The Pro model has a total of 1.6 trillion parameters (49 billion active), which makes it the biggest open-weight model available, outstripping Moonshot AI’s Kimi K 2.6 (1.1 trillion), MiniMax’s M1 (456 billion), and more than double DeepSeek V3.2 (671 billion). The smaller, V4 Flash has 284 billion parameters (13 billion active).

DeepSeek says both models are more efficient and performant than DeepSeek V3.2 due to architectural improvements, and have almost “closed the gap” with current leading models, both open and closed, on reasoning benchmarks.

The company claims its new V4-Pro-Max model outperforms its opensource peers across reasoning benchmarks, and outstrips OpenAI’s GPT-5.2 and Gemini 3.0 Pro on some tasks. In coding competition benchmarks, DeepSeek said both V4 models’ performance is “comparable to GPT-5.4.”

However, the models seem to fall slightly behind frontier models in knowledge tests, specifically OpenAI’s GPT-5.4 and Google’s latest Gemini 3.1 Pro. This lag suggests a “developmental trajectory that trails state-of-the-art frontier models by approximately 3 to 6 months,” the lab wrote.