DeepSeek

A large reasoning-focused model with a 128,000-token context window, built on the Llama architecture. It specializes in logical analysis, problem-solving, and handling complex multi-step reasoning chains, particularly for technical and analytical tasks. DeepSeek R1 Llama 70B excels in applications requiring depth of analysis, such as research assistance, technical Q&A, and complex data interpretation.

A versatile reasoning-focused foundation model with a 128,000-token context window, designed for comprehensive analytical tasks. It offers strong logical reasoning, consistent output quality, and reliable performance across domains. DeepSeek R1 excels in application areas requiring careful analysis, knowledge synthesis, and complex reasoning, such as research, education, and specialized knowledge work.

A next-generation model from DeepSeek with a 64,000-token context window, featuring enhanced reasoning, multilingual capabilities, and improved instruction following. It delivers advanced performance across domains with particular strength in technical content, code generation, and complex problem-solving while maintaining efficient response generation and processing requirements.

A mid-sized DeepSeek reasoning model with a 128,000-token context window, based on the Qwen architecture. It provides strong logical reasoning and complex task completion with lower computational demands, making it ideal for efficient analytical applications like data interpretation, automated reasoning, and technical problem-solving.

An open-source Mistral model with a 32,000-token context window, optimized with NVIDIA NeMo for superior performance and deployment flexibility. It features enhanced efficiency and hardware acceleration, making it suitable for high-performance inference on NVIDIA platforms, ideal for developers and researchers needing customizable, efficient AI solutions.