🧠 Model Architecture

• Model Name: DeepSeek R1 0528
• Series: Part of the deepseek_v3 family
• Architecture Type: Mixture-of-Experts (MoE)
  • Total Parameters: ~671 billion
  • Active Parameters per Token: ~37 billion
• Attention Mechanism: Multi-head Latent Attention (MLA)
  • Enhances context reasoning and parallelism efficiency
• Context Window: 64K–128K tokens
• Tokenizer: Byte-level BPE
• Training Data: Multilingual + code-rich corpus, emphasis on high-quality reasoning, math, and logic content
• Training Hardware: Distributed H800 GPU clusters with full expert, pipeline, and data parallelism
• Training Efficiency: Uses curriculum learning + expert routing to balance speed and convergence

⚙️ Capabilities

• Primary Domains:
  • Advanced mathematical reasoning
  • Formal logic and deduction
  • Natural language understanding
  • Code generation and debugging
  • Multi-step planning (chain-of-thought)
• Notable Features:
  • Handles deep tree-like logical flows with stable consistency
  • Excellent JSON/function calling support
  • Competitive long-form reasoning without model collapse
  • Great at in-context learning across large prompts

📊 Performance Benchmarks

• AIME Math Accuracy: ~87.5%
• MMLU-Redux (reasoning): ~93.4%
• MMLU-Pro (general knowledge): ~85%
• LiveCodeBench (code generation): ~73.3%
• Hallucination Rate: Significantly reduced from v2 generation models

🚀 Inference & Deployment

• Step Latency:
  • First Token Latency: ~2.3 seconds
  • Throughput: ~28.9 tokens/sec
• API Ready:
  • OpenAI-compatible format
  • Accepts structured tool-calling inputs
  • Can return structured JSON outputs with high reliability
• Deployment Contexts:
  • Reasoning agents
  • AI tutors
  • Complex retrieval-augmented generation (RAG) systems
  • Autonomous planning pipelines
  • Coding copilots for IDEs

⚖️ Model Strengths vs Peers

• Compared to GPT-4-turbo / o3: Slightly behind on raw language fluency but close in math and code
• Outperforms: Qwen 2.5, Grok-3-mini, Claude 3 Haiku in logic-heavy tasks
• Edge Case Handling: Much better at understanding ambiguous but valid input ranges (e.g., math word problems, recursive reasoning)

📦 Deployment Specs