Chroma is an 8.9 billion-parameter model based on the FLUX.1-schnell architecture.
Qwen2.5-VL-7B is a powerful multimodal model with 7 billion parameters, delivering an optimal balance between high performance and efficiency. Designed for complex document analysis, video stream processing, and agent-based interaction tasks.
Qwen2.5-VL-3B - is a compact, 3-billion-parameter multimodal model designed for edge deployment, yet it delivers outstanding capabilities in image/video comprehension and agent-based task execution.
Qwen2.5-7B-1M is a compact yet powerful model with 7.6 billion parameters. Thanks to sparse attention technologies, it can process up to one million context tokens at excellent speeds. The model is an ideal solution for organizations requiring high-performance analysis of long documents while optimizing resource usage.
DeepSeek-R1-Distill-32B — a model built based on distilling a large MoE reasoning expert-level model, setting new records among open-source dense models. It is suitable for scientific, corporate, and educational platforms with high demands on logic and analysis.
DeepSeek-R1 is a unique reasoning model with 671 billion parameters, trained based on reinforcement learning (RL), supporting long chains of thought (CoT), and specializing in multi-step reasoning and logical analysis. It is indispensable for tasks requiring well-founded conclusions and transparent reasoning processes.
DeepSeek-R1-Distill-1.5B — a compact model that, thanks to distillation, possesses strong reasoning capabilities. It is ideal for fast text analysis in mobile and edge applications.
DeepSeek-V3 is a powerful MoE model with 671 billion parameters and 16 experts, one of the most popular open-source alternatives capable of competing with commercial analogs. With 128K tokens of context and high generation accuracy, it is ideal for professional tasks - from analyzing complex data to creating high-quality creative content.
Phi-4 is Microsoft's flagship compact model with 14 billion parameters, designed with a focus on efficiency within a limited context window of 16K tokens. It is optimized for tasks where fast response speed and accuracy are critical in short interactions.
Llama-3.3-70B is a language model supporting 8 languages, featuring a large context window (128k tokens) and high accuracy, making it ideal for assistant and dialogue systems. According to the developers, its performance is on par with Llama 3.1 with 405 billion parameters.
FLUX.1 Kontext [dev] is a 12 billion parameter rectified flow transformer capable of editing images based on text instructions.
FLUX.1 Fill [dev] is a 12 billion parameter rectified flow transformer capable of filling areas in existing images based on a text description.
FLUX.1 Depth [dev] is a 12 billion parameter rectified flow transformer capable of generating an image based on a text description while following the structure of a given input image.
FLUX.1 Canny [dev] is 12 billion parameter rectified flow transformer capable of generating an image based on a text description while following the structure of a given input image.
Shuttle 3 Diffusion is a text-to-image generation model designed to create detailed and diverse images in just four steps. It offers enhanced image quality, understanding of complex prompts, efficient resource usage, and increased detail.
CogVideoX1.5-5B is an open-source text-to-video generation model analogous to the commercial model QingYing. It is designed to create video based on text prompts, supports the English language, and also offers image-to-video generation (version CogVideoX1.5-5B-I2V). The model is available on platforms such as Hugging Face, ModelScope, and WiseModel.
Stable Diffusion 3.5 Medium is a Multimodal Diffusion Transformer with improvements (MMDiT-X) text-to-image model that features improved performance in image quality, typography, complex prompt understanding, and resource-efficiency.
Mochi-1 is a state-of-the-art open-source text-to-video generation model developed by Genmo. It achieves high-fidelity motion and strong prompt adherence in preliminary evaluations, significantly narrowing the gap between closed and open video generation systems.
Text-to-image model with Multimodal Diffusion Transformer with improvements (MMDiT-X) that features improved performance in image quality, typography, complex prompt understanding, and resource-efficiency.
A text-to-image model based on Multimodal Diffusion Transformer with improvements (MMDiT-X) that features improved performance in image quality, typography, complex prompt understanding, and resource-efficiency.