Model families

Cohere's enterprise-oriented model family. Command R+ targets retrieval-augmented generation workflows.

Hangzhou-based lab known for highly efficient MoE training. DeepSeek V3 and R1 set new bars for open reasoning and coding.

Technology Innovation Institute (UAE). Falcon Mamba pioneered state-space-model open releases.

Google's open-weights model family derived from the same research as Gemini. Strong performance at small scales.

xAI's open-weights releases. Grok 1 and Grok 2 weights have been published under Apache 2.0.

Hybrid Mamba/Transformer architecture with very long native context windows.

Meta's open-weights LLM family. Llama 3 introduced 128K context and strong instruction tuning across 1B–405B parameter scales.

Paris-based lab whose dense and mixture-of-experts open-weights models popularised sliding-window attention and high efficiency.

NVIDIA's research and instruction-tuning effort built on top of Llama base models, with strong RLHF and reward-modelling work.

AI2's fully-open initiative: weights, data and training code all under permissive licences.

Microsoft's small-language-model line, focused on data-quality-driven training. Phi-4 punches well above its weight class.

Alibaba's Qwen series. Qwen2.5 delivers competitive frontier-class performance across sizes from 0.5B to 72B with permissive licensing.

Stability AI's general-purpose text models.

01.AI's bilingual (Chinese/English) open-weights series including vision variants.