Leaked Information about “DeepSeek R2”

In today's increasingly competitive AI landscape, a new variable may soon emerge. Recently, leaked information about Deepseek R2 has sparked intense discussions in the tech community.

We have a responsibility to analyze and interpret this information, helping readers understand the potential impact and credibility of these leaks. It's important to emphasize that this analysis is based on publicly available information and has not been confirmed by Deepseek officially.

According to leaked information, Deepseek R2 employs an innovative Hybrid MoE 3.0 architecture with a total of 1.2 trillion parameters but only 78B active parameters. This design could potentially maintain high performance while reducing inference costs to just 2.7% of GPT-4o.

Deepseek R2's Technical Innovation: Beyond Parameter Scale

Unlike current mainstream large language models, Deepseek R2 appears to seek efficiency breakthroughs through architectural innovation rather than simply increasing parameter scale. If successful, this approach could challenge the current "bigger is better" paradigm in the AI field.

Mixture of Experts Architecture: Balancing Efficiency and Performance

According to leaked information, Deepseek R2 employs a proprietary Hybrid MoE 3.0 architecture, a mixture of experts gating system. Unlike traditional Transformer architectures, MoE allows the model to activate only a portion of its parameters during inference, significantly improving computational efficiency.

• Total vs. Active Parameters: R2 reportedly has 1.2 trillion (1.2T) total parameters, but only activates 78B during actual operation, meaning only about 6.5% of parameters are involved in computation simultaneously.
• Resource Allocation Efficiency: This design reportedly enables efficient resource allocation for inference and multitasking, significantly reducing computational costs.
• Long-text Processing Advantage: For scenarios requiring processing of lengthy documents, such as legal and financial applications, this architecture may provide greater cost-effectiveness.

Multimodal Capabilities: Breakthroughs in Visual Processing

In terms of multimodal capabilities, R2 allegedly achieves 92.4 mAP on COCO dataset object segmentation tasks, representing an 11.6 percentage point improvement over CLIP. If true, this would represent significant progress in visual processing.

It's worth noting that this metric is substantially higher than those of currently recognized top-tier models, so we should maintain a cautious attitude while awaiting further independent verification.

Alternative Computing Architecture: Expanding Hardware Options

Perhaps the most strategically significant aspect of the leaked information is R2's optimization for alternative AI accelerator chips. Reportedly, R2 features a custom distributed training framework achieving 82% cluster utilization and 512 Petaflops FP16 peak performance, representing high efficiency compared to industry standards.efficiency of a similarly-sized NVIDIA A100 cluster.

If this information is accurate, it would represent an important step in diversifying the global AI hardware ecosystem. However, due to the lack of sufficient technical details and independent verification, this claim should also be approached with caution.

Disruptive Pricing Strategy: The Possibility of AI Democratization

Perhaps the most striking aspect of the leaked information is R2's pricing strategy. Reportedly, R2's inference pricing is $0.07 per million input tokens and $0.27 per million output tokens, 97.3% lower than GPT-4o.

If accurate, this price level would have profound implications for the AI service market:

• Lower Enterprise Adoption Barriers: Dramatically reduced costs could enable more small and medium-sized businesses to afford AI services, expanding use cases.
• Developer Ecosystem Expansion: Low-cost APIs could attract more developers to enter the field of AI application development.
• Intensified Market Competition: This could force mainstream providers like OpenAI and Google to reconsider their pricing strategies.

If Deepseek R2's pricing strategy proves accurate, it could become the "Tesla moment" for AI services—bringing high-end technology to a broader market through technological innovation and business model transformation.

Performance Evaluation: Claims versus Reality

According to leaked evaluation data, Deepseek R2 allegedly achieves 89.7% accuracy on the CEval2.0 standard instruction set evaluation, with the community describing it as having "performance comparable to GPT-4-Turbo."

However, without independent third-party verification, these figures merely represent claims from the leaked information. There is typically a long transition period between technology validation and commercial implementation, so for R2's actual application prospects, we need more substantive cases and data.

Vertical Domain Applications: Potential for Specialization

The leaked information mentions that R2 shows significant advantages in professional fields such as finance, law, and patents, possibly benefiting from its reportedly massive 5.2PB training dataset with special enhancement for professional domain content.

This direction aligns with current AI development trends—evolving from general models toward specialization and verticalization to meet the deep needs of specific industries.

Information Credibility Analysis: Maintaining Rational Judgment

As technology observers, we have a responsibility to analyze the credibility of leaked information. The current situation presents several noteworthy points:

• Cross-verification of Information Sources: These leaks appear on multiple platforms (Reddit, X.com, etc.), but the content highly overlaps, possibly originating from a single source.
• Technical Feasibility Concerns: Community discussions focus on the technical feasibility of combining high performance with extremely low cost, as well as the actual efficiency of advanced MoE routing logic in large-scale inference scenarios.
• Original Leaker's Attitude: Notably, the original leaker on X.com also suggested "rational consideration" and waiting for official verification.

Credibility Advisory: In the absence of official confirmation and independent testing, all technical specifications and pricing information mentioned in this article should be considered unverified rumors, and readers should maintain rational judgment.

Potential Impact: What Happens If It's True?

Assuming the core information about Deepseek R2 is accurate, it could have the following impacts on the AI industry:

• Price Restructuring: Could trigger comprehensive price reductions for AI services, changing the current market landscape.
• Architectural Innovation: Might encourage more research teams to focus on hybrid architectures like MoE, rather than simply pursuing increased parameter scale.
• Technology Democratization: Low-cost, high-performance AI services could become catalysts for innovation, driving more AI applications from concept to reality.
• Technology Ecosystem: If R2 truly achieves efficient operation on alternative chip architectures, it could accelerate diversity in global AI hardware development.

Conclusion: Anticipation and Caution Coexist

The leaked information about Deepseek R2 presents a potential technological breakthrough point that could redefine the value proposition of AI services through architectural innovation and business model transformation. However, in the absence of official confirmation and independent verification, we should maintain rational judgment.

Technological innovation often advances amid questioning and anticipation, and the truth will ultimately emerge through practical applications and independent verification. We will continue to follow Deepseek R2's subsequent developments, bringing timely and accurate analysis to our readers

Social Media Sources

1. deedydas [@deedydas]. (2025, April 27). DeepSeek R2 technical specifications and performance metrics [Tweet]. X.com. https://x.com/deedydas/status/1916160465958539480
2. sagevedant [@sagevedant]. (2025, April 27) . Benchmark results for DeepSeek R2 model [Tweet]. X.com. https://x.com/sagevedant/status/1916162566503497876
3. bindureddy [@bindureddy]. (2025, April 27) . Open source development in AI models [Tweet]. X.com. https://x.com/bindureddy/status/1916171873941131453