DeepSeek

Chinese AI lab and model family. Originally a research arm of High-Flyer Quant; spun out as a standalone lab around 2023. Sits in the small cluster of labs (alongside Mistral, Qwen, Yi) that ship competitive open-weights models, and has the strongest published reasoning lineage of the Chinese labs through the R1 / V3 / V4 series.

The lab matters in the LLM landscape for a few reasons distinct from raw capability:

• Open weights for frontier-class reasoning. R1 (Jan 2025) was the first open-weights model to land near the top of reasoning benchmarks, with weights and technical report public. V4 continues that pattern.
• Training-cost reporting that flipped the discourse. The published training compute and budget numbers for V3/R1 were small enough relative to GPT-4-class assumptions that they reframed the cost discussion across the industry.
• MoE architecture choices that pushed the routing-and-expert-load-balancing literature forward, picked up downstream by other labs.

Model line as relevant to this vault

• V3 — Mid-2024 dense+MoE base model. The published training report (sub-$6M figure for the final pretraining run) is the source most often cited in cost-collapse arguments.
• R1 — Jan 2025 reasoning model derived from V3. Open weights. Long visible <think> traces by default, distillation-friendly.
• V4 — Current generation as of 2026. Has reasoning variants (deepseek-v4-flash, deepseek-v4-pro) plus an "Expert Mode" surface in the official app/web. See deepseek-v4-roleplay-instruct for one documented quirk of V4's training-injected style markers and controllable-thinking-style for what that implies about how its CoT data was built.

Agent ecosystem

DeepSeek maintains awesome-deepseek-agent — a curated list of 16 AI agent and coding-assistant tools (Claude Code, Pi, OpenCode, Crush, Reasonix, AstrBot, OpenClaw, etc.) with per-tool guides for using V4-Pro / V4-Flash. The list is the canonical signal of which tools DeepSeek considers first-class for V4, including both Western terminal coding agents and China-facing chat-platform agents (Feishu, WeChat, Telegram).

Things to track

• Future ingests of DeepSeek technical reports (V4 if/when published)
• Distilled / quantized derivatives of V4 from the open-source community
• Other reverse-engineered training-format quirks similar to the roleplay markers
• Cost-curve comparisons (DeepSeek vs OpenAI / Anthropic per-token cost)
• New entries on awesome-deepseek-agent worth a closer look

Related pages

• deepseek-v4-roleplay-instruct — undocumented <think>-style control markers in V4
• controllable-thinking-style — broader pattern these markers exemplify
• awesome-deepseek-agent — DeepSeek's curated list of 16 agent / coding-assistant integrations