Chinas AI Giants Split Strategies Ahead of April Model Showdown

China's AI Titans Diverge in OpenClaw Frenzy as DeepSeek, Tencent Prepare for April Showdown

A seismic shift is underway in China's artificial intelligence landscape, driven by the explosive popularity of the open-source Agent framework OpenClaw. What began as a surge in valuations for a select group of large language model (LLM) developers is rapidly evolving into a strategic fragmentation, as companies chart starkly different paths to capitalize on the so-called "Agent元年" (Agent First Year). Concurrently, the industry braces for a pivotal April that will see the release of new models from two formidable players on divergent trajectories: the insurgent DeepSeek and the tech titan Tencent.

The OpenClaw Valuation Frenzy The catalyst for the current market euphoria is unmistakable. Since its release in November 2025, OpenClaw has amassed over 299,000 stars on GitHub, surpassing legendary open-source projects like Linux to become the platform's most-starred repository. Its real impact, however, lies in its "Token black hole" effect: AI Agents built on the framework can consume up to 15 times more tokens than traditional chat-based interactions, creating a sudden and massive surge in demand for inference computing power.

This demand has translated into vertiginous valuation climbs for Chinese LLM firms perceived as primary beneficiaries. The most dramatic rise belongs to Moonshot AI, creator of the Kimi chatbot. According to sources, the company is advancing an extension funding round aiming for up to $10 billion at a post-money valuation of $18 billion. This represents a more than fourfold increase from its $4.3 billion valuation in late 2025, achieved in under three months. The valuation logic is underpinned by explosive commercial data. Global payment giant Stripe reported that payment orders for Kimi's personal subscriptions surged 8,280% month-over-month in January 2026, followed by another 123.8% jump in February, placing it in Stripe's global top ten. An insider claimed that revenue in the 20 days following the late-January launch of its Agent product, KimiClaw, had already surpassed the total for the entirety of 2025.

In Hong Kong's public markets, the "OpenClaw concept" has triggered a parallel frenzy. Between February 1 and March 10, 2026, shares of MiniMax (00100.HK) skyrocketed 157%, while those of Zhipu AI (02513.HK) rose 186%. On March 10 alone, MiniMax surged 22.37%, briefly achieving a market capitalization of HK$382.6 billion, with Zhipu close behind at HK$289.3 billion. The core driver is the same: anticipation of soaring token consumption. MiniMax launched its MaxClaw Agent on February 26, requiring four service expansions within 120 hours due to demand. The company's first post-IPO earnings report for 2025 showed revenue of $79.04 million, a 158.9% year-on-year increase, with over 70% derived from international markets. CEO Yan Junjie disclosed that monthly revenue as of the end of February 2026 had nearly doubled compared to the 2025 monthly average.

Strategic Fork in the Road Beneath the unified valuation surge, the three companies' strategies are rapidly diverging in response to the OpenClaw opportunity, revealing distinct market positions and potential vulnerabilities.

Moonshot AI has pursued a "rapid productization" path for the consumer market. Its KimiClaw is positioned with a 199 yuan monthly subscription, targeting high-ARPU (Average Revenue Per User) customers. Its moat relies on user experience and first-mover advantage. However, as internet giants inevitably enter the Agent arena, maintaining that lead through relentless product iteration becomes paramount. The $18 billion valuation now places Moonshot in the global AI unicorn top tier, but pressure mounts to demonstrate that its revenue growth is sustainable beyond a short-term spike.

MiniMax is leveraging its core strength in multimodal capabilities. On March 9, it directly integrated VoiceMaker and MusicMaker features into MaxClaw, making advanced audio and video processing a standard offering. This represents a strategic differentiation aimed squarely at capturing the creator community, a move that has fueled its market enthusiasm. Yet, its stock price fell 6.48% on March 11, the day after its peak, highlighting the volatility inherent in its current hype-driven valuation.

Zhipu AI has adopted the most distinctive, enterprise-focused approach. On March 10, it launched AutoClaw (marketed as "澳龙" or "Australian Lobster"), promoted as a "one-click installable local version of OpenClaw." It comes pre-installed with Zhipu's Pony-Alpha-2 model, optimized for Agent scenarios, but crucially remains model-agnostic, supporting integration with competitors like DeepSeek, Kimi, and MiniMax. This strategy targets enterprise clients with stringent data privacy and security requirements, aligning with Zhipu's existing business-to-business revenue structure, where 85% of its 2025 revenue (exceeding $100 million) came from localized deployments. While potentially more defensible, this path likely leads to slower commercial scaling compared to consumer-facing products.

The April Crucible: Defining the Next Phase Even as the market digests the OpenClaw-induced volatility, the industry's attention is shifting to an impending April deadline that symbolizes the next phase of competition.

Two major releases are slated for the month: DeepSeek V4, the long-awaited multimodal model from the pioneering startup led by Liang Wenfeng, and a new iteration of Tencent's Hunyuan model, overseen by the recently returned Tencent Chief AI Scientist, Yao Shunyu. Their concurrent release is not coincidental but marks a critical inflection point.

The backdrop is Tencent's strategic gambit in early 2025. Facing intense competition from Baidu's Ernie, Alibaba's Tongyi, and ByteDance's Doubao, and with its own Hunyuan-powered Yuanbao assistant lagging, Tencent made the pragmatic yet striking decision to integrate DeepSeek's model into Yuanbao. The move successfully boosted user activity but was also a public admission that a startup had surpassed the tech giant in core model capability. April represents the end of that borrowed time. When Tencent switches Yuanbao back to its own in-house Hunyuan model, the market will deliver a verdict: do users engage with Yuanbao for Tencent's ecosystem or for DeepSeek's underlying technology?

For DeepSeek, the pressure is of a different magnitude. Its V3 model achieved staggering scale, with cumulative downloads exceeding 110 million and weekly active users nearing 97 million at its peak shortly after launch. More profoundly, its open-source, efficiency-first approach challenged the industry consensus on brute-force compute scaling, an event symbolized by a single-day $600 billion evaporation in Nvidia's market capitalization following V3's release. Consequently, expectations for V4 are not merely for improvement but for another redefinition.

Liang Wenfeng's approach appears to be focused, foundational innovation. Recent technical papers from his team, such as one on a "Conditional Memory" mechanism that allows dynamic retrieval of relevant context, point to architectural overhauls aimed at enhancing long-term memory and complex task completion—key attributes for the Agent era. Perhaps most significantly, DeepSeek V4 is touted as being deeply adapted for domestic AI chips (e.g., Huawei's Ascend, Cambricon), potentially becoming the first top-tier LLM to run fully on a native Chinese computing ecosystem. If successful, this would represent a breakthrough in reducing critical dependency on Nvidia's hardware.

Converging Goals, Divergent Philosophies Intriguingly, both DeepSeek and Tencent's technical roadmaps are converging on similar goals: long context, persistent memory, and Agent usability. However, their methodologies and underlying philosophies clash.

DeepSeek's path is one of open-source disruption and architectural innovation from the ground up, seeking to prove that superior efficiency and sovereignty are viable competitive foundations. Tencent's approach leverages its immense scale and application ecosystem. It has proactively sought to shape the narrative by proposing CL-bench, a new benchmark for evaluating context learning capabilities. This represents a battle for definitional power: is the winner the one who builds the better model, or the one who defines the standards by which "better" is measured?

As Guosen Securities analysts note, the Agent era is shifting the core competitive edge for model providers from "parameter scale" to "task completion rate," and from "technical leadership" to "ecosystem lock-in." The current valuation frenzy for Moonshot, MiniMax, and Zhipu is a bet on their positioning within this new paradigm. Yet, as one investor cautioned, "The market has preliminarily accepted the commercial logic of AI as a core productivity tool, but who can truly prove profitability remains to be verified by time."

April will provide crucial early indicators. The performance of DeepSeek V4 and the user retention metrics for Tencent's Hunyuan switch will begin to answer whether the current hype is built on sustainable foundations. When the fever around OpenClaw eventually subsides and capital returns to rationality, the entire sector—from the $18 billion Moonshot to the HK$380 billion MiniMax—will be judged by a stern metric: real, demonstrable profit. The race is no longer a simple sprint; it has fragmented into multiple simultaneous marathons across different terrains, defining the fragmented yet fiercely competitive future of Chinese AI.