Open source LLMs have reached quality levels that rival commercial models for many tasks. Top models 2025: 1) Llama 3.1 405B: Meta flagship, matches GPT-4o on most benchmarks, 405B full model needs 8xA100, but quantized 4-bit fits on 2xA100 (commercial use allowed). 2) Llama 3.1 70B: best performance/size trade-off, fits on 2xRTX 3090 with Q4, widely used for production. 3) Mistral Large 2 (123B): European lab, fully open weights (commercial OK), strong multilingual. 4) Microsoft Phi-3.5 (3.8B): small but surprisingly capable for reasoning tasks, runs on consumer GPU. 5) Google Gemma 2 27B: excellent quality for size, optimized for consumer GPU inference. License comparison: Llama 3 (Meta License - commercial OK except >700M MAU apps), Mistral (Apache 2.0 - fully open), Phi (MIT - fully open), Gemma (Google Gemma Terms - commercial OK). Deployment: Ollama for local development (automatic GGUF download and serving). vLLM for production serving. Together.ai, Replicate, Groq for managed inference. Performance benchmarks (MMLU 5-shot): GPT-4o 88.7%, Llama 3.1 405B 88.6%, Mistral Large 2 84.0%, Llama 3.1 70B 82.0%, Phi-3.5 78.9%, Gemma 2 27B 75.2%. Selection guide: production quality = Llama 3.1 70B/405B. Edge/mobile = Phi-3.5 or Gemma 2 9B. Multilingual = Mistral. Privacy-first local = Ollama + Llama 3.1 8B.