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…
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 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.
This exact model name is also listed by 5 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 complex reasoning and technical workflows. With 1.6 trillion total parameters and 49 billion active parameters per token, it is engineered to deliver performance that rivals top-tier closed-source models. The architecture features a hybrid attention mechanism that combines Compressed Sparse Attention and Heavily Compressed Attention, allowing the model to maintain a one-million-token context window with significantly improved efficiency. By incorporating Manifold-Constrained Hyper-Connections, the design ensures stable signal propagation, making it particularly effective for demanding tasks in mathematics, STEM, and agentic coding.
The model lineage focuses on achieving high-efficiency intelligence at scale, utilizing structural innovations to reduce inference costs and computational overhead. By requiring only a fraction of the FLOPs and KV cache compared to its predecessors, the model provides a practical solution for developers needing deep reasoning capabilities without the typical resource intensity. Its design is optimized for complex agentic workflows, positioning it as a leading open-weight choice for users who require high accuracy in coding and world knowledge. As a versatile tool for technical decision-makers, it serves as a bridge between high-end reasoning performance and cost-effective, long-context deployment.
Why teams adopt it
Discuss this model
Add corrections, implementation notes, pricing changes, or usage caveats for other readers.