Models

An encoder-only transformer that learns bidirectional text representations through masked language modeling pretraining.

A dual-encoder multimodal model that learns to align text and images in one shared embedding space.

Open omnimodal world model family from NVIDIA for physical AI, combining autoregressive reasoning with diffusion-based generation across text, image, video, audio, and action outputs through Nano and Super checkpoints.

A lighter DeepSeek-V4 serving profile that keeps the same conceptual stack while prioritizing faster deployment paths.

The larger DeepSeek-V4 reasoning model, built around long-context attention, MoE routing, and coupled post-training and serving choices.

A Black Forest Labs image-generation model family that turns text prompts into images using rectified-flow transformer backbones in latent space.

Open model family from Google DeepMind with Gemma 4 as the current main line, spanning Effective 2B and 4B edge checkpoints through 12B, 26B mixture-of-experts, and 31B dense variants with native text, image, and audio input, thinking-mode reasoning, and Apache 2.0 licensing.

A large open-weights GLM model that pairs sparse-attention and mixture-of-experts routing with an agentic-engineering training story.

A later GLM line member that extends sparse-attention mixture-of-experts architecture with a solid 1M-token context and long-horizon coding emphasis.

A large decoder-only language model that made in-context prompting and few-shot text generation a mainstream reference point.

Meta's open-weight Llama-family decoder-only transformer release, spanning dense and instruction-tuned checkpoints with long-context support.

An open-weight diffusion-transformer model that generates synchronized video and audio from text, image, and audio conditioning.

A larger open-weights Mixtral sparse mixture-of-experts model with 141 billion total parameters, about 39 billion active per token, and a 64,000-token context window under Apache 2.0.

The first open-weights Mixtral sparse mixture-of-experts model with 46.7 billion total parameters, about 12.9 billion active per token, and a 32,768-token context window under Apache 2.0.

Open-weights 120B-total / 12B-active hybrid Mamba-Attention Mixture-of-Experts text model from NVIDIA's Nemotron 3 family, with long-context support up to 1M tokens and NVFP4/BF16 deployment variants.

The smallest dense Qwen3 open-weight checkpoint: a 0.6-billion-parameter causal language model with 32,768-token context, Apache 2.0 licensing, and post-training for reasoning and instruction following.

The smallest published Qwen3.5 open-weights checkpoint with multimodal inputs, a hybrid decoder stack, and a 262,144-token native context window.

The dense Qwen 3.6 open-weights model with a 27-billion-parameter stack, multimodal inputs, and a 262,144-token native context window.

The sparse-expert Qwen 3.6 open-weights model with 35 billion total parameters, about 3 billion active per token, multimodal inputs, and a 262,144-token native context window.

An encoder-decoder transformer from Google Research that reformulates every NLP task as reading text and writing text in a shared token space.