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…
Chinese startup says DeepSeek-V4-Pro beats all rival open models for maths and coding.
According to @deepseek_ai, the DeepSeek API now supports the new deepseek-v4-pro and deepseek-v4-flash models with 1M context windows and dual Thinking and...
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-
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.
DeepSeek-V4-Pro Description DeepSeek-V4-Pro is a Mixture-of-Experts (MoE) language model with 1.6 trillion total parameters and 49 billion activated parameters. It features a hybrid attention architecture combining Compressed Sparse Attention (CSA) and Heavily Compressed Attention (HCA), achieving 2...
The DeepSeek-V4-Pro Container is a deployable inference container for serving DeepSeek-V4-Pro, a third-party sparse Mixture-of-Experts language model for reasoning, coding, and agentic tasks.
This exact model name is also listed by 3 other providers.
Keep Reviews Moving
When AI speeds up shipping, review queues get exposed fast. CodeRabbit reviews pull requests quickly, catches issues that surface late, and adds coverage before code reaches production.
Developers already feel this
DeepSeek-V4-Pro is a large-scale Mixture-of-Experts language model built to handle massive information processing tasks with high efficiency. It features a hybrid attention architecture that combines Compressed Sparse Attention and Heavily Compressed Attention to manage a one-million-token context window while significantly reducing inference costs and memory requirements compared to its predecessors. With 1.6 trillion total parameters and 49 billion activated parameters, the model is engineered to excel in demanding environments, offering state-of-the-art performance in agentic coding, STEM reasoning, and broad world knowledge.
The model undergoes a rigorous 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 to ensure stable signal propagation and robust performance. By incorporating manifold-constrained hyper-connections to strengthen residual connections, the architecture provides a reliable foundation for complex agentic workflows. These advancements position the model as a powerful, cost-effective tool for developers seeking to integrate high-level reasoning and extensive context handling into their applications.
Why teams adopt it
... NVIDIA GPUs DeepSeek V4 Pro is ideal for: Advanced reasoning and analytical tasks; Code generation and complex debugging; AI research and ...
... DeepSeek's V4-Pro runs 1.6 trillion parameters with a million ... Nvidia," undermining the structural logic behind three years of ...
