Strategic Upgrade: Switching to DeepSeek V3.2 for Instruction Following
SOTA-RESEARCH Team
In a previous evaluation of DeepSeek V3.2 Exp, we conducted extreme stress tests—such as the 8-digit combination lock problem—and found that while it offered significant cost advantages, it fell short in complex reasoning tasks compared to dedicated reasoning models.
However, the landscape of our platform has evolved. We have decided to switch our primary Instruct Model from DeepSeek V3.1 to DeepSeek V3.2, while retaining DeepSeek R1 as the dedicated Reasoning Model.
The New Hybrid Architecture
Our upgraded architecture leverages the strengths of both models:
- Thinking Layer (DeepSeek R1): This model is responsible for generating the Chain of Thought (CoT). It breaks down complex user queries, performs reasoning, and outlines the logic required to solve the problem.
- Execution Layer (DeepSeek V3.2): This model receives the reasoning chain from R1 as part of its prompt context. It then executes the instructions, formats the output, and handles the final response generation.
Why We Made the Switch
This decision is driven by three key factors:
1. Cost Efficiency
DeepSeek V3.2 offers a significantly more attractive pricing structure compared to V3.1. By offloading the “heavy lifting” of reasoning to R1 and using V3.2 for the final instruction following and generation, we can optimize our total cost of ownership without sacrificing output quality. The bulk of the token consumption happens in the instruction phase, where V3.2’s efficiency shines.
2. Balanced Agent Capabilities
DeepSeek V3.2 has demonstrated a more balanced performance profile for Agentic tasks. In our internal benchmarks, V3.2 showed improved ability to handle function calling, tool use, and multi-turn conversations compared to its predecessor, making it an ideal candidate for the “front-end” of our AI agents.
3. Maturation of the DSA Mechanism
One of the critical technical improvements in DeepSeek V3.2 is the maturity of its DSA (DeepSeek Sparse Attention) mechanism.
While NSA (Native Sparse Attention) introduced a dynamic hierarchical strategy to balance global context with local precision, DSA takes this a step further. DSA offers a significantly more fine-grained sparsity pattern than NSA. By utilizing an advanced specific “indexer + top-k” selection pipeline, DSA can dynamically identify and attend to the most relevant tokens with even greater precision. This “finer-than-NSA” granularity allows DeepSeek V3.2 to process the massive, detailed reasoning chains from R1 with superior efficiency and accuracy, ensuring no critical logic is lost while maintaining low computational overhead.
Conclusion
By combining the raw reasoning power of DeepSeek R1 with the cost-effective, agent-capable, and technically mature DeepSeek V3.2, we believe we have found the optimal balance for the SOTA-AI platform. This hybrid approach ensures our users get the smartest possible answers at the most sustainable price point.