Introduction

On April 24, 2026, Chinese AI company DeepSeek officially released the new generation large language model—DeepSeek-V4, with open-sourced model weights and technical papers. The core highlight of this version is the native 1M (1 million Token) ultra-long context window, combined with self-developed DSA sparse attention and token dimension compression technology, achieving a leap in long-text processing capability while significantly reducing inference costs.

DeepSeek officially stated: “Starting now, 1M context will be the standard for all DeepSeek official services.” This marks the official entry of open-source LLMs into the era of million-context accessibility.

1. Core Versions and Specifications

The DeepSeek-V4 series includes two versions for different scenario needs:

Version Total Parameters Activated Parameters Pre-training Data Positioning
V4-Pro 1.6 Trillion 490 Billion 33T Tokens Flagship performance, competing with top closed-source models
V4-Flash 2.84 Trillion 130 Billion 32T Tokens High cost-performance, suitable for daily calls

Both versions use MoE (Mixture of Experts) architecture, maintaining high performance while significantly reducing inference computation.

2. Core Technical Breakthroughs

2.1 Million-Token Ultra-Long Context

Traditional LLMs typically have context windows limited to 32K-128K tokens, making it difficult to process ultra-long documents. DeepSeek-V4 achieves a native 1M (1 million Token) context window, approximately equivalent to:

  • 750,000 Chinese characters
  • Complete text of “Dream of the Red Chamber” can be loaded entirely
  • Thousands of pages of legal documents processed at once
  • Seamless analysis of entire code repositories

In the “needle in a haystack” test, V4 demonstrated excellent long-range information retrieval capability, completely breaking through the bottleneck of long-text processing.

2.2 DSA Sparse Attention Mechanism

DeepSeek-V4 introduces the DSA (Dynamic Sparse Attention) architecture with innovative token dimension intelligent compression:

  • Computation: Reduced to 27% of the previous V3.2
  • Memory Usage: Compressed to 10% of the previous generation

This means that under the same hardware conditions, concurrent users can increase by 3-4x, greatly lowering the threshold for long-context inference.

2.3 CSA+HCA Hybrid Attention Architecture

DeepSeek’s self-developed CSA (Compressed Sparse Attention) + HCA (Hierarchical Context Aggregation) hybrid architecture achieves:

  • High-speed inference, first-token latency under 0.5 seconds for 1M tokens
  • Generation speed of 60-80 tokens/second
  • Significantly reduced memory usage

2.4 Dual-Platform Deep Adaptation

V4 is not only adapted to NVIDIA GPUs but also completed deep adaptation for Huawei Ascend NPU, supporting FP16/INT8 quantized inference, adapted to Baidu Cloud’s thousand-card/ten-thousand-card super node clusters, promoting domestic computing power ecosystem development.

3. Performance

3.1 Benchmark Results

Evaluation Dimension DeepSeek-V4-Pro Performance Competitor Comparison
Agentic Coding Best among open-source models, superior to Sonnet 4.5 Close to Opus 4.6 non-thinking mode
World Knowledge Significantly ahead of other open-source models Slightly behind Gemini-Pro-3.1
Math/STEM Surpasses all published open-source models On par with top closed-source models
Competition Code Surpasses all published open-source models On par with top closed-source models

3.2 Real-World Application Performance

  • Code Generation: Specially optimized for mainstream Agent products like Claude Code and CodeBuddy, approaching GPT-5.4 and Gemini-3.1-Pro in SWE-Bench Pro testing
  • Long Document Processing: Can process entire novels, complex legal documents, and multi-hour code repositories at once
  • Tool Calling: Demonstrates powerful logical reasoning and tool-calling capabilities

4. Pricing and Openness

4.1 API Pricing

DeepSeek-V4 continues its high cost-performance strategy:

  • V4-Pro: $0.002/1M tokens
  • V4-Flash: More competitive pricing

Compared with closed-source models that often cost several dollars per million tokens, DeepSeek’s price advantage is significant.

4.2 Open Source License

V4 uses MIT license for open source, allowing developers to:

  • Use model weights for free
  • Freely customize fine-tuning
  • Unlimited commercial deployment

This is of great significance for the prosperity of the domestic open-source ecosystem.

5. Application Scenarios

5.1 Enterprise Applications

  • Long Document Analysis: Contract auditing, patent search, policy interpretation
  • Code Repository Understanding: Large project architecture analysis, code review
  • Knowledge Base Q&A: Enterprise knowledge management, intelligent customer service

5.2 Developer Tools

  • Agent Development: Complex multi-step task execution
  • Code Generation: Frontend/backend application construction, automated scripts
  • Data Analysis: Large-scale dataset processing and insights

5.3 Academic Research

  • Literature Review: One-click summary of thousand-page papers
  • Cross-Document Reasoning: Multi-source information correlation analysis
  • Scientific Computing: Mathematical problem solving and proof verification

6. Comparison with Similar Products

In the current open-source LLM market, DeepSeek-V4’s competitive advantage is clear:

Model Context Window Open Source API Price Features
DeepSeek-V4 1M MIT Extremely Low Million context + dual-platform adaptation
GPT-4o 128K Closed Higher Mature ecosystem
Claude 3.5 200K Closed Higher Long context optimization
Llama 4 128K Open Source Low Community ecosystem

DeepSeek-V4, with its million-context + open-source + low-price triple advantage, has become the new benchmark in the open-source LLM field.

7. Conclusion

The release of DeepSeek-V4 marks the official entry of domestic open-source LLMs into the “Era of Million-Context Accessibility.” Its core value is reflected in:

  1. Technical Breakthrough: DSA sparse attention + token compression technology makes long context no longer a “noble ability”
  2. Performance Leap: Code generation and mathematical reasoning reach the level of top closed-source models
  3. Accessible Pricing: Allows more developers and enterprises to afford and use it effectively
  4. Open Source Ecosystem: MIT license releases innovation vitality and promotes domestic AI technology going global

As DeepSeek officially stated: “1M context will be the standard for all DeepSeek official services.” We have reason to believe that long-text intelligent processing will transform from a “luxury” to an “everyday item,” opening new imagination space for AI landing applications in various industries.

References

  • DeepSeek Official Release (April 24, 2026)
  • CSDN “Technical Signals After DeepSeek V4 Release”
  • Securities Times “Three Important Signals from DeepSeek Receiving Large Fund Support”
  • 21st Century Business Herald “Kunlun Core Completes Full-Stack Adaptation of DeepSeek-V4 Domestic Model”