math and code
├── Fully open source
├── Training cost: under $6M vs hundreds of millions for OpenAI
└── Best for analytical tasks and code problems
Qwen (Alibaba)
Unexpectedly strong in Arabic.
Qwen2.5 72B:
├── Best Arabic among open-source models
├── Outstanding code performance
├── Long context support (128K tokens)
└── Suitable for Arabic companies
Objective Comparison
Task: Translate technical document (5,000 words) English to Arabic
Model | Quality | Technical Accuracy | Time (8vCPU)
────────────────────────────────────────────────────────────────
Qwen2.5 72B (Q4) | 5/5 | 5/5 | ~8 min
Llama 3 70B (Q4) | 4/5 | 4/5 | ~10 min
DeepSeek R1 70B (Q4) | 4/5 | 5/5 | ~12 min
Mixtral 8x7B (Q4) | 3/5 | 4/5 | ~5 min
Llama 3 8B (Q4) | 3/5 | 3/5 | ~1 min
Server Setup
# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh
systemctl enable ollama
systemctl start ollama
# Download models
ollama pull llama3 # 4.7 GB
ollama pull qwen2.5:72b-q4 # 43 GB
ollama pull deepseek-r1:8b # 4.9 GB
Open WebUI Interface
docker run -d \
-p 3000:8080 \
--add-host=host.docker.internal:host-gateway \
-v open-webui:/app/backend/data \
--name open-webui \
--restart always \
ghcr.io/open-webui/open-webui:main
This is exactly what is automatically installed in the "Open WebUI + Ollama" AI Hosting template on كلاود (cloud.net.sa).
Recommendation for Saudi Companies
| Use Case | Recommended Model | Server |
|---|---|---|
| Email and general writing | Llama 3 8B | Business (4 vCPU) |
| Arabic/English translation | Qwen2.5 72B | Enterprise + extra RAM |
| Legal document analysis | DeepSeek R1 8B | Business |
| Code writing | DeepSeek R1 / Qwen2.5 Coder | Business |
| General company application | Mixtral 8x7B | Enterprise |
Conclusion
The open-source model landscape changes monthly. Choose Qwen2.5 for Arabic, DeepSeek R1 for analysis and code, and Llama 3 for general purposes. Start with small models (8B) for experimentation, and move to larger ones when higher quality is needed.
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