DeepSeek-V4-Pro

DeepSeek‑V4‑Pro is a 1.6T‑parameter (49B active) MoE model with native 1M‑token context, hybrid CSA+HCA attention, and MIT‑licensed open weights, built for frontier‑level reasoning, coding, and long‑running agents at a fraction of V3‑class compute.

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How to Get Started

Step 1

Step 2

Paste the URL from Huggingface repository: https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro

Step 3

Choose the GPU machine to deploy.

That’s all! You’re ready to use the model in few minutes without infrastructure complexity in few minutes.

Additional Info

Applications & Use Cases

High‑end coding copilots and DevOps assistants that tackle complex repositories, competitive‑programming‑style problems, and infrastructure tasks, leveraging strong scores on LiveCodeBench, Codeforces, SWE‑bench Verified/Pro, and Terminal‑Bench 2.0.
Long‑context RAG and research agents that operate over up to 1M tokens of mixed text (papers, books, logs, wikis), using hybrid CSA+HCA attention to keep FLOPs and KV cache practical.
Multi‑step, tool‑rich agents where different reasoning modes (fast vs Think High vs Think Max) trade latency for stronger analysis in planning, data transformation, and decision‑support workflows.
Teacher and evaluation models for distillation into smaller LLMs, using V4‑Pro’s near‑frontier benchmark profile across knowledge, math, and coding as an open MIT‑licensed reference.
Experimental long‑horizon applications—such as simulation control, complex what‑if analysis, or multi‑day conversational threads—where a stable 1M‑token context and MoE efficiency are more important than minimal model size.