DeepSeek just launched its fourth generation of flagship models with DeepSeek-V4-Pro and DeepSeek-V4-Flash, both targeted at enabling highly efficient million…
Model details
DeepSeek just launched its fourth generation of flagship models with DeepSeek-V4-Pro and DeepSeek-V4-Flash, both targeted at enabling highly efficient million…
DeepSeek, a Chinese AI company, released its AI model ' DeepSeek-V4 ' on April 24, 2026. There are two versions: DeepSeek-V4-Pro and DeepSeek-V4-Flash. DeepSeek-V4-Pro has achieved scores exceeding Claude Opus 4.6 in multiple tests. deepseek-ai/DeepSeek-V4-Pro · Hugging Face https://huggingface.co/deepseek-ai/DeepSeek-
Chinese startup says DeepSeek-V4-Pro beats all rival open models for maths and coding.
DeepSeek V4 is live with two models. V4-Pro approaches Claude Opus 4.6; V4-Flash is faster and cheaper. Here's which to use, how to migrate your API, and what the Huawei chip story actually means.
1.6T parameter (49B activated) MoE model with 1M token context, hybrid attention requiring only 27% inference FLOPs and 10% KV cache vs V3.2, three reasoning modes, and 93.5% LiveCodeBench.
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DeepSeek-V4-Pro is a large-scale Mixture-of-Experts language model built to handle demanding reasoning and coding tasks with high efficiency. It features a massive 1.6 trillion total parameters, with 49 billion parameters activated per token, allowing it to maintain competitive performance against top-tier closed-source models. The architecture introduces a hybrid attention mechanism that combines Compressed Sparse Attention and Heavily Compressed Attention to manage a one-million-token context window. This design significantly reduces computational overhead, requiring only a fraction of the inference operations and memory cache compared to previous generations, making it a robust choice for deep analysis and extensive document processing.
The model benefits from a structured two-stage post-training pipeline that begins with independent domain-expert cultivation using supervised fine-tuning and group relative policy optimization. This is followed by a unified model consolidation phase through on-policy distillation, which helps refine its reasoning and agentic capabilities. By incorporating manifold-constrained hyper-connections to improve signal propagation stability, the model achieves state-of-the-art results in agentic coding benchmarks and strong performance in STEM-related fields. These advancements position it as a versatile tool for developers and enterprises looking for high-level intelligence that balances sophisticated reasoning with operational efficiency.
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