Alibaba's Qwen Tops Global API Usage: Real Overtaking or Just a Market Illusion?
When I first saw the news about Alibaba’s Qwen topping the global API usage leaderboard, my immediate reaction was: are these numbers legit?
Not because I doubt Alibaba, but because the result felt counterintuitive. After all, conventional wisdom suggests OpenAI’s API call volume should be leagues ahead. But after carefully examining the ranking criteria and actual data, I found this story more interesting than I expected—not because it proves Chinese models “won,” but because it reveals a new logic in the LLM competition.
Three Truths Behind the Ranking
Truth 1: API Volume ≠ Technical Superiority
Let’s be clear: this “global usage leaderboard” measures API call counts, not model performance. Just like the most-viewed TikTok video isn’t necessarily the most well-produced, the most-called model isn’t necessarily the strongest.
Qwen topped the list primarily due to its pricing strategy—in early 2026, Qwen launched a combination of “enterprise free tier + ultra-low incremental pricing,” directly compressing calling costs to 1/3 or even 1/5 of competitors. For startups and SMBs, the math is simple: why pay more for similar functionality?
This reminds me of the early cloud server market—AWS had superior technology, but Alibaba Cloud captured the Chinese market through pricing and localized services. The LLM market is repeating the same story.
Truth 2: China’s “Island Effect”
There’s a detail in the data that’s easily overlooked: Qwen’s API volume comes mainly from domestic users, with overseas usage under 15%. Behind this lies an awkward reality—due to multiple factors like network, payment, and compliance, many Chinese developers don’t have the opportunity to use OpenAI’s API at scale.
This isn’t a technical issue, it’s an “availability” issue. I have friends building AI applications who started with GPT-4, then switched to Qwen because of unstable speeds and payment hassles. Not because Qwen is better, but because Qwen is “more convenient.”
From this perspective, Qwen topping the global leaderboard is more like being king in an “isolated market.” This isn’t a criticism—achieving dominance in your home market is itself a competitive advantage. But talk of “overtaking OpenAI” might be premature.
Truth 3: Ecosystem Trumps Models
I noticed a pattern: Qwen has been aggressively building its “ecosystem” in recent months—open-sourcing the Qwen2.5 series, launching a model fine-tuning platform, and doing deep integrations with numerous SaaS vendors. These moves seem unsexy individually, but together they create a “stickiness trap.”
Once developers enter this ecosystem, migration costs increase—your data is here, your fine-tuned models are here, your applications and SDKs are all adapted to this API. Even if OpenAI releases a model 20% stronger, you might not switch.
This is Qwen’s real “moat.” Not technical leadership, but making users “unable to leave.”
The Real Gap for Chinese LLMs
Returning to the “overtaking” narrative. I think many people have polarized views on Chinese LLMs—either believing they’re “completely ahead” or “still catching up.”
The reality likely falls somewhere in between.
According to Stanford’s latest AI Index Report, top Chinese models (like DeepSeek V4, Qwen 3.6) have caught up to GPT-4-level models on most benchmark scores, with slight advantages in certain tasks (like Chinese understanding, code generation).
But significant gaps remain in several key dimensions:
Reasoning capability: On tasks requiring multi-step reasoning and complex logic chains, Chinese models still lag slightly. This isn’t about parameter count, but training data and methodology.
Generalization: OpenAI’s models perform more stably on “unseen problems,” while Chinese models often require targeted fine-tuning to achieve similar results.
Multimodal integration: GPT-4V and Claude 3.5 Sonnet’s capabilities in text-image mixing and cross-modal understanding are areas Chinese models are still chasing.
So Qwen topping the usage leaderboard is more a victory of business model than comprehensive technical superiority. But that doesn’t make it unimportant—in this LLM marathon, technology, business, and ecosystem are all essential.
An Open Question
I want to raise a question: in LLM competition, do you think “technical leadership” or “ecosystem stickiness” matters more?
My personal feeling is that in the short term, ecosystem might matter more—users won’t bear migration costs for a 10% performance boost. But long-term, if the technical gap keeps widening, the ecosystem will collapse too.
What Qwen needs to think about now isn’t celebrating the top spot, but how not to fall behind in the next wave of technology. After all, OpenAI’s GPT-6 is coming, Anthropic’s Claude 4.5 just launched—this battle has only begun.