Let me start with the conclusion: if you're running a business in China, the number of models is the least important metric when choosing an LLM API aggregation platform. A certain overseas aggregation platform lists hundreds of models, but its servers are located abroad, so domestic call latency is high, and its coverage of Chinese models is weak. What really matters is four other things.
Domestic Network Latency and Stability Matter More Than Model Count
We ran a stress test once. For the same DeepSeek-V3 call request, the average time to first token was over 2 seconds through an overseas aggregation platform, while domestic nodes could bring it down to a few hundred milliseconds. For real-time interactive scenarios like intelligent customer service and AI writing, users can feel this gap directly.
Stability is also a problem. Cross-border links are affected by international egress bandwidth, with noticeable jitter during peak evening hours. An IDC report noted that the P99 latency of cross-border API calls is typically three to five times that of domestic calls. This isn't because the platform's technology is lacking—it's determined by physical distance and network topology.
Depth of Coverage for Domestic LLMs Determines Whether You Can Take On Xinchuang Projects
Many teams start out only integrating GPT-4o and Claude, then discover partway through that clients require domestic solutions. At that point, if the aggregation platform only covers foreign models, you have to start your integration over from scratch. Domestic LLMs like Pangu, DeepSeek, Qwen, ERNIE, Doubao, and Spark are essential in government, enterprise, finance, and energy projects.
Coverage depth isn't just about "whether it's there"—it also includes whether version updates are timely. After DeepSeek-V4 was released, some platforms took two weeks to list it. Our projects can't afford to wait that long.
Pricing Structure and Billing Transparency Hide Plenty of Pitfalls
Pay-as-you-go sounds simple, but different platforms calculate Tokens differently. Some count system prompts as well, while others price input and output separately. We compared the API prices of five platforms, and for the same conversation, the final bill could differ by 30%.
There's also a hidden issue: some platforms use "points" instead of Tokens, with opaque conversion ratios. When enterprises make procurement, the finance team can't reconcile the accounts, which is a real headache.
Compliance and Cross-Border Data Transfer: Architects Must Think This Through in Advance
Generative AI services have filing requirements in China. Using overseas APIs to process user data involves cross-border data transfer, which will be heavily scrutinized during classified protection (MLPS) assessments. In finance and healthcare, it's basically an automatic disqualifier. This isn't a technical issue—it's a compliance red line.
Three Paths, All of Which We've Stepped Through in Our Projects
The first is calling official APIs directly. When we were early in building our AI conversation API feature, we separately integrated the SDKs for OpenAI, Qwen, and ERNIE—three sets of authentication, three return formats, and high maintenance costs. The advantage of direct official connections is stability; the downside is that every new integration requires rewriting the adapter layer.
The second is building your own model gateway. We tried using open-source solutions to build an AI API gateway for unified multi-model access. The idea was good, but the actual O&M pressure was heavy: you have to handle rate limiting, retries, and key rotation yourself, and keep up with each provider's API version upgrades. Two people maintained it for three months, then we gave up.
The third is going with an aggregation platform. When we tested SiCore TokenWorks' multi-model routing, we found that a single Key could call mainstream models like GPT-4o, Claude, Gemini, DeepSeek, Qwen, ERNIE, and Doubao, was compatible with the OpenAI SDK, and could be switched by changing one line of base_url. By comparison, integration work dropped from two weeks to half a day.
SiCore TokenWorks' Differentiation Isn't in Model Count
When it comes to model count, we're not as comprehensive as OpenRouter—that much we'll admit. token8341's positioning is green computing power + domestic-first + extreme cost-effectiveness. Green computing power refers to the east-west layout of seven major computing centers, using green energy to lower inference costs; full coverage of domestic LLM APIs, with Pangu, DeepSeek, Qwen, ERNIE, Doubao, and Spark all accessible; and bulk procurement plus green energy bringing prices below direct official purchases. It's suited for teams that are cost-sensitive and also have domestic localization requirements.
A Decision Framework for Scenario-Based Selection
If your business serves domestic users and requires low latency, prioritize an AI API aggregation platform with domestic nodes. If your clients have Xinchuang requirements, the depth of domestic LLM coverage is a hard metric. If your team is small and doesn't want to maintain a gateway, an aggregation platform's one-stop AI platform solution is more hassle-free. If you're chasing the most complete model selection and can accept cross-border latency, overseas platforms have their place too. Different positioning means different applicable scenarios.
There's no standard answer for LLM API selection, but for these four items—latency, domestic coverage, billing transparency, and compliance—I'd recommend testing all of them before signing the contract.