Model family

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.

A transformer-style block that updates noisy image or latent patch tokens during diffusion denoising, steered by timestep and optional conditioning signals.

A transformer architecture that applies one shared block repeatedly over many loop iterations instead of stacking many distinct layers.

A U-shaped convolutional network that denoises images by mixing multiscale context down a downsampling path, through a bottleneck, and back up an upsampling path with skip connections.

A generative training objective that teaches a model how samples should move from a simple or noisy starting state toward real data.

A learned compressed representation space where models store internal codes instead of raw pixels or token ids, then decode or denoise back to visible outputs.

Extra prompt-derived vectors that steer an image generator while the denoiser removes noise step by step.

Models that produce moving visual sequences by generating or refining frames—or visual tokens arranged over time—while keeping motion, identity, and appearance coherent from one moment to the next.

The 2017 paper that introduced the transformer, using self-attention as the main way tokens mix information in encoder-decoder sequence models.

The paper that introduced deep bidirectional transformer encoders trained with masked language modeling and fine-tuned for language understanding tasks.

A technical report that introduced GPT-2 as a large decoder-only language model trained with broad next-token pretraining and byte-level BPE tokenization.

The research paper behind Stable Diffusion-style image generation: compress images into a latent space, denoise there instead of in pixel space, then decode the result.

July 2026

A narrative arc from denoising diffusion probabilistic models and convolutional U-Net backbones through CLIP-conditioned image systems, latent diffusion, diffusion transformers, flow matching, and source-backed open video and world-model examples.

The pretraining-style regime where a denoiser learns to predict noise or a clean-direction signal from corrupted examples across a noise schedule.

A generative model family that learns to reverse a noise corruption process through many iterative denoising steps.

A model family that accepts or produces more than one data modality, such as text paired with images or audio.

A model family built from stacked attention and feed-forward blocks that mix information across token positions in parallel.

A model family that learns environment dynamics or state transitions so it can predict or simulate what happens next.