DeepSeek V4 Model Explained: Parameter Scale, Capabilities, and Performance
DeepSeek V4 Model pushes long context to the million-level and introduces native multimodality with a brand-new architecture. This article provides a concise explanation of DeepSeek V4’s parameter scale, capabilities, and performance.
1. Parameters and Architecture
- Scale: The full V4 version is approximately a 1 trillion parameter MoE model, with about 32 billion activated parameters; V4 Lite is around 200B and has been released.
- Context: Increased from 128K to 1 million tokens, making it more usable for entire repositories, long documents, and multi-turn Agent tasks.
- Architecture: Engram conditional memory, DSA sparse attention, and mHC improved hyper-connections, controlling costs and improving stability under long-context scenarios.
2. Key Capabilities
- Native Multimodality: Unified modeling of text, images, and videos, supporting text-to-image, text-to-video, and cross-modal reasoning.
- Code: Reported SWE-bench Verified score is approximately 83.7%, demonstrating engineering-level holistic thinking.
- Cost: Inference cost is advantageous compared to competitors, beneficial for 7×24 Agent operations and large-scale applications.
3. V4 Lite vs. Full Version
Currently, V4 Lite has been released; the full version is expected to have higher parameter counts and capabilities—please refer to official announcements for specifics. The roadmap for the DeepSeek V4 Model is long context + native multimodality + cost efficiency.
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