DeepSeek-V4-Pro: 1M‑Token MoE Model Redefines Long‑Context Generation

DeepSeek-V4-Pro is the flagship preview model of the DeepSeek‑V4 series, released by DeepSeek‑AI in April 2026. It is a Mixture‑of‑Experts (MoE) language model with 1.6 trillion total parameters, of which 49 billion are activated during inference. The model supports a context window of one million tokens, a scale previously reserved for research prototypes, and achieves this with a hybrid attention architecture that combines Compressed Sparse Attention (CSA) and Heavily Compressed Attention (HCA). According to the technical report, this design reduces single‑token inference FLOPs to 27 % and KV‑cache memory to 10 % of its predecessor, DeepSeek‑V3.2. The model is distributed in FP8 mixed‑precision (with FP4 for MoE expert parameters) and is available in 8‑bit and fp8 quantized formats, making it compatible with the Hugging Face transformers library for text‑generation pipelines.

The model was pretrained on more than 32 trillion tokens and then refined through a two‑stage post‑training pipeline that cultivates domain‑specific experts via supervised fine‑tuning and Reinforcement Learning with GRPO, followed by on‑policy distillation that consolidates these experts into a single unified model. Evaluation tables in the README show state‑of‑the‑art results on benchmarks covering world knowledge (MMLU, C‑Eval), reasoning (BBH, DROP), coding (HumanEval, BigCodeBench), and especially long‑context tasks (LongBench‑V2). The model also offers three distinct reasoning effort modes—Non‑think, Think High, and Think Max—allowing users to trade speed for depth of analysis.

DeepSeek‑V4‑Pro has quickly become a trending model on Hugging Face, with over 2.7 million downloads, nearly 4 000 likes, and a trending score of 179. Licensed under MIT, it can be run locally using the provided encoding scripts and inference guides, and is advertised as the best open‑source model for high‑quality knowledge, reasoning, and agentic tasks at the time of writing.