In February 1945, the three major powers of the United States, the United Kingdom, and the Soviet Union convened on the Crimean Peninsula to establish the rules of the game for the following half-century. Major powers delineated spheres of influence, smaller nations accepted their assigned roles, and once the system was entrenched, latecomers found it nearly impossible to overturn. Historians refer to this as the "Yalta System."
On the third Thursday of April 2026, the global AI industry reached a similar inflection point.
Within a 24-hour span, from GPT-5.5 to DeepSeek-V4, what appeared to be iterative updates to several models on the surface was, at a deeper level, a contest over pricing power and technological dignity.
Two distinct paths were clearly demarcated at this moment: one represented by OpenAI's dominance in computational power and its harvesting of pricing power, and the other by DeepSeek’s pursuit of algorithmic efficiency and ultimate inclusiveness.
This can no longer be simply regarded as a continuation of a technological race; rather, it marks the starting point for the reconstruction of the global artificial intelligence industrial order. The rules are being written, and participants can only choose which side to stand on.
01 Silicon Valley's Calculations
The true divergence of GPT-5.5 lies not in its parameter scale but in its fundamental leap toward agent-based capabilities.
OpenAI unveiled a key metric. In internal tests of Expert-SWE, where humans require 20 hours to complete long-cycle engineering tasks, GPT-5.5 achieved end-to-end autonomous repair. It no longer merely completes code but possesses a “system shape understanding” capability. It can comprehend dependency relationships within vast codebases and anticipate whether a single-line change might cause another module to fail. Coupled with multi-modal computing abilities, it can navigate across software, read screens, click UI elements, run tests, and independently close debugging loops. The role of AI is transitioning from a passive tool to an active collaborator.
Such capabilities have already permeated OpenAI’s internal operations. The finance team used it to process 24,000 tax forms, totaling 71,000 pages, compressing months of work into two weeks. Each member of the marketing team saves 5 to 10 hours per week.
The academic community has also been shaken. GPT-5.5 proposed an asymptotic proof for the century-old challenge in combinatorial mathematics, the “off-diagonal Ramsey constant,” which has passed rigorous scrutiny through Lean formal verification. It has moved from retrieving known knowledge to exploring the unknown.
One more thing deserves attention. To enhance inference efficiency on NVIDIA's GB200/300 systems, GPT-5.5 analyzed weeks of production traffic patterns and autonomously developed a set of dynamic load balancing and partitioning heuristic algorithms. Without compromising intelligence levels, the token generation speed increased by over 20%. AI has begun participating in the optimization of its own infrastructure. Once this closed loop is formed, the acceleration of technological iteration will exceed most people’s expectations.
However, the real impact of this release lies in pricing.
The API pricing for GPT-5.5 Pro is $30 for input and $180 for output, per million tokens. The previous industry ceiling, Claude Opus 4.7, was priced at $25 for output. GPT-5.5 has directly increased it sevenfold.
In real-world scenarios involving agent tasks, models need to continuously cycle, invoke tools, and repeatedly verify results. It is common for lightweight tasks to consume millions of tokens.
This means that the API threshold has been aggressively raised. Within just the first few steps of a task, thousands or even tens of thousands of dollars flow into OpenAI’s account.
But a more intriguing detail lies here. The pricing for ChatGPT’s Plus and Pro subscription plans has not changed—no price hikes, no service interruptions, no rate limiting. Users paying $20 per month for Plus can still access what is arguably the world’s most powerful model at an almost unreasonably low price.
This represents a meticulously designed adjustment to the commercial architecture. By setting sky-high API prices to redefine the industry ceiling, high-usage customers are effectively coerced into switching to subscriptions, converting fragmented API revenue into stable cash flow. On the other hand, affordable subscription fees help retain the core user base.
The market principle it conveys is cold: the cost and distribution of foundational models are dictated by computational power monopolists.
Section 02: China’s Path Forward
To understand the value of DeepSeek V4, one must return to a brutal starting point.
Due to export controls, Chinese AI companies cannot match OpenAI's chip matrix in terms of computing power reserves in the short term. The sensitivity of the Chinese people to being 'technologically strangled' stems from real challenges. Each instance of technological blockades ultimately points to the same solution: advancing deeply into algorithm optimization while constrained by hardware limitations.
DeepSeek’s strategic response is a continuation of this logic in the AI era.
The length of context represents a computational black hole for large models. Under traditional attention mechanisms, computational demand explodes quadratically with sequence length. This is precisely the technical root cause of why intelligent agents burn through money rapidly. With each interaction accumulating context, token consumption spirals out of control unnoticed.
DeepSeek V4 offers a solution combining CSA and HCA, a hybrid compressed attention mechanism. CSA compresses every m tokens’ KV cache into a single entry, then selects top-k entries via sparse attention for computation. HCA applies even more aggressive compression, executing dense attention after full compression. The model focuses on only the most critical features along the sequence dimension, achieving highly efficient information compression.
The results are quite impressive. For an ultra-long context of 1 million tokens and the V4 Pro model with 1.6 trillion parameters, the per-token inference computation is only 27% of the previous generation, with KV cache usage plummeting to 10%. They also abandoned the traditional AdamW optimizer, introducing the Muon optimizer and pioneering mHC manifold-constrained hyperconnections. Residual mappings are strictly constrained within doubly stochastic matrices, ensuring signals neither attenuate nor explode across hundreds of network layers.
Algorithmic shortcuts often come at a cost. However, the practical performance of V4 Pro nearly defies this assumption. On the Codeforces global ladder ranking, it ranks 23rd, matching GPT-5.4. For the first time in the history of open-source models, it stands shoulder-to-shoulder with closed-source top-tier models on this list. DeepSeek employees have fully adopted it for agent programming, reporting an experience surpassing Claude Sonnet 4.5 and approaching Opus 4.6.
Innovations are also evident in post-training. Moving away from costly traditional RLHF reward models, OPD policy distillation was proposed. Expert models in fields such as mathematics and programming were seamlessly integrated into V4 Pro via inverse KL divergence. Knowledge transfer completed an intergenerational upgrade in algorithms.
What truly stings the industry is the pricing strategy.
V4 Pro output price: 24 RMB per million tokens. The simultaneously released V4 Flash costs only 2 RMB. Cheaper than its predecessor, its performance is only slightly inferior to domestic top-tier models. In terms of cost-effectiveness, this moat will be difficult to challenge in the short term. Official technical reports suggest prices will drop further after the mass production of domestically produced Ascend 950 supernodes in the second half of the year.
To view this merely as a 'promotional discount' would be overly naive. Essentially, this represents a structural assault on the industry’s pricing system.
The subtext is clear. The value of foundational model services is not defined by the scarcity of computing power but determined by the efficiency of algorithms. In a hardware-constrained market, this path must be pursued. The competition for pricing power essentially reflects the struggle for market access.
03 The Final Revelation
The true lesson that the Yalta System imparted to history does not lie in who won or lost but rather in how, once rules are written, the room for maneuver available to latecomers becomes largely locked in.
Today, OpenAI has drawn a line through its control over pricing. Above this line lies what it defines as 'top-tier productivity,' charging rent based on computational costs. Below this line, DeepSeek has forcibly created an opening, recalibrating the threshold for inclusivity through algorithmic efficiency.
In the coming years, industrial evolution will likely remain within this framework. Some players will set standards from above, while others reconstruct rules from below. Those in the middle ground, lacking both pricing power and unwillingness to relentlessly optimize underlying efficiency, will ultimately resort to tweaking parameters behind the scenes and degrading user experience—doing unseemly things in a dignified manner.
The only question that Thursday truly answered was this: when computing power is no longer your card to play, can you still get a seat at the table? DeepSeek provided one answer, but whether this answer can endure depends on a deeper variable: when competitors next raise the bar, will algorithmic innovation keep pace?
This is the sobering aspect of the Yalta moment. It is far from being the endgame; it merely signals to everyone: the time to choose sides has arrived.
Editor/KOKO