Master the vocabulary behind DeepSeek's reasoning-focused R1 model and its distilled variants.
0 / 5 completed
1 / 5
At standup, a dev asks what distinguishes DeepSeek-R1 from a standard chat model. What is the key trait?
DeepSeek-R1 is a reasoning model trained with large-scale reinforcement learning to produce extended chain-of-thought before answering. This targets stronger performance on math, coding, and logic tasks. It contrasts with models optimized purely for fast conversational replies.
2 / 5
During a design review, the team notices the API response includes a separate field for the model's internal deliberation. What is this called?
DeepSeek-R1 responses can expose reasoning content separately from the final answer, showing the chain-of-thought the model used. Apps can choose to display, log, or hide this trace. Separating it from the final answer keeps the UI clean while preserving transparency.
3 / 5
In a code review, a dev notes R1 was distilled into smaller dense models. What is this process called?
Distillation transfers reasoning capability from the large R1 model into smaller dense models (e.g., based on Llama or Qwen architectures) by training them on R1's outputs. This yields faster, cheaper models retaining much of the reasoning strength. It is how R1's capabilities became available at smaller sizes.
4 / 5
An incident report shows a latency-sensitive chat feature became sluggish after switching to R1. What is the likely cause?
Because R1 performs extended reasoning before answering, it generates substantially more tokens per response than a non-reasoning model, increasing latency. For latency-sensitive chat, a lighter distilled or non-reasoning model may fit better. Reasoning depth is the direct tradeoff against speed.
5 / 5
During a PR review, a teammate asks how DeepSeek-R1 compares on cost to closed frontier reasoning models. What is generally true?
DeepSeek-R1 is notable for offering competitive reasoning performance at substantially lower API pricing than many closed frontier alternatives, and its weights are open for self-hosting. This combination drove much of its adoption. Cost-conscious teams weigh this against closed-model ecosystem features.