Llama 3 (8B) vs Mistral v0.2: Best Open-Source Model for Local Inference in 2026?

Pros

• Top benchmark scores in the 7–8B class (MMLU ~66%)
• Broad community support — thousands of GGUF/GPTQ quants
• Meta ecosystem: LlamaIndex, llama.cpp, Ollama
• Instruction-tuned variant (Llama-3-8B-Instruct) ready out-of-box
• Actively maintained with regular model updates

Cons

• Meta Llama Community License — derivatives must comply (cannot use if monthly active users > 700M)
• 8K base context window (extensions available but unofficial)
• Slightly higher VRAM than Mistral: ~5.5GB in 4-bit
• Fine-tuning data less straightforward to reproduce than Apache models

Pros

• Apache 2.0 — fully commercial, no derivative restrictions
• 32K sliding window context vs Llama 3's 8K base
• Lower VRAM: ~4.1GB in 4-bit quantization
• Excellent instruction following with minimal system prompts
• Widely supported in llama.cpp, Ollama, vLLM, HuggingFace TGI

Cons

• Slightly below Llama 3 on MMLU and HumanEval benchmarks
• Smaller model family — fewer size variants to choose from
• Mistral AI pivoting to proprietary models; community momentum slowing
• No official instruction fine-tune best practices