DeepSeek-V3.1 is built upon the DeepSeek-V3 architecture, which is extensively documented in a 52-page technical report. It utilizes a Mixture-of-Experts (MoE) framework with Multi-head Latent Attention (MLA) and an auxiliary-loss-free load balancing strategy. The V3.1 variant specifically introduces a 'hybrid reasoning' capability, allowing the model to toggle between standard and chain-of-thought modes via chat templates. While the base architecture is highly transparent, the specific 'hybrid' training delta for V3.1 is less detailed than the original V3 pre-training documentation.

The model was trained on 14.8 trillion tokens for the base version, with V3.1 receiving an additional 840 billion tokens for long-context extension (32K and 128K phases). However, the specific sources of this data remain largely undisclosed beyond general categories like 'diverse high-quality data' and 'web, code, and math.' There is no public breakdown of dataset proportions (e.g., % CommonCrawl vs % GitHub) or specific filtering/cleaning code, which is a significant gap in upstream transparency.

The tokenizer is publicly available on Hugging Face with a vocabulary size of 129,280 tokens. It uses a byte-level BPE approach and includes specific special tokens for the 'thinking' mode (e.g., <think> and </think>). The configuration files are fully accessible, allowing for independent verification of tokenization behavior and alignment with claimed language support (primarily English and Chinese).

DeepSeek-V3.1 is transparent about its MoE structure, disclosing a total of 671 billion parameters with 37 billion active parameters per token. The architectural breakdown (61 layers, 256 experts per layer, and 1 shared expert) is clearly stated in technical documentation. However, some third-party reports cite 685B total parameters (including MTP modules), creating slight confusion that requires careful reading of the technical report to resolve.

The provider offers unusually detailed compute metrics for a model of this scale. The technical report states the pre-training required 2.664M H800 GPU hours, with an additional 119K for context extension and 5K for post-training, totaling approximately 2.788M hours. They also provide an estimated training cost of $5.6M. While hardware specs are clear (H800 clusters), a formal carbon footprint calculation is missing.

While DeepSeek provides extensive benchmark results (MMLU, MATH, HumanEval) and some evaluation scripts on GitHub, third-party reproduction has shown inconsistent results. For instance, community tests on LiveCodeBench for V3.1-Base reported significantly lower scores than official claims. The lack of a unified, one-click reproduction suite for the V3.1 specific 'hybrid' benchmarks limits full verifiability.

The model consistently identifies itself as DeepSeek-V3.1 and is transparent about its dual-mode capabilities (thinking vs. non-thinking). It correctly handles system prompts to switch between these identities and does not exhibit the identity confusion common in models that are heavily distilled from competitors. Versioning is clearly maintained through Hugging Face and API endpoints.

The model weights are released under the MIT License, which is highly permissive for both commercial and non-commercial use. However, there is some ambiguity regarding the 'DeepSeek Model License' mentioned in some repositories, which can conflict with the MIT header on Hugging Face. The terms for derivative works and output usage are generally clear but require cross-referencing multiple documents.

VRAM requirements are documented for various quantization levels (FP8, BF16), with clear guidance that a full BF16 deployment requires significant resources (approx. 1.3TB VRAM). While community tools like Unsloth provide additional guidance for consumer hardware (e.g., 226GB for 2-bit), the official documentation focuses primarily on enterprise-grade H800/A100 clusters, leaving a gap for smaller-scale users.

DeepSeek uses a versioning system (V3 -> V3-0324 -> V3.1), but the release of V3.1 was described as a 'silent launch' without a formal changelog or detailed migration guide. While weights are versioned on Hugging Face, there is limited documentation on how the model's behavior drifts over time due to the frequent, unannounced updates to the hosted API endpoints.