DeepSeek's 552B MoE activates 8B params on prefill and stores 890 bytes of KV cache per token. What the card says and how to start running it.
deepseek-ai/DeepSeek-V4.1-Flash
Google DeepMind's largest dense Gemma 4 model handles images, video frames and 256K-token context. Here's how to run it with Transformers.
google/gemma-4-31B-it
Z.ai's first natively multimodal GLM-5 model: 320B total / 18B active parameters, hybrid sparse-linear attention, MIT license. What the card covers.
zai-org/GLM-5.3-Flash
A 27B vision decision model that returns a calibrated probability for every option in one forward pass. Here's how to serve it and use it.
autotrust/JEV-27B-VL
Kimi K3 is a 2.8T-parameter open-weight multimodal MoE with a 1M-token context. What the model card says and how to call it.
moonshotai/Kimi-K3
Xiaomi's 1.02T-parameter MoE model has 1M context and omnimodal input, and was trained for agents. Here is how to serve it with vLLM or SGLang.
XiaomiMiMo/MiMo-V2.6-Pro-RL
MiniMax H3 is an open-weight model that generates video and stereo audio together. Here's how to serve it with SGLang and where the hosted APIs fit in.
MiniMaxAI/MiniMax-H3
Qwen's driving model adds a BEV perception head and a flow-matching planner to Qwen3.5-4B. Install it and sample ego trajectories from demo scenes.
Qwen/Qwen-Drive-1.0-4B
TeleOCR is a 1.2B Apache-2.0 model that turns text, tables, formulas and layouts into structured output, including from photos of warped pages.
XingChen-AGI/TeleOCR
Clef answers typed questions about any input (text, JSON, images) with a probability per option in one forward pass. Here's how to use it for routing, triage and classification.
Cloudflare/clef
Google's new 740M multimodal embedding model runs on a CPU. Here's how to build a working semantic search over your own documents, and the prefixes that make it accurate.
google/embeddinggemma-2