The commercialization process of global large language models is approaching a critical deflationary turning point. Last weekend, China's DeepSeek open-sourced its V4 series models and implemented an unprecedented rate reduction strategy, compressing the cost of one million tokens to mere cents. This move has shattered the industry guide price established by a few leading North American companies, making the use cost of their core products nearly 97% lower than equivalent services from OpenAI. This de-dimensional pricing, based on algorithm optimization and underlying hardware synergy, not only significantly reduces the technological barriers for industries to access AI but also triggers widespread competition regarding the re-evaluation of computing power value across the industry chain.
Competitive Landscape
The competitive landscape of the current large model track is rapidly evolving from a "parameter competition" to a "inference cost war." The entry of DeepSeek's V4 version substantially raises the performance benchmark for open-source ecosystems. In agent capability and code generation scenarios, the V4-Pro version has exhibited better empirical feedback than the Anthropic camp's Claude Sonnet 4.5, and in broader STEM and mathematical quantitative evaluations, its performance closely rivals leading international closed-source models. In terms of world knowledge, only Google's (GOOGL:US) Gemini-3.1-Pro maintains a slight lead. However, its cost to complete standard tests is only about one-fortieth of Claude Opus 4.7. This extreme cost-value disparity is dismantling the previous duopoly monopoly, forcing leading companies including OpenAI to face passive pressure to follow suit in future product pricing.
Industry Chain Transmission
The pricing storm at the model layer is accelerating its transmission to downstream application sectors along the industry chain. For middle-layer application developers (AI Agent/SaaS), a 97% drop in inference costs means an enormous release space for their product gross margins, which will give rise to numerous business models that previously could not be feasible due to high invocation costs (such as high-frequency automated customer service, real-time immersive translation of lengthy texts, etc.). In response to the explosion of data volume on the application side, the OpenRouter platform recorded a single-day massive throughput of 13.6 billion tokens, a fourfold increase month-on-month. This massive concurrent demand will impose reverse pressure on cloud service providers, requiring a new round of capital expenditure on data center network architecture and load balancing to handle the surge in API requests.
Computing Power Base and Domestic Substitution Closed Loop
Achieving such extreme cost control is not merely a commercial subsidy but a reconstruction dividend at the underlying technical stack. The commercial landing of DeepSeek V4 is deeply integrated into Huawei's Ascend hardware ecosystem. By targeting the sparsity design of the model architecture with the tensor computation units of Ascend's super nodes, the utilization of GPU memory bandwidth has been significantly improved. This path of moving beyond the dependence on a single software and hardware ecosystem signifies that domestic computing power now possesses the engineering capability to support world-class large model high-concurrency inference. If this collaborative strategy can maintain high availability in complex agent tasks, it will accelerate the strategic migration of core businesses to localized computing power bases for domestic government and enterprise clients.
Commercialization Path and Long-term Profitability Restructuring
Driving API prices to approach the marginal cost of hardware is an extreme exploration of DeepSeek's commercialization path. By open-sourcing at a meager cost and providing low-priced APIs, its core strategy is to quickly capture market share, monopolizing the operating habits of incremental developers and the enclosed application data. However, this poses a challenge to the entire industry's long-term profitability expectations. If the inference services of foundational models evolve to become undifferentiated utilities, future revenue growth for large model companies will not be able to rely on simple computing power sales but must instead transition to deeply customized solutions, private enterprise deployments, and high-value-added industry-specific data authorization.
