DeepSeek-R1 is a specialized reasoning model built for complex multi-step logic, mathematical problem-solving, and analytical tasks.
DeepSeek-V4, including its Pro and Flash versions, is a general-purpose flagship model optimized for speed, agent-based workflows, and supports a massive 256K token context window.

DeepSeek follows an open-weight model. This means the model parameters are publicly available for download and local hosting, often under permissive licenses like MIT.
However, the full training data and training pipeline remain proprietary.

DeepSeek V4-Pro is the flagship model with approximately 1.6 trillion parameters, designed for maximum performance and advanced tasks.
V4-Flash is a lighter version with around 284 billion parameters, optimized for faster responses and lower cost, making it ideal for high-frequency AI agent use.

When using the official web platform or API, your data is processed in DeepSeek’s data centers in China.
If you require full data control and privacy, you can host the open-weight models locally on your own infrastructure.

According to current policies, data from the web interface and API may be used for model improvement.
Enterprise users or those running models locally may have different data handling terms.

DeepSeek uses advanced architectures like Mixture-of-Experts (MoE) and Multi-Head Latent Attention (MLA).
These technologies reduce computational requirements while maintaining high performance, making the service significantly more cost-efficient than traditional models.

You can generate an API key through the official DeepSeek Open Platform.
Rate limits are dynamic and depend on current server load as well as your account’s usage history.

This usually happens due to high global demand, especially after major model releases.
To avoid this, you can use third-party API providers or deploy the model locally.

Yes. DeepSeek V4 supports up to 256K tokens, allowing it to analyze large documents, full books, extensive technical documentation, and even complex codebases.

Requirements vary depending on the model size.
Smaller distilled versions (like 7B or 14B models) can run on consumer-grade GPUs, while full-scale models like V4-Pro require enterprise-level GPU clusters with significant VRAM.