Author｜Yang Licheng

Editor｜Chen Xiaoran

On the eve of the May Day holiday, Alibaba Cloud discreetly updated the pricing list on its large model service platform, BaiLian. Without much fanfare, this move precisely tackled the pain points faced by all enterprises that frequently utilize large models.

Dramatic Price Reduction: Up to 90% or More

Starting from 23:59:59 Beijing time on April 29, 2026, the billing rate for implicit caching of the DeepSeek-V4-Pro model has been officially slashed to 1 yuan per million Tokens.

For those uninitiated in large model billing, this figure may not seem particularly striking. However, when juxtaposed with current industry norms, its significance becomes apparent. After all, the standard input prices for mainstream large models typically range from 10 to 80 yuan per million Tokens. This price cut is tantamount to offering a 90% discount or even more on repeated computations.

The rule design is both precise and measured. Only input Tokens that successfully hit the cache are billed at the reduced rate, while input Tokens that miss the cache, all output Tokens, and the base inference price of the model remain unchanged.

Implicit caching does not necessitate any additional configuration by developers. The system automatically identifies common prefixes in requests and reuses computation results, specifically targeting scenarios with high context repetition rates, such as multi-round dialogues, RAG knowledge base queries, and batch processing of fixed instructions.

This move is not merely a promotional stunt but a strategic signal to the industry. The price war for large models has bid farewell to the simplistic competition of "who is cheaper" and has officially entered a new era of refined competition, focusing on "who possesses stronger actuarial capabilities."

To grasp the impact of this move, one must first understand Alibaba Cloud BaiLian's current ecological niche and the underlying shifts in the industry.

One-Stop Service: A Comprehensive Solution

As Alibaba Cloud's core MaaS (Model as a Service) platform, BaiLian has evolved beyond merely selling self-developed models. Instead, it positions itself as the "operating system for large models," integrating mainstream domestic and international models such as Tongyi Qianwen, DeepSeek, Kimi, and GLM. It provides enterprises and developers with a one-stop solution, encompassing unified APIs, fine-tuning, deployment, and operations and maintenance.

However, the MaaS industry is rapidly evolving, with the open-source movement, exemplified by DeepSeek, quickly dismantling the technological barriers of foundational models. Vendors can no longer rely on the claim of "my model is superior to yours" to retain customers. The true competitive advantages now lie in engineering cost-reduction capabilities and ecological stickiness.

The implicit caching price reduction precisely targets these two critical areas. Technically, cache hits translate to a significant reduction in Alibaba Cloud's computational power consumption. This price reduction space is achieved through the combined effects of scale and optimization of underlying scheduling technologies, rather than through subsidies that burn money.

Ecologically, it directly addresses the "wasted tax on repeated calculations" that developers find most frustrating. In typical scenarios such as RAG and intelligent customer service, cache hit rates often exceed 60%, and in some stable businesses, they can even surpass 90%. This translates to a direct reduction in actual model usage costs by 70%-90%.

Choosing DeepSeek-V4-Pro as the vehicle for this price reduction is also a shrewd strategic move by Alibaba Cloud. This model has already gained a reputation among developers for being "effective and affordable," thanks to its 1M ultra-long context and low inference costs enabled by its MoE architecture. Leveraging its traffic can maximize the market impact of the price reduction.

This seemingly minor billing adjustment is, in fact, a significant milestone in the commercialization process of large models. The industry value, development trends, and potential risks behind it warrant in-depth analysis.

Core Value: Lowering the Barrier to Entry

From a core value perspective, the cache price of 1 yuan per million Tokens truly propels large models a significant step closer to becoming infrastructure akin to "utilities like water, electricity, and gas." It substantially lowers the trial-and-error threshold for small and medium-sized enterprises and developers, enabling many business models that were previously confined to PowerPoint presentations due to high costs—such as 24/7 unmanned intelligent customer service, automated generation of financial research reports, and continuous inspection of large-scale codebases—to suddenly achieve positive ROI.

Expected to Solidify Leading Position

In terms of industry trends, the pricing system for large model APIs is comprehensively aligning with mature cloud computing practices. In the future, structured pricing for features like streaming output, asynchronous calls, batch inference, and cache discounts based on different hit rate gradients will become the norm. Actuarial capabilities will become a core competitiveness for cloud vendors.

Regarding market opportunities, AI-native applications in vertical industries are poised for explosive growth. The storage industry chain, including enterprise-grade SSDs, will also benefit from the popularization of cold data caching technologies. Alibaba Cloud, in turn, is expected to further solidify its leading position in the MaaS market by leveraging its cost advantages.

Potential Risks: Not a Panacea

However, it is worth noting that the potential risks behind this price reduction should not be underestimated. The benefits of cache price reductions do not cover all scenarios. For open-ended Q&A needs with scattered dialogue content and no fixed prefixes, the hit rate of implicit caching is almost negligible. This could easily lead the market to misjudge and expect "low prices for everyone," resulting in expectation biases.

At the same time, the ultra-low prices pose a dual test of underlying computational power operation and maintenance capabilities and cache algorithm optimization. If the platform strictly controls costs at the expense of service quality, leading to issues like response delays and cache failures, it could erode the brand reputation and user trust it has accumulated.

Moreover, it is important to recognize that the cost advantages brought by caching technology are not a long-term barrier but essentially a phased dividend. As the inference efficiency of large models continues to improve and more precise models like explicit caching are continuously optimized, the marginal benefits brought by implicit caching will eventually gradually weaken.

In conclusion, the trump card in the large model price war has been revealed. It is no longer a simple numerical competition but rather about who can make their prices seem "smartly cheap" in the complex pricing maze. And this competitiveness hidden in the actuarial tables will ultimately settle as ecological barriers for whoever possesses it. That is the truly exciting show to anticipate next.