DeepSeek V3

Generates coherent text responses up to 128K tokens using a transformer architecture with Multi-Head Latent Attention (MLA), enabling processing of entire documents, codebases, or conversation histories in a single forward pass without context truncation. The MLA mechanism compresses attention heads into latent space, reducing memory overhead compared to standard multi-head attention while maintaining semantic coherence across extended sequences.

Generates syntactically correct, semantically meaningful code across 40+ programming languages using transformer-based sequence prediction trained on 14.8 trillion tokens including substantial code corpora. Achieves GPT-4o-level performance on coding benchmarks through instruction tuning and RLHF (post-training method unspecified in documentation), enabling both single-function completion and multi-file architectural generation.

Achieves GPT-4o-level performance (87.1% MMLU, 90.2% MATH) with training cost of $5.5M through DeepSeekMoE and MLA architectural innovations, reducing training cost by estimated 5-10x compared to dense models of equivalent capability. Cost efficiency enables rapid iteration on model improvements and makes large-scale model development accessible to organizations with limited compute budgets.

Maintains conversation context across multiple turns using transformer-based attention mechanisms, enabling coherent multi-turn dialogues where the model references previous messages and maintains consistent persona and knowledge state. Context preservation operates within 128K token window, allowing conversations with 100+ turns before context truncation.

Solves mathematical problems including algebra, calculus, geometry, and formal logic through chain-of-thought reasoning patterns learned during training on 14.8 trillion tokens. Achieves 90.2% accuracy on MATH benchmark (claimed GPT-4o parity) by decomposing problems into intermediate reasoning steps and generating step-by-step solutions with symbolic manipulation.

Answers factual questions and retrieves knowledge across diverse domains (science, history, culture, current events) using transformer-based language understanding trained on 14.8 trillion tokens. Achieves 87.1% accuracy on MMLU benchmark (claimed GPT-4o parity) by leveraging broad training data and instruction-tuned response formatting for structured knowledge extraction.

Routes each token through a subset of 37B active parameters from a total 671B parameter pool using DeepSeekMoE architecture, enabling inference cost and latency comparable to much smaller dense models while maintaining capability parity with larger models. Expert routing is learned during training and applied deterministically at inference time, reducing GPU memory requirements and per-token computation.

Compresses multi-head attention mechanisms into latent space using learned projections, reducing memory overhead and computation of attention operations while maintaining semantic quality across 128K token sequences. MLA replaces standard multi-head attention's O(n²) memory complexity with a more efficient latent representation, enabling longer contexts on fixed GPU memory budgets.

Distributes model weights and architecture under MIT license, permitting unrestricted commercial use, modification, and redistribution without royalty payments or usage restrictions. This licensing approach enables organizations to build proprietary products, fine-tune models for commercial applications, and integrate DeepSeek V3 into closed-source systems without legal constraints.

Provides REST API access to DeepSeek V3 through the DeepSeek Open Platform, enabling developers to integrate the model into applications without local deployment. API supports standard text generation parameters (temperature, top-p, max-tokens) and returns structured JSON responses with generated text, token counts, and usage metadata.

Provides web-based chat interface (DeepSeek App and web version) enabling non-technical users to interact with V3 model through conversational UI without API integration or local deployment. Interface supports multi-turn conversations, context preservation across turns, and real-time streaming of generated responses.

Generates responses formatted according to instruction-tuning objectives, producing structured outputs including step-by-step reasoning, code with comments, formatted lists, and other organized response formats. Instruction tuning (method unspecified) enables the model to follow complex multi-part instructions and produce outputs matching specified formats without explicit prompt engineering.