DeepSeek-R1 provides high transparency regarding its architecture, explicitly stating it is built upon the DeepSeek-V3-Base model. The technical report and GitHub documentation detail the Mixture-of-Experts (MoE) structure, the use of Multi-head Latent Attention (MLA), and the auxiliary-loss-free load balancing strategy. The training methodology is thoroughly documented, describing the transition from DeepSeek-R1-Zero (pure RL) to DeepSeek-R1 (cold-start SFT + RL). While the base model's pre-training is well-documented in the preceding V3 paper, the R1-specific modifications are clearly delineated.

While the model documentation mentions the use of 14.8 trillion tokens for the underlying V3 base model and specific 'cold-start' data (thousands of reasoning samples) for R1, it lacks a detailed public breakdown of the dataset composition by percentage or specific source names. The filtering and cleaning methodologies are described in general terms ('high-quality', 'carefully curated') without providing a comprehensive breakdown of web, code, and book proportions. The 800k samples used for distillation are mentioned, but the original pre-training data remains largely opaque beyond general categories.

The tokenizer is publicly accessible via the official GitHub repository and Hugging Face. It uses a Byte-Pair Encoding (BPE) approach with a stated vocabulary size of approximately 129,280 tokens (though some configuration files show 151,665 to match embedding sizes). The tokenizer's alignment with the claimed multilingual and technical (code/math) capabilities is verifiable through the provided code and model files. Documentation exists for tokenization behavior, including the handling of special tokens for reasoning traces (<think> tags).

DeepSeek-R1 is exemplary in its disclosure of parameter density for an MoE architecture. It explicitly states a total of 671 billion parameters with 37 billion active parameters per token. The architectural breakdown is further detailed in the technical report, clarifying the distribution of parameters between the dense layers and the MoE experts. This level of detail prevents the common 'parameter inflation' marketing trap seen in other sparse models.

The technical report provides specific details on training compute, stating the use of 2,048 NVIDIA H800 GPUs over a period of approximately two months for the R1 training phase. It also references the 2.788 million H800 GPU hours used for the V3 base model. While it provides hardware specifications and duration, it lacks a direct, official calculation of the total carbon footprint or a granular breakdown of energy consumption, though these can be estimated from the provided hardware and time data.

DeepSeek provides a comprehensive list of benchmark results (AIME, MATH-500, MMLU, etc.) and specifies the evaluation settings (temperature 0.6, top-p 0.95, 64 samples for pass@1). However, the full evaluation code and the exact prompts for every benchmark are not fully centralized in a single reproducible repository, and some third-party audits have noted difficulties in matching the exact reported scores without further clarification on prompt formatting (e.g., the impact of system prompts vs. user prompts).

The model demonstrates high identity consistency, correctly identifying itself as DeepSeek-R1 and maintaining awareness of its versioning. It is transparent about its nature as a reasoning model and its reliance on chain-of-thought processing. There are no significant reports of the model claiming to be a competitor's product or denying its AI nature in official deployments. The distinction between the 'Zero' and standard R1 variants is clearly maintained in its self-identification.

DeepSeek-R1 is released under the MIT License, which is one of the most transparent and permissive open-source licenses available. The license terms are explicitly stated in the GitHub repository and Hugging Face model cards, clearly allowing for commercial use, modification, and redistribution. There are no conflicting 'open-weight' custom licenses or hidden commercial restrictions that override the MIT terms for the 671B model.

Hardware requirements are well-documented for various deployment scenarios. The documentation specifies that the full 671B model requires significant VRAM (~1.3TB in FP16, reduced with quantization) and recommends multi-GPU setups (e.g., 16x A100 80GB). Quantization tradeoffs are discussed by the community and supported by official model formats (GGUF, etc.), though official documentation could be more detailed regarding the specific accuracy-loss curves for different quantization levels (4-bit vs 8-bit) on the full 671B variant.

DeepSeek maintains a versioning system (e.g., the 0528 update) and provides changelogs for major releases. However, the frequency of silent updates to the API-hosted versions has been a point of concern for some users, and the documentation for 'drift'—specifically how alignment or safety updates affect reasoning performance over time—is not as comprehensive as the initial architectural disclosures. Semantic versioning is used, but the granularity of change documentation for minor weights updates is limited.