DeepSeek V4: How Million‑Token Context and Open‑Source Design Redefine AI Ecosystems

Core Information: Model Variants and Parameters

DeepSeek‑V4‑Pro – 1.6 T total parameters, 49 B activation parameters, trained on 33 T tokens, 1 M context length. Marketed as a high‑performance flagship comparable to top closed‑source models.

DeepSeek‑V4‑Flash – 284 B total parameters, 13 B activation parameters, trained on 32 T tokens, 1 M context length. Designed for higher cost‑efficiency and faster inference.

Technical Breakthroughs

Introduction of DSA sparse attention combined with token‑dimension compression, dramatically reducing memory consumption and making a 1 M token context the default service offering.

Support for configurable thinking modes that adjust inference intensity; the maximum intensity is recommended for complex agent scenarios such as code generation and long‑document analysis.

Open‑source weights released on Hugging Face and ModelScope, enabling local deployment, fine‑tuning, and API compatibility with OpenAI‑style and Anthropic‑style interfaces.

Performance Highlights

V4‑Pro outperforms all existing open‑source models on Agentic Coding, mathematical reasoning, and world‑knowledge benchmarks, reaching quality close to Claude Opus 4.6 when the thinking mode is disabled.

V4‑Flash delivers inference quality near V4‑Pro while using fewer parameters and activations, resulting in faster and cheaper API services suitable for large‑scale deployment.

Hardware Ecosystem Impact

Domestic Chip Adaptation

Core operators rewritten and migrated to CANN, achieving end‑to‑end training and inference on Huawei Ascend 950 PR, Cambricon, and Tianzu Smart Chip.

Breaks the compute monopoly of Nvidia GPUs: top‑tier models can now run natively on domestic AI chips, forming a “chip‑framework‑model‑application” closed loop.

Optimized inference on Ascend 950 PR improves performance while hardware cost drops to roughly one‑third of high‑end Nvidia solutions; API call cost falls to about 1 % of comparable closed‑source models.

Enables fully domestic AI pipelines, enhancing data‑security and supply‑chain stability.

Capability and Cost Revolution

Million‑token context (≈750 k words) becomes standard across both variants; architectural innovations reduce compute consumption to 27 % of the previous generation and compress KV cache to 10 % of its size.

Long‑process task stability: agents retain multi‑turn dialogue, tool‑call chains, and code‑base context, preventing “forgetting” and task drift in scenarios such as code engineering, enterprise workflow automation, and long‑document analysis.

Scalable professional deployment: single requests can load 500 k lines of code, 800‑page PDFs, or extensive literature without truncation, supporting R&D, legal, finance, and medical AI applications.

Affordability: V4‑Flash pricing allows individual developers and SMEs to use million‑token context without prohibitive cost.

Open‑Source vs. Closed‑Source

Released under a permissive open‑source license with full weights and technical details; performance matches closed‑source flagships while costing only a few percent of their price.

Global developers can download, deploy, and fine‑tune the model, lowering innovation barriers and fostering vertical‑domain applications.

Low‑cost open‑source models challenge the high‑margin pricing of closed solutions, shifting enterprise spending toward R&D and business innovation.

Optimization for domestic chips and collaborative development avoids single‑vendor lock‑in and boosts societal digital productivity.

Industry Perspective (Nvidia CEO Jensen Huang)

Huang warned that if high‑quality open‑source models like DeepSeek are forced to be tightly optimized for Chinese hardware, the global advantage of the US tech stack could erode.

Abandoning the world’s second‑largest market may compel China to build an independent compute architecture, potentially reshaping long‑term AI standards.

DeepSeek‑V4’s combination of domestic‑chip adaptation and open‑source availability is altering global AI compute and standards.

Conclusion

DeepSeek‑V4 demonstrates that architectural innovation (DSA sparse attention, token compression), full domestic‑chip support, and open‑source licensing can simultaneously deliver million‑token context, high performance, and low cost, thereby breaking hardware monopolies, democratizing agentic AI, and reshaping the competitive landscape of global AI ecosystems.

Code example

第一时间获取互联网一线大厂的核心技术，应用架构