In 2026, China's large model will welcome its "first year of going global," with its open weights and high cost-performance ratio frequently topping the charts on major aggregation platforms. The "easy to use and affordable" Chinese large model is becoming a viable option for overseas developers and enterprises.

However, beneath the lively rankings, a more concerning issue for the industry is emerging. Survey data reveals that while the overall usage of Chinese large models on the overseas aggregation platform OpenRouter is growing, the original manufacturer's own token share is significantly declining. The reason is that when the same model is concurrently hosted by numerous third-party providers, the calls are distributed layer by layer, leading to a dilution of the original manufacturer's usage and revenue. Additionally, the default "automatic routing" mechanism of such platforms further amplifies this effect: if users do not make any settings, requests will flow to the cheapest service provider based on price or latency. Thus, "increment without revenue growth" has become the most realistic challenge for Chinese large models going global.

When traffic no longer equals revenue, what Chinese large models truly need is a channel to return value to the original manufacturer.

B.AI, as a new generation of globalization AI infrastructure, offers a different answer. Unlike the traditional "automatic routing" mechanism, B.AI creates official APIs as independent, transparent, user-chosen exclusive lines, preventing the original manufacturer's token share from being quietly diluted. Moreover, relying on in-depth ecological cooperation with large models to release token subsidies, it ensures that the price of official channels remains competitive. By adding a multi-currency aggregation payment engine, the friction of cross-border transactions is cleared. More profoundly, with solid user accumulation, B.AI has successfully captured a massive user base and officially entered the trillion-token throughput era. B.AI is no longer just a channel for going global, but rather a super hub connecting global enterprise demands and the era of intelligent agents.

Behind the Dominant Usage: The "Increment Without Revenue Growth" Dilemma of Chinese Large Models

The commercial monetization dilemma of "increment without revenue growth" for Chinese large models going global has recently been visually corroborated by industry survey data.

According to a tracking report released by the well-known AI think tank Epoch AI, the overall usage of Chinese large models on the overseas API aggregation platform OpenRouter is showing exponential growth, but this massive "increment" has not proportionally translated into "revenue" for the model's original manufacturers. This is not an isolated case but a common phenomenon faced by leading Chinese large models during their internationalization process.

Taking Zhipu GLM-5.3 Flash as an example, the competition from the same source has led to severe traffic diversion. The report indicates that the model was made available for weights for just one day, and the number of third-party hosting providers quickly rose to 12, reaching 26 within 20 days, with 8 providers offering lower prices than Zhipu. During this period, the overall usage of GLM-5.3 Flash on OpenRouter grew by 17%, but Zhipu's average daily token usage dropped by 70%, with its processed model token share plummeting from 88% to 22%. The total usage of GLM increased, but more calls were handled by other providers.

Epoch AI's macro-estimation data further validates this structural pain point. By analyzing estimated customer spending (annualized) from August 27 to September 19, 2026, on OpenRouter, it was found that the vast majority of spending directed towards Chinese large models flowed to third-party hosting providers rather than the model developers (original manufacturers), with the actual revenue share received by the original manufacturers being just a tiny portion of the total spending.

This means that Chinese large model enterprises bear the core costs of model training and computing power iteration, but in the commercial monetization chain, they have not been able to capture commercial benefits that adequately match their technical contributions, which is compounded by several structural factors.

Firstly, there is supply homogeneity: when model weights are open and the same model can be deployed by multiple service providers, the inference services are highly homogeneous, with the competitive focus naturally shifting to price and latency. Epoch AI's research also found that the higher the pricing premium of the original manufacturer for the open-weight models, the smaller the share of its own tokens handled on OpenRouter.

Secondly, the "default routing" mechanism exacerbates the issue: aggregation platforms usually distribute requests based on the lowest price or latency, and if users do not make additional settings, traffic will flow to the lowest-bidding hosting provider, rather than the original model manufacturer.

Thirdly, there are access and payment barriers: overseas users facing friction in cross-border payments, currencies, and account systems tend to prefer more convenient aggregation channels, making it difficult for the original manufacturers' direct connections to form scale.

For large model manufacturers, the key to breaking the impasse does not lie in stopping the widespread distribution of models through multiple channels, as openness and sharing are inherently the "accelerators" for Chinese large models to go global. What truly allows value to return to the original manufacturers is establishing a clear, transparent, low-cost, and user-choosing official channel, while also lowering the usage and payment barriers for global users. In other words, the industry needs not another "cheaper transit station," but rather a set of infrastructure that respects user choice and enables value to flow back to the original manufacturer.

Returning Choice to Users: B.AI Makes "Official Direct Connection" a Visible Channel

To solve the industry's dilemma of "increment without revenue growth," reshaping the connection between users and models is the first step in reconstructing the commercial order. To make commercial value clear and visible, B.AI's solution starts from the very foundation of product architecture, completely returning the "final choice of routing" to users.

In B.AI's system, "official direct connection," as an independent, transparent channel, is left for users to choose. In scenarios where there are clear demands for model performance and stability, users can directly choose to connect to the original manufacturer's API, ensuring extremely low latency under high concurrency, while meeting stringent data security and compliance requirements. For scenarios where cost and flexibility are more sensitive, users can opt for other channels as needed. This "user-defined" transparent mechanism brings significant direct bilateral commercial value to the entire ecosystem.

For original manufacturers, this effectively prevents the diversion of core token shares. When enterprise-level customers' high-concurrency and high-reliability requests reach the original manufacturers through dedicated channels, the token share and profit margins that the original manufacturers deserve are guaranteed. For developers, this means ultimate business certainty. Choosing the official channel means bidding farewell to performance fluctuations and "intelligence reduction" blind boxes, and being able to stably obtain the "full-capacity" model capabilities.

Moreover, it is worth mentioning that this transparent direct connection mechanism has gained widespread recognition among the leading factions of Chinese large models. Currently, B.AI has established deep ecological cooperation with many top Chinese large models such as Zhipu, MiniMax, Kimi, Xiaomi Mimo, Qianwen, and DeepSeek, jointly promoting these high-quality "original manufacturer dedicated lines" to global developers. This not only validates the commercial viability of the B.AI model but also signifies that the internationalization of large models is evolving from "pursuing low prices" to a healthy ecosystem that "values quality and commercial sovereignty."

However, giving choice back to users also needs to address a practical question: will the official channel be more expensive? On this point, B.AI relies on the platform's enormous scale effect, continuously launching a series of limited-time promotional activities in collaboration with large models to continuously release ecological dividends. In these activities, the maximum subsidy even reaches complete free access. Through real financial ecology benefits, B.AI successfully plays the card of "lower prices without lower intelligence." Developers can access 100% native full-capacity official model capabilities at a comprehensive cost even lower than that of third-party hosting, without bearing steep premiums. Users have completely bid farewell to the dilemma of compromising between "price and performance."

More importantly, this "direct connection + subsidy" deep cooperation model has commercial values that extend far beyond short-term customer acquisition. In the past, some models going global were often seen by overseas markets as merely "cheap computing substitutes." Now, B.AI, leveraging its strong ecological aggregation ability, is helping domestic large models establish real brand loyalty abroad. By binding the high-quality services of official original manufacturers with extreme cost-performance ratios, B.AI is helping Chinese models shake off the stereotype of being mere "cheap computing pools," significantly enhancing their strategic positioning in the global AI industry chain.

Beyond Channels: Global Settlement, Trillion Throughput, and Strategic Layout for the Agent Era

For overseas developers, the smooth usability of channels also depends on payments. In its overall architecture, B.AI not only provides connections for computing power and models but positions itself as an "intelligent global settlement layer," further extending its ecological carrying capacity.

As an intelligent global settlement layer, B.AI's payment engine achieves high compatibility within a compliant framework. It has not only horizontally integrated local settlement methods such as Visa, Apple Pay, Alipay, and WeChat Pay but also vertically accessed seven mainstream blockchain networks, supporting various digital assets. This dual-track parallel design allows both traditional Web2 enterprise clients and native Web3 developers to complete recharges, subscriptions, and calls on the same platform with minimal access friction. Currently, over 50% of payments on the B.AI platform are made through traditional fiat channels such as Stripe.

This combination of breadth and depth in global settlement infrastructure signifies that B.AI has completely broken the limitations of traditional "technical transit stations," rising to become a super hub that reshapes the commercial landscape of large models. It not only enables top AI computing power to seamlessly integrate into the global commercial cycle but also, through its strong ecological adhesive power, consolidates dispersed global enterprise demands into a value network with tremendous commercial explosive potential.

Supporting the efficient operation of this vast settlement network is the platform's robust scale base. Currently, B.AI's daily throughput has exceeded one trillion tokens, with a user base nearing three million. Notably, it reached the scale level achieved by the early aggregation platform OpenRouter in about 100 days after its launch—an achievement that OpenRouter took nearly two years to reach. Through deep cooperation with large model manufacturers, the B.AI platform fully supports over 70 mainstream large models, building an advanced multi-modal ecological matrix. This massive business volume and well-established ecological network mean that B.AI has substantively established its core hub position as a new generation of global AI infrastructure.

When "official direct connection" safeguards the original manufacturers' share of computing power, and "token ecological subsidies" build an ultimate customer acquisition advantage, in conjunction with a trillion-level underlying throughput network and global settlement system, B.AI has woven a complete ecological closed loop for Chinese large models, extending from "demand reaching" to "commercial monetization." Through this mechanism, Chinese large model manufacturers can overcome the "increment without revenue growth" consumption battle, maximizing the value of their global endeavors in a stable, transparent, and highly explosive commercial soil.

Finally, on the foundation of payment settlements and scaled throughput, B.AI is also looking towards the more cutting-edge agent economy. The platform's long-term vision is not limited to the model routing layer but aims to deeply integrate computing power routing, decentralized settlements, and full-stack agent infrastructure. Currently, B.AI has deployed tools such as Agent Wallet and Skills, fully empowering agents to autonomously handle complex tasks across different environments. At the protocol layer, the platform has integrated x402 payment protocols and 8004 identity verification protocols, providing standardized trust and settlement support for high-frequency, micro-amount automated calls between machines in the future. As the subject of API calls gradually shifts from human developers to automated agents, this intelligent settlement layer and execution hub that B.AI has laid out in advance will become a crucial node connecting foundational models with the future trillion-level agent ecology.

In this new era driven by large models and agents, B.AI will become the most certain "super router" in the globalization journey of Chinese large models. It not only safeguards the maximization of commercial value for Chinese models but also connects global demands, computing power, and intelligent agents, propelling the global AI industry towards the next prosperous cycle with a transparent, efficient, and win-win commercial closed loop.

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