DeepSeek — Everything You Need to Know

DeepSeek is a Chinese AI research lab that has rapidly become one of the most consequential players in the foundation model space. Founded by Liang Wenfeng and backed by quantitative hedge fund High-Flyer, DeepSeek builds open-weight large language models that compete with closed-source offerings from Anthropic, OpenAI, and Google — at a fraction of the training cost. Their models use Mixture-of-Experts (MoE) architecture to achieve strong benchmark performance while keeping inference efficient. DeepSeek's combination of open releases, technical innovation, and cost efficiency has forced the entire industry to rethink assumptions about what it takes to build frontier AI.

Latest Developments

DeepSeek made global headlines in early 2025 with the release of DeepSeek-R1, a reasoning model that matched or exceeded OpenAI's o1 on key benchmarks while being fully open-weight. The model demonstrated that chain-of-thought reasoning capabilities — previously assumed to require massive proprietary infrastructure — could be achieved with significantly less compute. R1's release triggered a market reaction, briefly wiping hundreds of billions off US tech valuations as investors reassessed the competitive moat of closed-model companies.

DeepSeek-V3, their general-purpose flagship, pushed MoE efficiency further with 671 billion total parameters but only 37 billion active per token. Training reportedly cost under $6 million — orders of magnitude less than comparable Western models. The model performs competitively on coding, math, and multilingual benchmarks.

DeepSeek has also expanded into specialized models, including DeepSeek-Coder for code generation tasks, which has gained traction in the open-source developer community.

Key Features and Capabilities

Mixture-of-Experts Architecture: DeepSeek's models route each token through a small subset of total parameters, keeping inference costs low while maintaining the capacity of a much larger model. DeepSeek-V3's 671B parameter count sounds massive, but only ~37B parameters activate per forward pass — making it practical to run on smaller GPU clusters.

Multi-Head Latent Attention (MLA): DeepSeek introduced MLA as an alternative to standard multi-head attention, compressing key-value caches to reduce memory usage during inference. This architectural innovation allows longer context windows without proportional memory scaling.

Open-Weight Releases: Unlike Claude or GPT-4, DeepSeek models ship with downloadable weights under permissive licenses. Researchers and companies can fine-tune, deploy on-premises, and inspect the models directly. This has made DeepSeek a default choice for teams that need full control over their model infrastructure.

Reasoning Capabilities: DeepSeek-R1 uses reinforcement learning to develop chain-of-thought reasoning, producing step-by-step solutions for math, logic, and coding problems. The model's reasoning traces are transparent and inspectable — a property that has attracted significant research interest.

Cost Efficiency: DeepSeek's training costs consistently come in far below Western competitors. This efficiency extends to inference: the MoE architecture means API and self-hosted deployments use less compute per request, translating to lower operational costs.

Common Questions

Questions about DeepSeek are among the most frequent we receive. Key areas of interest include model selection, deployment options, and how DeepSeek compares to closed-source alternatives. As we build out our FAQ section, we'll link detailed answers here.

Common topics developers ask about:

• How does DeepSeek-R1 compare to OpenAI o1? — R1 matches o1 on many reasoning benchmarks while being open-weight and significantly cheaper to run
• Can I self-host DeepSeek models? — Yes, weights are publicly available and can be deployed on your own infrastructure with appropriate GPU resources
• What's the difference between DeepSeek-V3 and DeepSeek-R1? — V3 is the general-purpose model optimized for broad capability; R1 is specialized for step-by-step reasoning tasks

How DeepSeek Compares

DeepSeek occupies a unique position: open-weight models with frontier-level performance. Against closed-source competitors like Anthropic's Claude or OpenAI's GPT-4, DeepSeek trades polish and safety tooling for transparency and cost efficiency. Against other open-weight models like Meta's Llama series, DeepSeek generally leads on reasoning and coding benchmarks.

We're building detailed comparison pages — check back for head-to-head breakdowns as they publish.

All DeepSeek Resources

Blog Posts

• Claude Code Extension Stack: Skills, Hooks, Agents, and MCP

Glossary

• DeepSeek — Chinese AI lab building open-weight foundation models
• Agentic — AI systems that plan and execute multi-step tasks autonomously
• Anthropic — AI safety company building Claude
• Claude — Anthropic's family of large language models

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