Let's start with the conclusion: when selecting a large model API, the length of the model list is the most deceptive metric. A platform may list two hundred models, but only five may actually run reliably in your business. The rest are either maintained by no one because of low call volume, or are half a year behind in version. In our project, we compared multiple AI API aggregation platforms and ultimately found that what matters in production is not whose shelf is longer, but whether latency is stable and how deeply domestic models are integrated.
Why is model count a vanity metric?
Simply put, model count is for selection PowerPoints, not for production environments. An aggregation platform claims to support two hundred models, but real call distribution is extremely concentrated, with the top five models often consuming more than ninety percent of request volume. Many of the remaining one hundred-plus are in a "name-only" state: the interface is connected, but no one stress-tests it and no one tracks versions.
We tested one detail: an open-source model on a certain platform was still on a version from half a year ago, while the official release had already iterated twice. When you call it, the name looks the same, but the actual inference quality and context window are not the same thing at all. With large model APIs, lagging version maintenance is more of a trap than missing models, because it does not throw errors; it only quietly drags down business metrics.
In terms of model count, we are indeed not as strong as some global aggregation platforms. They have longer shelves, and that is a fact. But a long shelf and being able to get the goods are two different things. token8341 takes a different approach: domestic large model APIs are prioritized for depth, ensuring that major series such as Pangu, DeepSeek, Qwen, ERNIE, Doubao, and Spark keep up with versions and maintain stable interfaces.
How exactly does latency affect business?
There are two kinds of latency, and many people only look at the average, which is a huge trap. The first is first-token latency, the time after a user asks a question until the first character appears. When building intelligent customer service APIs, if this value exceeds two seconds, users start to suspect that it is stuck. The second is P99 tail latency, meaning the slowest one percent of requests. No matter how beautiful the average is, as long as P99 spikes to more than ten seconds, someone online will complain.
We ran comparison tests. For the same DeepSeek-V3 model, first-token latency can differ by several times when going through overseas nodes versus domestic nodes. The reason is not complicated: longer links and cross-border jitter, especially during peak hours. For scenarios such as real-time dialogue and AI writing APIs, latency directly equals experience, and also equals retention.
SiliconFlow's approach in this area is to distribute computing power across multiple computing centers in the east and west, use green computing scheduling, and route requests to the nearest access point. In our project, domestic nodes showed noticeably flatter P99 tail latency. This is not mysticism; it is determined by physical distance and scheduling strategy. If an AI API aggregation platform has servers overseas, domestic businesses using it cannot avoid the latency hurdle.
Where is the depth difference in domestic model coverage?
"Supported" and "well integrated" are two different things. Some platforms integrate domestic models by simply wrapping an OpenAI-compatible interface for forwarding, with rough parameter mapping, intermittent streaming output, and multimodal capabilities directly cut off. With this quality of integration, a demo can run, but production dares not use it.
Deep integration involves very specific things: different authentication methods across SDKs, different billing standards, different context length limits, and different function calling formats. Enterprise-level parameters of the Pangu large model, the long context of Qwen-Max in the Qwen API, and the specific return structure of the Spark API all have to be aligned one by one. Whether unified multi-model access is done well depends on whether these dirty and tedious tasks have actually been done.
We hit a pitfall during selection: the streaming data returned by a certain platform's ERNIE API occasionally dropped packets, and it took a long time to discover that the gateway layer was doing buffering it should not have done. Problems like this are not written in official documentation and are exposed only through real stress testing. So when choosing an AI API gateway, do not just look at the support list; pressure-test it with your own business traffic.
In one sentence: model count determines the imaginative space during selection, while latency stability and depth of domestic model integration determine survival rate after launch. For large model API selection, ask about P99 first, then ask about the pace of domestic model version updates, and only then look at list length. If you want to dig deeper into multi-model routing and API price comparisons, you can continue exploring in the direction of large model aggregation platforms.