Model families
Browse open-source models grouped by the team that released them.
Cohere's enterprise-oriented model family. Command R+ targets retrieval-augmented generation workflows.
Databricks' 132B mixture-of-experts (12 of 16 experts active per token) built on the MegaBlocks stack. Released under the Databricks Open Model Licence.
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.
Nous Research's community-driven fine-tune series. Hermes 3 sits on top of Llama 3.1 base weights, tuned for very strong tool use and steerable roleplay behaviour.
Hybrid Mamba/Transformer architecture with very long native context windows.
Beijing-based lab founded by ex-Tsinghua researchers. Kimi K1.5 and K2 push very long context and reasoning; K2 is a trillion-parameter MoE with strong tool-use behaviour.
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.