An efficient MoE model with 80B parameters (3B active), specifically designed for programming-oriented agents. The model features highly efficient inference, an extended context length (262K tokens), and best-in-class handling of various tool call formats, making it a highly suitable choice for deploying intelligent developer assistants.
A foundation model designed for Image-to-Video-Audio (IT2VA) and Text-to-Video-Audio (T2VA) tasks, enabling simultaneous generation of high-fidelity video and synchronized audio. It addresses limitations of cascaded pipelines and proprietary systems by providing a fully open-source solution.
A foundation model designed for Image-to-Video-Audio (IT2VA) and Text-to-Video-Audio (T2VA) tasks, enabling simultaneous generation of high-fidelity video and synchronized audio. It addresses limitations of cascaded pipelines and proprietary systems by providing a fully open-source solution.
It is a native multimodal autoregressive model designed for image generation, supporting both text-to-image and image-to-image (TI2I) tasks. It features a unified architecture for multimodal understanding and generation, achieving performance comparable to leading closed-source models. The model includes two main variants: HunyuanImage-3.0 (text-to-image) and HunyuanImage-3.0-Instruct (enhanced with reasoning capabilities for intelligent prompt improvement and creative editing).
An innovative multimodal model for optical character recognition (OCR) that mimics human visual perception. Instead of standard line-by-line image scanning, its new DeepEncoder V2 uses a compact language model to dynamically reorder visual tokens, following the semantic logic of the document. This significantly improves the understanding of complex layouts, tables, and formulas while maintaining the high efficiency of the previous version.
This model is designed for image-to-video generation. It falls under the category of "World Model". The project is licensed under Apache-2.0, ensuring open access to the code and models.
This is the base model of the ⚡️-Image family, designed for high-quality image generation, broad style coverage, and precise alignment with text prompts. It is intended for professional use, creative tasks, and research, in contrast to the accelerated version Z-Image-Turbo.
A 30-billion parameter MoE model with only ~3.6B parameters activated per token, delivering record-breaking performance in its class with minimal resource requirements (~24 GB VRAM). The model leads in agent-based tasks and programming, supports a 200K context, and is optimized for easy local deployment.
It is a 4 billion parameter rectified flow transformer model designed for fast image generation and editing. It unifies text-to-image generation and multi-reference image editing into a single compact architecture, enabling end-to-end inference in under a second. Optimized for real-time applications without compromising quality, it runs on consumer-grade GPUs such as NVIDIA RTX 3090/4070 with approximately 13GB VRAM.
It is a 9 billion parameter rectified flow transformer model designed for high-speed image generation and editing. It unifies text-to-image generation and multi-reference image editing into a single compact architecture, achieving state-of-the-art quality with end-to-end inference in under half a second. The model leverages an 8 billion parameter Qwen3 text embedder and is step-distilled to 4 inference steps, enabling real-time performance while matching or exceeding the quality of models five times its size.
It is a text-to-image and image-to-image generation model employing a hybrid architecture combining an autoregressive generator and a diffusion decoder. It excels in generating high-fidelity images with precise text rendering and semantic understanding, particularly in complex, information-dense scenarios.
an audio-visual base model based on the DiT architecture, developed for synchronized generation of video and audio within a single model. It incorporates key components of modern video generation systems, including open weights and optimization for local use.
An open-source model built on a Mixture-of-Experts architecture with 1 trillion parameters, of which 32 billion are activated per token. The developers have implemented a "visual agentic intelligence" paradigm within it—a combination of visual perception, reasoning, and autonomous agents. The model is multimodal, presented in native INT4 quantization, and includes a unique Agent Swarm mechanism that orchestrates and enables the parallel operation of up to 100 sub-agents. This improves quality and reduces the execution time for complex tasks by an average factor of 4.5.
It is the December 2025 update to Qwen-Image, a text-to-image foundational model. It is designed to generate high-quality images from textual prompts with enhanced capabilities in realism, detail rendering, and text integration.
The model for text-to-image generation represents an improved version of the previous NextStep-1 model. It was developed to enhance image quality and address visualization issues inherent in earlier versions.
An advanced MoE model with agentic capabilities, created as an intelligent partner for programming. Its uniqueness lies in its multi-level "thinking" system, which delivers unprecedented stability and control when tackling complex tasks. The ideal choice for development, automation, and programmatic visual content creation.
An evolutionary update of the M2 model, significantly enhancing its coding capabilities. Through massive reinforcement learning, a full attention mechanism, and a new approach to performance evaluation on real-world tasks (VIBE Bench), M2.1 demonstrates deep code understanding and achieves SOTA for AI agents tackling real-world engineering tasks.
It is an enhanced image-to-image generation model, succeeding Qwen-Image-Edit-2509.
A model from NVIDIA with 31.6B parameters (3.6B active), specifically optimized for high-performance agentic systems. The model combines a hybrid Mamba-Transformer MoE architecture, delivering simultaneous memory efficiency, high throughput, and reasoning accuracy on contexts up to 1M tokens.
A multimodal model with 106B parameters, using a Mixture-of-Experts (MoE) architecture and a 128K token context. Its key feature is native tool-calling support, enabling it to directly work with images as both input and output, making it an ideal platform for building complex AI agents for document analysis, visual search, and front-end development automation.