DeepSeek V4

DeepSeek V4 is DeepSeek's fourth-generation open-weight model family, released as a preview on April 24, 2026. It includes two main instruct variants: DeepSeek-V4-Pro, the largest and strongest model, and DeepSeek-V4-Flash, the smaller, faster, cheaper model for high-throughput use.

DeepSeek frames V4 around one central idea: efficient million-token context. Both variants support a 1 million token context window, and the technical report describes a hybrid attention architecture combining Compressed Sparse Attention and Heavily Compressed Attention. DeepSeek says that in the 1M-token setting, V4-Pro uses only 27% of the single-token inference FLOPs and 10% of the KV cache required by DeepSeek V3.2.

DeepSeek V4 Variants

Variant	Total parameters	Active parameters	Context window	Best fit
DeepSeek-V4-Pro	1.6T	49B	1M tokens	Frontier-style reasoning, coding, agentic workflows, and knowledge-heavy tasks
DeepSeek-V4-Flash	284B	13B	1M tokens	Faster production inference, cheaper API use, and simpler agent tasks

Both variants are Mixture-of-Experts models. That means the full model stores far more capacity than it activates on each token. V4-Pro is enormous in stored capacity, but it only activates 49B parameters per token. V4-Flash pushes the same idea further for cost-sensitive deployment, activating 13B parameters per token.

Architecture

DeepSeek V4 uses three major architectural and optimization changes over prior DeepSeek models:

• Hybrid attention: CSA plus HCA for cheaper long-context inference.
• Manifold-Constrained Hyper-Connections: a residual connection upgrade intended to improve signal propagation and model stability.
• Muon optimizer: used to improve training convergence and stability.

The models were pretrained on more than 32 trillion tokens and then post-trained through a pipeline that combines supervised fine-tuning, reinforcement learning, and on-policy distillation across domain-specific capabilities.

Benchmark Profile

DeepSeek's own reported evaluation positions V4-Pro-Max, the maximum reasoning effort mode of V4-Pro, as one of the strongest open-weight models available in spring 2026. Published highlights include:

• Codeforces: 3206
• LiveCodeBench: 93.5
• IMOAnswerBench: 89.8
• SWE Verified: 80.6
• Terminal Bench 2.0: 67.9
• Toolathlon: 51.8

The important point is not that DeepSeek V4 beats every closed frontier model. It does not. The important point is that an open-weight model family is now close enough on coding, reasoning, browsing, tool use, and long-context tasks that cost, deployment control, and data sovereignty become first-order model-selection factors.

Why It Matters

DeepSeek V4 matters because it compresses the gap between open-weight and proprietary models while making 1M-token context a default part of the product. For teams building coding agents, document analysis systems, repository-scale assistants, and enterprise workflows, that changes the tradeoff. The question is no longer only which model is best. It is whether a slightly weaker but much more controllable and cheaper open-weight model is good enough.

DeepSeek V4 also sits in the broader shift toward efficient sparse models. Like GLM-5, Qwen, and other frontier open-weight releases, it separates stored knowledge from active inference cost. The model is huge, but the per-token compute path is much smaller than the headline parameter count suggests.