What “best” means for open models
There is no single best open-source LLM, because the right answer depends on your constraint. If you are bounded by hardware, the smallest capable model wins. If you are bounded by licence, the most permissive wins. If you are bounded by raw quality, the largest well-trained model wins. The four families below — Meta’s Llama 3, Mistral, Microsoft’s Phi, and Alibaba’s Qwen — cover the spectrum, and choosing well means matching a model to your real bottleneck rather than chasing leaderboard rank.
Llama 3: the ecosystem default
Meta’s Llama 3 family, spanning roughly 8B to 70B+ parameters, is the de facto standard for open deployment. Its advantage is not always being top of every benchmark but having the deepest ecosystem: the widest tooling support, the most fine-tuning recipes, the most community variants, and the most documentation. The 8B model is an excellent local workhorse, while the 70B competes with mid-tier closed models on reasoning. The catch is the licence — a custom community licence that is free for nearly everyone but carries conditions for the very largest deployments.
Mistral and Qwen: permissive and multilingual
Mistral built its reputation on punching above its weight, delivering strong quality at small sizes under the genuinely permissive Apache 2.0 licence, which makes it the easy pick when commercial licence clarity matters most. Qwen, from Alibaba, has become a benchmark leader among open models and is notably strong on multilingual tasks, maths, and coding, with many releases also under Apache 2.0. Between them, these two cover most enterprises that want top open quality without licence friction.
Phi: small, efficient, and capable
Microsoft’s Phi family takes a different bet: small models trained on carefully curated, high-quality “textbook” data that perform far beyond what their parameter count suggests. Phi models are the best choice when your real constraint is hardware — edge devices, laptops, or cheap inference at scale — because they deliver useful reasoning in a footprint that fits where larger models cannot. They are not meant to top general leaderboards; they are meant to be the most capable model that runs on modest hardware.
How to pick and run one
Start from your bottleneck. For local experimentation on a laptop, take a quantised Llama 3 8B or a Phi model and run it through Ollama or LM Studio in minutes. For commercial deployment where licence terms are non-negotiable, prefer Apache 2.0 models like Mistral or Qwen. For the strongest open quality where you have GPU budget, reach for a 70B+ Llama or a large Qwen. Whatever you choose, benchmark it on your own tasks rather than trusting public scores, quantise to fit your hardware, and read the exact licence for the version you deploy.