Something interesting is happening in the AI model market. Looking at this week's OpenRouter model usage ranking , 8 of the Top 10 models by token usage are Chinese models. Yes — 8 out of 10. Here is the current ranking: Rank Model Weekly Tokens 1 Tencent: Hy4 Preview 20.4T 2 OpenAI: GPT-5.6 Luna 14.8T 3 DeepSeek: DeepSeek V4 Flash 0731 12.9T 4 Z.ai: GLM-5.3 Flash 12.7T 5 DeepSeek: DeepSeek V4 Flash 0423 5.15T 6 Tencent: Hy3 4.01T 7 NVIDIA: Nemotron 3 Ultra (free) 3.82T 8 Xiaomi: MiMo-V2.5 3.82T 9 Z.ai: GLM-5.3 3.48T 10 Z.ai: GLM-5.2 2.51T The only two non-Chinese models in the Top 10 are: OpenAI GPT-5.6 Luna NVIDIA Nemotron 3 Ultra Everything else comes from: Tencent DeepSeek Z.ai Xiaomi And there is another number that makes this even more interesting. Nearly 78% of Top-10 Token Usage Comes From Chinese Models If we add up the weekly usage shown in the ranking, the Top 10 models account for approximately: 83.59 trillion tokens The eight Chinese models account for approximately: 64.97 trillion tokens That's roughly: 78% of all token usage represented by the Top 10. So Chinese models aren't just occupying many positions on the leaderboard. They're also representing the majority of actual token volume within this Top 10. That's a much more interesting signal. Tencent Hy4 Preview Is #1 Perhaps the biggest surprise is the model at the top. Tencent Hunyuan Hy4 Preview currently sits at #1 with: 20.4 trillion tokens That's significantly ahead of OpenAI GPT-5.6 Luna at 14.8T. Hy4 Preview is Tencent's latest large MoE model, with: 770B total parameters 49B activated parameters 1M context Strong focus on agents Coding Tool use Productivity Long-horizon execution What's interesting is that Hy4 isn't positioned as a simple chatbot model. It's designed around a broader shift: Question ↓ Answer is becoming: Goal ↓ Plan ↓ Use tools ↓ Execute ↓ Observe ↓ Adjust ↓ Continue ↓ Complete task That kind of workload can also consume a lot of tokens, which is worth remembering when interpreting usage rankings. DeepSeek Still Has Huge Usage DeepSeek occupies two positions in the Top 5: #3 — DeepSeek V4 Flash 0731 12.9T tokens #5 — DeepSeek V4 Flash 0423 5.15T tokens Combined, that's more than: 18 trillion tokens in one week across these two V4 Flash versions alone. DeepSeek has become one of the most recognizable Chinese AI brands globally, but this ranking shows something more important than awareness: developers are actually using the models at scale. And the Flash family illustrates an important trend in AI infrastructure. Not every workload needs the largest and most expensive frontier model. For many production applications, developers care about a balance between: Intelligence × Speed × Cost × Reliability A model that is slightly weaker on a benchmark but dramatically cheaper to run can be much more attractive at scale. GLM Has Three Models in the Top 10 Z.ai may actually have the most interesting representation in this ranking. Three GLM models appear in the Top 10: GLM-5.3 Flash — #4 GLM-5.3 — #9 GLM-5.2 — #10 GLM-5.3 Flash alone processed: 12.7T tokens That puts it almost level with DeepSeek V4 Flash 0731. The recent GLM direction is also interesting because the models are becoming increasingly focused on: Coding Agents Visual understanding Browser workflows Tool use Professional productivity Long-running tasks GLM-5.3 Flash is particularly notable because it combines: 320B total parameters 18B activated parameters with a highly efficient hybrid attention architecture. This is another trend we're seeing across Chinese AI labs: The race isn't only about making models bigger. It's also about making intelligence cheaper to run. Chinese AI Is No Longer Just "DeepSeek" A year ago, when many international developers talked about Chinese AI, the conversation often started and ended with: DeepSeek. That picture is changing quickly. Look at this Top 10 again: Tencent DeepSeek Z.ai Xiaomi Four different Chinese companies are represented. And other major Chinese AI ecosystems include: Qwen MiniMax Kimi Doubao Baidu and others This matters because we're not looking at one breakout model anymore. We're looking at an increasingly broad AI model ecosystem . Different companies are competing on different dimensions: Coding Reasoning Agents Multimodal Long context Latency Cost Tool use Productivity That creates much more choice for developers. But Usage Does Not Mean "Best" There is an important caveat. This is a usage ranking , not an intelligence leaderboard. 20T tokens does not mean a model is objectively better than one processing 5T tokens. Token usage can be influenced by many factors: Pricing Free availability Context length Model routing Agent workloads Coding workloads API availability Developer adoption Latency Promotions Application volume For example, an agent that reads an entire repository and performs dozens of tool calls can consume far more tokens than a chatbot answering simple questions. A cheap model may also be used much more aggressively than an expensive frontier model. So the conclusion shouldn't be: "Chinese models are better because they use more tokens." The more interesting conclusion is: Chinese models have clearly moved from being alternative models to models developers are actively using at very large scale. That's an important distinction. Cost May Be One of the Biggest Reasons One thing Chinese AI providers have been especially aggressive about is API pricing. This changes how developers think about model selection. Suppose one model is 5% better for your workload but costs 10× more. Which one should you use? For a low-volume application, perhaps the stronger model. For billions of tokens of production traffic, the answer may be very different. This is why I think the most useful model metric is increasingly not: Benchmark score or even: Price per million tokens but: Cost per successfully completed task A coding agent might use a more expensive model but finish in fewer attempts. A cheaper model might consume more tokens but still have a lower total cost. The economics depend on the workload. Flash Models Are Becoming Extremely Important Another thing that stands out in this ranking is the popularity of Flash-class models . We have: DeepSeek V4 Flash GLM-5.3 Flash near the very top. That makes sense. Production AI workloads often need: Good enough intelligence + Low latency + Low cost + High throughput rather than maximum benchmark performance on every request. For example, an application might use: Simple tasks ↓ Flash model Medium tasks ↓ General model Difficult tasks ↓ Frontier reasoning model That's likely to become a common architecture. The question isn't necessarily: Which model should my application use? It may instead become: Which model should handle this particular request? The Future Is Probably Multi-Model This ranking is also another reminder of how quickly the model market changes. Today: Hy4 Preview DeepSeek V4 GLM-5.3 GPT-5.6 MiMo are receiving huge amounts of traffic. Next month, the ranking may look completely different. That's why I think tightly coupling an application to a single AI provider is becoming increasingly limiting. A more flexible architecture could look like: Application ↓ AI Model Layer ↓ ├── GPT ├── Claude ├── Gemini ├── DeepSeek ├── Qwen ├── GLM ├── Hunyuan ├── MiniMax └── Others Then select models according to: Capability Cost Latency Context Availability Tool support Task type The more competitive the model market becomes, the more valuable this flexibility becomes. This Is Exactly Why We're Building ApiHub One of the things we're trying to solve with ApiHub is making this growing model ecosystem easier for developers to access. Several of the Chinese models appearing in this ranking are already available through ApiHub, including models from: Hunyuan DeepSeek GLM and other major AI providers Instead of creating a completely separate integration every time you want to test another model, ApiHub provides a unified way to access and compare different models. 👉 https://www.apihub.ink/ We support multiple integration styles: Responses API Messages API OpenAI-compatible API And we provide free credits so developers can test different models before deciding what works best for their application. The goal isn't to tell developers: "Model X is the best." It's to make it easier to answer: "Which model is best for my workload?" by actually testing them. The Most Interesting Part Isn't 8 Out of 10 The headline is surprising: 8 of OpenRouter's Top 10 most-used AI models this week are Chinese. But I think the bigger story is underneath it. Chinese AI models are increasingly competing on: Capability Price Efficiency Coding Agents Multimodal understanding Long-context execution And developers are actually using them. At the same time, OpenAI, Anthropic, Google, NVIDIA, and other labs continue pushing the frontier forward. That's good for developers. More competition means: More models ↓ More choices ↓ Lower prices ↓ Faster iteration ↓ Better AI applications We're moving away from a world where choosing an AI model meant choosing between only two or three companies. The model layer is becoming a competitive marketplace. And that may ultimately matter much more than who happens to be #1 this week. What do you think? Are Chinese AI models becoming part of your default model stack, or do you still mainly use GPT, Claude, and Gemini? And if you've tried Hy4, DeepSeek, GLM, Qwen, or other Chinese models, which one surprised you the most? Disclosure: I'm building ApiHub , a unified AI API platform designed to make multiple AI models easier to access, test, compare, and integrate. 👉 https://www.apihub.ink/ ai #llm #programming #devtools
Kaynak: Dev.to
Alinti: Bu haber Dev.to tarafindan yayinlanmistir. Haberin tamamini ziyaret ederek okuyabilirsiniz.
Guncelleme: 09.09.2026 08:05 – Barış Tekin haber derlemesi