DeepSeek is a Chinese AI research lab that has rapidly become one of the most important contributors to open-source AI, producing state-of-the-art coding models (DeepSeek-Coder), general-purpose LLMs, and pioneering Mixture-of-Experts architectures — with DeepSeek-Coder widely considered the best open-source code model (surpassing CodeLlama on all benchmarks), native Fill-in-the-Middle support for code completion, and a general-purpose 67B model that rivals GPT-3.5 on reasoning tasks.

What Is DeepSeek?

• Definition: An AI research laboratory (founded 2023, Hangzhou, China) that develops and open-sources large language models — distinguished by exceptional coding model quality, innovative MoE architectures, and a research-first approach that publishes detailed technical reports alongside model releases.
• DeepSeek-Coder: The flagship coding model family trained on 2 trillion tokens of code and code-related data — available in 1.3B, 6.7B, and 33B sizes, consistently topping open-source code generation benchmarks (HumanEval, MBPP, MultiPL-E).
• Fill-in-the-Middle (FIM): DeepSeek-Coder natively supports FIM — given code before and after a cursor position, the model generates the missing middle section. Essential for IDE code completion (like GitHub Copilot) where the model needs to understand both preceding and following context.
• DeepSeek-V2/V3: General-purpose models using innovative MoE (Mixture of Experts) architecture with Multi-head Latent Attention (MLA) — achieving frontier performance with significantly lower inference costs than dense models of equivalent quality.

DeepSeek Model Family

Why DeepSeek Matters

• Coding Model Leadership: DeepSeek-Coder models beat CodeLlama across all sizes and benchmarks — the 33B model rivals GPT-3.5-Turbo on code generation tasks, making it the default choice for open-source code assistants.
• MoE Innovation: DeepSeek-V2 introduced Multi-head Latent Attention (MLA) that reduces KV-cache memory by 93% compared to standard attention — a fundamental efficiency improvement for serving large models.
• Cost Efficiency: DeepSeek-V2's MoE architecture activates only 21B of 236B parameters per token — achieving performance comparable to Llama-3-70B at a fraction of the inference cost.
• Research Transparency: DeepSeek publishes detailed technical reports with training details, ablation studies, and architectural innovations — contributing genuine research advances to the open-source community.

DeepSeek is the research lab that redefined what open-source AI models can achieve in code generation and efficient inference — producing the best open-source coding models, pioneering MoE architectures with novel attention mechanisms, and demonstrating that Chinese AI labs can lead in both model quality and research innovation.