DeepSeekR1 - Full Breakdown

DeepSeek has released open weights for its reasoning model family, led by DeepSeek R1, along with a set of distilled smaller models that can outperform several well-known proprietary systems on benchmark tasks. The release matters because it makes “reasoning-style” performance—previously locked behind closed APIs—available to anyone who can run the models locally or via DeepSeek’s chat interface, and it also includes the training approach behind the results.

The biggest headline is that DeepSeek R1 arrives not as a single model, but as an ecosystem: the original R1 weights, a precursor called DeepSeek V3, and multiple distilled variants down to 1.5 billion parameters. In reported benchmark comparisons, DeepSeek R1 (shown as the blue line) lands on par with OpenAI o1 in some tests and beats it in others, while also surpassing OpenAI o1 mini across multiple benchmarks. The distilled models are positioned as especially striking: smaller parameter counts still deliver strong scores, including cases where a 1.5B model is reported to score higher than Claude 3.5 Sonnet on certain tasks.

A key technical thread ties the family together: DeepSeek R1 uses the same underlying mixture-of-experts (MoE) base as DeepSeek V3. DeepSeek V3 is described as an MoE system with 671 billion total parameters, but only 37 billion active at any moment. DeepSeek R1 keeps that active-parameter profile while changing the post-training and reinforcement-learning pipeline, suggesting the performance jump comes more from how the model is trained after pretraining than from simply scaling raw size.

In live testing through DeepSeek.com’s chat demo, the model’s behavior emphasizes stepwise internal deliberation. It often produces “thinking” content enclosed in tags, then backtracks or clarifies before answering. Example prompts include simple factual reasoning (e.g., a sibling-count question), spelling correction, and scenario analysis such as how geopolitical dynamics might shift after a new abundant clean energy source is discovered. When asked about sentience, it responds with a careful definition-first approach and concludes it is not sentient. Asked how close it is to AGI, it gives a blunt assessment that current LLMs are nowhere near AGI.

The accompanying technical report frames the training pipeline around two main models: DeepSeek R0 and DeepSeek R1. DeepSeek R0 is described as taking the pretrained DeepSeek V3 base and applying reinforcement learning without the usual supervised fine-tuning step. Instead, the system is coaxed into generating chain-of-thought style outputs using a prompt template that forces user/assistant conversation formatting and “think”/“answer” tags. Rewards are rule-based—using deterministic checks on tasks like GSM8K—rather than relying on an external learned reward model.

DeepSeek R1 then follows a multi-stage process: start with a small “cold start” fine-tuning set (thousands of examples), run reinforcement learning, return to supervised fine-tuning using rejection sampling from the RL checkpoint, and finally apply another RL stage. Distillation is presented as a major lever for the smaller models: DeepSeek reportedly fine-tunes distilled variants using 800,000 curated samples generated from DeepSeek R1, and those distilled models outperform applying RL directly at smaller scales.

Finally, the release includes practical guidance for running models. The full-size 671B MoE version is likely out of reach for most local setups, but distilled sizes from 1.5B up to 70B are available, including quantized options. Local experiments highlight strengths in math-style reasoning (GSM8K with LaTeX outputs) while showing limitations in tool use, structured JSON output, and agent-like behavior—areas expected to improve in later iterations.