Woofun AI reports that the release of DeepSeek V4-Flash has fundamentally altered the economic baseline for AI application entrepreneurs, shifting the industry focus from speculative model competition to tangible product business realities. The core phenomenon driving this change is not merely a new model launch, but the exposure of the underlying 'token reselling' mechanics that define current AI app economics. By drastically reducing the cost of inference, DeepSeek has forced a re-evaluation of value creation in the sector, moving the bottleneck from access to intelligence toward the execution of specific user workflows.

The technical specifications and pricing mechanics of DeepSeek V4-Flash, released for public testing on July 31, establish a new low-cost benchmark. The API is priced at 1 yuan per million input tokens and 2 yuan per million output tokens, with a significant reduction to 0.02 yuan per million input tokens upon cache hits. The model supports a context window of up to 1 million tokens, along with thinking mode, tool calls, and the Responses API, allowing for a maximum of 2,500 concurrent connections per account.

DeepSeek has also announced future peak-valley pricing, which will double costs during high-usage periods, though the base rates remain exceptionally low. According to DeepSeek’s estimates, one Chinese character consumes approximately 0.6 tokens, meaning 1 million tokens can process around 1.67 million Chinese characters. Considering only input costs, processing this volume of text costs just 1 yuan, a figure that drastically lowers the barrier for high-volume text processing tasks.

The economic reality of AI applications has long been obscured by the hype surrounding model capabilities, but the underlying business model remains rooted in the retail of inference capabilities. In a recent debate with ChatGPT, the argument was made that the viable business model for AI apps is essentially token reselling, a term the AI attempted to soften with euphemisms like 'encapsulation of intelligent services.' However, the core mechanic is unchanged: purchasing, processing, pricing, and selling.

Unlike traditional software or SaaS products, where marginal costs for new users diminish with scale due to fixed development costs, AI applications incur a direct cost for every user action. Every time a user generates an image, writes a report, or prompts an Agent, the application company must call the model and pay for the tokens consumed. This structure means that bandwidth, storage, and service costs are secondary to the primary expense of model inference, making the procurement and resale of tokens the central activity of most pure software-based AI ventures.

Case studies in the industry illustrate the prevalence of these token reselling models. Poe, for instance, explicitly bases its pricing strategy, which includes a points system, on the costs charged by underlying model providers. Poe converts upstream token costs into internal points, selling them to users alongside unified access, payment options, and model switching interfaces. Similarly, Intercom, a customer service software company, prices its AI service Fin at $0.99 per result. A 'result' is defined as a resolved issue confirmed by the user or a completed workflow by Fin.

While this appears to be a shift from token-based to outcome-based billing, it is merely a change in labeling; behind the scenes, Intercom still calculates the tokens required to complete each delivery. This mirrors the historical role of vendors like those depicted in Li Song’s Southern Song Dynasty painting 'Market Vendor with Children Playing,' where the vendor’s profit came not from producing goods, but from selecting, organizing, and delivering them. AI application companies similarly integrate intelligence produced by model manufacturers into specific scenarios, selling completion rather than raw capability.

Woofun AI data shows that profit in this business model derives from three distinct sources: procurement difference, usage difference, and product difference. Procurement difference arises when teams with high usage volumes negotiate discounts or employ engineering techniques like caching, asynchronous processing, peak-valley scheduling, and model routing to reduce average costs. If one team can complete a task for 50 cents while another pays 1 yuan, the 50-cent margin represents pure profit from procurement efficiency. Usage difference exploits the gap between estimated and actual consumption, similar to gym annual passes or phone data plans. If the average consumption of a user base is lower than the plan’s design, the unused quota becomes profit.

However, this profit is contingent on flexible upstream procurement; if a company has locked in an annual contract, unused tokens become overstock rather than accumulated balance for the user. Product difference reflects the value added through processing, where the same 10,000 tokens might yield a rough draft requiring extensive user revision or a polished, verified, and formatted final deliverable. The latter commands a higher price, justifying the product’s existence beyond simple token resale.

The cost-control paradox in Agent products highlights the tension between capability and expense. The official user guide for Manus, for example, advises users to use Chat mode for simple questions to avoid activating a full autonomous Agent, and to check intermediate results for complex tasks to prevent wasted points. This guidance reveals a fundamental challenge: the more proactive an Agent is, the harder it is for product companies to predict costs. A chatbot that answers once has a calculable cost, but an Agent that breaks down tasks, searches repeatedly, opens web pages, and calls tools can trigger dozens of model calls behind a single user command.

Consequently, products begin to teach users how to minimize the Agent’s workload, effectively acting as an 'accountant' that prioritizes cost control over capability. This leads to an awkward inversion where users buy AI to do more, but application companies force models to do less to manage expenses. When upstream prices rise, application companies reduce quotas; when competitors secure lower costs, membership fees drop; and when new models appear, routing and packages must be recalculated. Thus, despite controlling the product interface, application companies remain heavily dependent on upstream providers for the most critical variables.

DeepSeek’s technical value extends beyond its low price, offering a stable and capable foundation for applications. The preview version, released in April, claimed inference capabilities comparable to V4-Pro, with similar performance in simple Agent tasks. The official version updated on July 31 did not change the model structure or scale but involved retraining, resulting in performance on various Agent benchmarks that surpassed the V4-Pro preview version.

This is significant because it demonstrates that low cost does not necessitate poor quality. For AI applications, what is truly needed is stable intelligence at a given price point. Based on the Agent and coding benchmarks published by DeepSeek, V4-Flash has surpassed the 'adequate' threshold for many high-frequency tasks. Its low price makes it feasible for teams to use it frequently without fear of prohibitive costs, allowing them to integrate intelligence into products with greater confidence and less reliance on speculative model upgrades.

Market perception is shifting in a manner analogous to the rise of Pinduoduo in e-commerce. In its early days, Pinduoduo did not just break down product prices; it revealed that many daily necessities could be sold at significantly lower costs, forcing brand premiums, channel costs, and intermediaries to justify their existence. DeepSeek is performing a similar function for intelligence. It has not made all models cheaper, but it has turned expensive models into products that require justification for their higher prices. In the past, model companies could naturally claim that stronger models warranted higher costs. Now, that logic is under scrutiny. The industry must clearly discuss where extra costs come from, what problems additional capabilities solve, and how many users truly need them.

This shift forces a reassessment of value, moving the focus from raw parameter counts to practical utility and cost-effectiveness.

Strategic independence for application teams is becoming possible as underlying models stabilize. In the past, rapid model updates left application companies easily led by upstream providers, constantly chasing new contexts, inference models, and Agent capabilities. This created a dynamic of upstream prosperity while downstream companies were distracted, with product roadmaps determined by model releases rather than user needs. Now, with models like DeepSeek V4-Flash offering stable and affordable performance, teams no longer need to treat 'integrating the latest model' as the primary product advancement.

This stability allows products to focus on integrating tasks into real workflows, handling permissions, data, collaboration, review, and failures. These aspects, while less eye-catching than model parameters, are critical for user retention and satisfaction. The value of AI applications lies in making models disappear into the work itself, so users no longer need to choose models, study prompts, or worry about inference rounds. Competition thus shifts from who gets the model first to who understands the task better and can embed industry-specific unspoken rules into the product.

Intelligence is becoming a commodity, while skills and workflow integration are emerging as the true barriers to entry. After DeepSeek lowered the threshold for intelligence, AI applications have shifted from a model competition back to a product business. As Chao Cuo wrote in 'On the Value of Grains' during the Western Han Dynasty, 'Value grains highly and undervalue gold and jade.' Over the past few years, large models have been like gold and jade—precious, visible, and displayed under lights, yet difficult to use in daily work.

The application layer’s role is to turn this gold and jade into grains, making intelligence accessible and useful. DeepSeek has not prepared the food for entrepreneurs; it has simply reduced the price of rice. Who can open a restaurant and succeed depends on their skills, their understanding of users, data, processes, and trust. Small teams can now serve specific niches, such as foreign trade inquiries or chain store processes, without needing to pursue massive user bases or grand narratives to cover inference costs.

The market does not need to be huge to accommodate everyone; as long as the problem is specific, the delivery is stable, and the value exceeds the price, the business can survive. Ultimately, the scarcity is shifting from models to the ability to solve real-world problems, giving those closer to actual work more opportunities in the evolving landscape.