DeepSeek

Why It Matters

DeepSeek changed the conversation about AI development economics. When DeepSeek R1 launched in January 2025, it demonstrated that frontier-level reasoning capabilities don’t require frontier-level budgets. The model matches OpenAI’s o1 on reasoning benchmarks while being open-weight and dramatically cheaper to run.

For AI engineers, this matters in two ways. First, open-weight reasoning models enable applications that were previously cost-prohibitive. Running sophisticated reasoning in production at scale becomes viable. Second, DeepSeek’s architectural innovations, particularly their efficient use of mixture-of-experts, provide a template for building capable models without massive compute budgets.

The broader impact: DeepSeek proved that smaller, focused teams can compete with well-funded labs. This shifts the landscape from “who has the most GPUs” toward “who uses compute most efficiently.”

Key Models

DeepSeek V3 The general-purpose foundation model. Uses a mixture-of-experts architecture with 671B total parameters but only 37B active per token. This gives you large-model quality at small-model inference costs. Competitive with GPT-4 and Claude on general benchmarks while being open-weight.

DeepSeek R1 The reasoning-focused model that made headlines. Trained with reinforcement learning to perform multi-step reasoning, similar to OpenAI’s o1 approach. Shows its reasoning process explicitly, making it valuable for applications requiring explainable decision-making. Open-weight, meaning you can run it on your own infrastructure.

DeepSeek Coder Specialized for code generation and understanding. Matches or exceeds proprietary models on coding benchmarks while remaining open-weight. Useful for code completion, generation, and analysis tasks.

Implementation Basics

API Access DeepSeek offers API access with pricing significantly below competitors. The R1 model is available through their API and through platforms like GitHub Models. Standard OpenAI-compatible endpoints make integration straightforward.

Self-Hosting As open-weight models, DeepSeek can run on your own infrastructure. The mixture-of-experts architecture is memory-intensive (you load all experts) but compute-efficient (only some experts activate per token). Plan for high VRAM requirements but reasonable inference costs.

When to Use DeepSeek

• Cost-sensitive reasoning tasks: R1 provides o1-level reasoning at a fraction of the cost
• Coding applications: DeepSeek Coder competes with proprietary alternatives
• Self-hosted deployments: Open weights enable full control over your AI infrastructure
• High-volume inference: MoE architecture provides favorable economics at scale

Trade-offs to Consider

• Ecosystem maturity is lower than OpenAI or Anthropic, with fewer integrations and tools
• Model availability and support may vary by region
• For production systems, evaluate latency and throughput on your specific workloads
• Open-weight doesn’t mean open-source, as training data and full methodology aren’t public

DeepSeek models are accessible through their API, GitHub Models, and self-hosting. Start with the API for prototyping, evaluate self-hosting economics once you understand your usage patterns.