Seek .(US:SKLTY) Hu Xiu·2025-10-10 00:09Core Insights - The article discusses the efficiency and cost implications of open-source models like DeepSeek-R1 compared to closed-source models, particularly focusing on token consumption and its impact on overall reasoning costs [2][19]. Token Consumption and Efficiency - A study by NousResearch found that open-source models, specifically DeepSeek-R1-0528, consume four times more tokens than the baseline for simple knowledge questions, indicating significant inefficiency in straightforward tasks [2]. - For more complex tasks such as math problems and logic puzzles, the token consumption of DeepSeek-R1-0528 is reduced to about twice the baseline, suggesting that the type of question posed significantly affects token usage [3][6]. AI Productivity Index - An independent study by AI recruitment unicorn Mercor noted that models like Qwen-3-235B and DeepSeek-R1 have longer output lengths compared to other leading models, which can enhance average performance at the cost of increased token consumption [5]. Economic Value of Tokens - The economic value of tokens is determined by the model's ability to solve real-world problems and the monetary value of those problems, emphasizing the importance of creating economic value in practical scenarios [10]. - The unit cost of tokens is crucial for the economic viability of models, with companies like NVIDIA and OpenAI exploring custom AI chips to reduce inference costs [10]. Hardware and Software Optimization - Microsoft’s research highlighted that actual energy consumption during AI queries can be 8-20 times lower than estimated, due to hardware improvements and workload optimizations [11]. - Techniques such as KV cache management and intelligent routing to appropriate models are being explored to enhance token generation efficiency and reduce consumption [11]. Token Economics in Different Regions - There is a divergence in token economics between China and the U.S., with Chinese open-source models focusing on achieving high value with more tokens, while U.S. closed-source models aim to reduce token consumption and enhance token value [15][16]. Environmental Impact - A study indicated that DeepSeek-R1 has the highest carbon emissions among leading models, attributed to its reliance on deep thinking and less efficient hardware configurations [18]. Overall Cost Advantage - Despite the higher token consumption, open-source models like DeepSeek still maintain a cost advantage overall, but this advantage diminishes at higher API pricing levels, especially for simple queries [19]. Conclusion on AI Economics - The pursuit of performance is overshadowed by the need for economic efficiency, with the goal being to solve valuable problems using the least number of tokens possible [20].