DeepSeek R1

DeepSeek

DeepSeek R1 is a reasoning-focused language model from DeepSeek, optimized for complex tasks in mathematics, coding, and logic. It utilizes chain-of-thought reasoning to solve problems step-by-step and is available as an open-weight model.

DeepSeek

DeepSeek R1 is a reasoning-focused model from the R1 series, optimized for complex tasks requiring step-by-step thinking. It excels in mathematics, coding, and logical reasoning by leveraging advanced reinforcement learning and chain-of-thought methodologies.

DeepSeek

A distilled reasoning model from DeepSeek, based on the Qwen3 8B architecture. It is optimized for mathematical and code reasoning tasks, offering strong performance in a lightweight and efficient package.

DeepSeek

A distilled version of the DeepSeek R1 reasoning model, built on the Llama 70B architecture. It inherits strong reasoning and chain-of-thought capabilities from the R1 series while being optimized for efficiency. The model excels at complex problem-solving, code generation, and tasks requiring logical deduction.

DeepSeek

A distilled 8B parameter model from the DeepSeek R1 series, optimized for fast and efficient reasoning tasks. It inherits strong reasoning capabilities from larger R1 models while being lightweight and cost-effective.

DeepSeek

A distilled version of the DeepSeek R1 reasoning model, based on the Qwen 1.5B architecture. It inherits strong reasoning capabilities from the larger R1 model while being significantly smaller and faster, making it suitable for edge deployment and low-latency applications.

DeepSeek

A distilled version of the DeepSeek R1 reasoning model, built upon the Qwen 14B architecture. It aims to deliver strong reasoning and problem-solving capabilities in a more compact and efficient form factor.

DeepSeek

A distilled version of the DeepSeek R1 reasoning model, built on the Qwen 32B architecture. It inherits strong chain-of-thought reasoning capabilities from the larger R1 model while offering faster inference speeds and lower computational costs. This model is optimized for efficient deployment without sacrificing core reasoning performance.