Gemma 3 27B — flagship multimodal model from Google DeepMind with 27 billion parameters and maximum performance. It is easy to fine-tune and ideal for a wide range of complex research tasks and high-end enterprise solutions.
Gemma 3 12B is a high-performance multimodal model with 12 billion parameters, a context window of 128K tokens, and multilingual understanding, designed for a wide range of straightforward tasks. It excels at processing long documents, images, and technical content.
Gemma 3 4B is a compact model that is also multimodal, featuring a context window of 128K tokens and built-in support for more than 35 languages, including Russian. It's an excellent solution for embedded systems and applications processing text and images with limited computational resources.
Gemma 3 1B — an ultra-compact model with just 1 billion parameters, yet retaining impressive capabilities. It supports a context window of 32K tokens and is ideal for resource-constrained devices and tasks where response speed is critical.
QwQ is a model with 32.5 billion parameters and a context length of 131K tokens, specifically designed for deep reasoning and logical analysis. Its unique ability to perform transparent and structured thinking sets it apart from competitors, delivering high-quality and well-thought-out responses.
Phi-4-multimodal is an efficient solution for multimodal tasks with edge deployment support, combining a compact size (5.6B parameters) with the capabilities of large language models. The model is ideal for developing applications with synchronous processing of speech, images, and text on resource-constrained devices.
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 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-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-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 Fill [dev] is a 12 billion parameter rectified flow transformer capable of filling areas in existing images based on a text description.
FLUX.1 Kontext [dev] is a 12 billion parameter rectified flow transformer capable of editing images based on text instructions.
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.