# Doubao

Professional Version Positioning: Aimed at high-precision requirements in dialogue and content generation, typically featuring larger parameter scales (such as 1.5B level) and more in-depth optimization.

High-Quality Output: Emphasizes accuracy and rich details, suitable for enterprises, professional applications, and scenarios requiring rigorous language expression.

Extended Context and Language Understanding: Thoroughly trained to excel in handling complex contexts, domain-specific terminology, and nuanced language, particularly demonstrating clear advantages in Chinese dialogue.

Lightweight Design: Utilizes fewer parameters and computational resources, optimizing model size for faster response speeds, more suitable for deployment on resource-constrained devices.

Balanced Performance and Efficiency: While maintaining basic language understanding and generation capabilities, further reduces latency and operational costs, suitable for mobile devices or large-scale real-time services.

Portable Applications: Though lightweight, still capable of handling daily Q&A and ordinary conversations, sufficient for scenarios that do not require professional-level in-depth output.

Flagship Large Model: Employs a Mixture of Experts (MoE) architecture with a total of 671B parameters, of which approximately 37B parameters are activated during each inference, supporting efficient computation.

Long Context Support: Features an extremely long context window (up to 128K tokens), suitable for processing long documents, complex conversations, as well as code and mathematical tasks.

Cost-effectiveness and Openness: Offers advantages in cost and training efficiency while providing open-source code convenient for research and enterprise applications, suitable for a wide range of language understanding and generation tasks.

Focus on Reasoning Ability: Champions a "reasoning-first" approach, generating Chain-of-Thought through reinforcement learning, showcasing detailed reasoning processes.

High-precision Logical Reasoning: Performs exceptionally well on mathematical problems, code generation, and complex logical questions, comparable to top reasoning models (such as OpenAI's o1).

Transparent Thinking Process: Users can see the model's thought process and self-verification before generating final answers, aiding in understanding and debugging model outputs, suitable for applications requiring result explainability.

Distilled Version: Employs distillation techniques to refine DeepSeek-R1's advanced reasoning capabilities into a smaller model based on the Qwen architecture, maintaining excellent performance with fewer parameters.

Efficient Reasoning and Low Resource Usage: Designed to balance reasoning effectiveness and operational efficiency, capable of completing complex mathematical and programming tasks while being suitable for deployment in resource-constrained environments.

Enhanced Practicality: Allows developers to obtain near-R1 level reasoning capabilities at lower cost, very suitable for business applications requiring high responsiveness and cost sensitivity.