# DeepSeek V4-Pro (1.6T)

> DeepSeek's flagship V4 preview scales to 1.6T parameters and introduces compressed sparse attention plus manifold-constrained hyper-connections for million-token contexts.

| Property | Value |
| --- | --- |
| Lab | DeepSeek (China) |
| Released | 2026-04-24 |
| Decoder type | Sparse MoE |
| Family | Sparse MoE |
| Scale | 1.6T total, 49B active (3.1% active) |
| Context window | 1,048,576 tokens |
| Attention | MLA-style CSA/HCA with mHC |
| Layer mix | 61 CSA/HCA |
| KV cache / token (bf16) | 7.7 KiB (Very low) |
| License | MIT License |

**Key detail:** Uses 384 experts, 6 routed plus 1 shared expert per token, hash-based routing, compressed attention caches, and the V4 MTP path.

## Architecture concepts

- [Compressed Sparse & Heavily Compressed Attention (CSA / HCA)](https://llm-atlas-7or.pages.dev/concepts/csa-hca.md): Pool groups of older tokens into fewer KV entries, compressing along the sequence axis.
- [Manifold-constrained Hyper-Connections (mHC)](https://llm-atlas-7or.pages.dev/concepts/mhc.md): Several parallel residual streams with learned, constrained mixing between them.
- [Multi-Head Latent Attention (MLA)](https://llm-atlas-7or.pages.dev/concepts/mla.md): Keys and values are compressed into a small latent vector that is cached instead of full K/V.
- [DeepSeek Sparse Attention (DSA)](https://llm-atlas-7or.pages.dev/concepts/deepseek-sparse-attention.md): A lightweight indexer scores past tokens and each query attends only to its top-k picks.
- [Mixture of Experts (MoE)](https://llm-atlas-7or.pages.dev/concepts/moe.md): Many feed-forward experts, but a router sends each token to only a few of them.
- [Multi-Token Prediction (MTP)](https://llm-atlas-7or.pages.dev/concepts/mtp.md): Extra heads learn to predict several future tokens, and can draft tokens at inference.

## Benchmark (Artificial Analysis Intelligence Index)

- AA Intelligence Index v5.0: total 23.1 (Knowledge Work 36.3, Coding 15.9, Science 12.8, General 27.1)
- AA Intelligence Index v4.3.2: total 36 (General 34.4, Scientific 29.5, Coding 32.6, Agents 44.2)

## Links

- Architecture diagram: https://sebastianraschka.com/llm-architecture-gallery/images/architectures/deepseek-v4-pro.webp
- View in article: https://magazine.sebastianraschka.com/p/recent-developments-in-llm-architectures#%C2%A75-csahca-mhc-and-compressed-attention-caches-deepseek-v4
- config.json: https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro/blob/main/config.json
- License: https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro/blob/main/LICENSE
- Tech report: https://arxiv.org/abs/2606.19348
- Source card (source of truth): https://sebastianraschka.com/llm-architecture-gallery/#card-deepseek-v4-pro
- Web page: https://llm-atlas-7or.pages.dev/models/deepseek-v4-pro/
- JSON: https://llm-atlas-7or.pages.dev/api/models/deepseek-v4-pro.json

_Data from Sebastian Raschka's LLM Architecture Gallery (https://sebastianraschka.com/llm-architecture-gallery/). Synced 2026-11-04._