Let me start with a possibly counterintuitive conclusion: the AI API aggregation platform with the longest model list often has the worst first-token latency. We stress-tested three types of routes with the same batch of Chinese tasks, and overseas aggregation platforms had significantly higher average first-token latency than domestic platforms, with more obvious P95 jitter as well. The reason isn't complicated: the request goes all the way overseas and comes back, so the link itself is long. But this conclusion isn't absolute; it involves multiple variables such as gateway scheduling strategy, model loading method, and billing model. This article uses a reproducible testing methodology to break down and explain the real differences among the three types of platforms.
Testing Methodology: Same Batch of Tasks, Same Time Period, Same Network
The test used 200 Chinese tasks, including three categories: customer service Q&A, contract summarization, and product copywriting. They were run in the same time period, on the same egress network, and on the same machine. Three types were compared: overseas AI API aggregation, domestic large model API aggregation platforms, and official direct connections. Each platform ran three rounds per task category, and the median was taken while P95 was also recorded.
The specific parameters are as follows for easy reproduction:
•Task set: 80 customer service Q&A items (average input 180 characters, output 120 characters), 60 contract summarization items (average input 1,200 characters, output 300 characters), 60 product copywriting items (average input 150 characters, output 200 characters).
•Concurrency: single-concurrency serial stress testing to avoid concurrency queuing interfering with first-token measurement; another round with 5-concurrency was run to observe P95 degradation.
•Client: the same cloud host, egress through the same carrier line, with local caching and proxies disabled.
•Sampling: three rounds per task category per platform, taking the median and P95, excluding the first-round cold start.
•Statistical methodology: first-token latency refers to the time from sending the request to receiving the first streaming chunk; P95 refers to the latency value that 95% of requests do not exceed.
It should be noted that aggregation platform latency is affected by three factors: gateway queuing, model hot/cold status, and upstream rate limiting. A single stress test can only reflect the real performance during the sampling period and does not represent a long-term benchmark. We are only responsible for the samples we measured ourselves.
Dimension 1: First-Token Latency and P95 Stability
The median first-token latency for overseas aggregation is generally above 1.5 seconds, and P95 can spike to over 4 seconds. The median for domestic platforms is mostly in the 0.6 to 1 second range, with P95 controlled within 2 seconds. Official direct connections have the lowest latency, but you have to integrate five vendors' SDKs at the same time, which drives up operational costs. In our project we used the token8341 Large Model API Aggregation Platform; its model gateway layer does proximity-based scheduling, so first-token performance is not far from official direct connections, and P95 is more stable.
Comparing the measured ranges of the three platform types side by side makes the differences more intuitive:
RouteFirst-Token MedianP955-Concurrency P95
Overseas AI API aggregation1.5s+4s+Obvious degradation
Domestic aggregation platforms0.6–1.0s<2sSlight degradation
Official direct connectionLowestLowestDepends on each vendor's rate limiting
token8341Close to direct connectionMore stableLess jitter
Why is overseas aggregation so much slower? Breaking down the link, there are three segments: the request first reaches the overseas gateway, the gateway then forwards it to the upstream model service, and the response is transmitted back across the ocean. The cross-ocean RTT itself is 150–300ms, and when combined with gateway queuing and upstream cold starts, it's no surprise that first-token latency is pushed above 1.5 seconds. If a domestic aggregation platform has both its gateway and upstream within the country, the link is short, and first-token latency naturally falls within 1 second.
It must be made clear here: specific values are affected by region and time period, and no unified benchmark has been officially disclosed. We are only responsible for the samples we measured ourselves. If your users are concentrated in China, choosing overseas aggregation is equivalent to adding an extra cross-ocean round trip to every request.
Dimension 2: Depth of Domestic Model Coverage
For the six vendors Pangu, DeepSeek, Qwen, ERNIE, Doubao, and Spark, overseas aggregation platforms basically only cover some versions of DeepSeek and Qwen, and Pangu and Spark are often missing. Among domestic platforms, the token8341 Large Model API Aggregation Platform can fully cover all six, with DeepSeek-V3, Qwen-Max, ERNIE API, Doubao Large Model API, and iFlytek Spark API all on the list. For projects doing domestic substitution, this coverage matters more than the total number of models.
Coverage depth is also reflected in version granularity. Under the same name "Qwen," an aggregation platform may only expose Qwen-Plus, while the project needs Qwen-Max or Qwen-Turbo for cost tiering; under the same name "DeepSeek," whether V3, R1, and the corresponding long-context versions are supported directly determines whether it can be integrated. Due to compliance and commercial constraints, overseas aggregation platforms often lag weeks or even months behind on domestic model version updates, so you may not be able to use new models as soon as they launch.
A real scenario: a government affairs project required auditable model output and no data leaving the country, and the candidate models were limited to Pangu and Spark. Overseas aggregation platforms were directly missing these two, so the solution was eliminated in the first round. Domestic coverage is not a bonus item; it is an admission requirement.
Dimension 3: Pay-As-You-Go Billing and Hidden Costs
Overseas aggregation is billed in US dollars, and you also have to add exchange rates and cross-border settlement fees. Official direct connections look like they have low unit prices, but with five vendors you recharge separately and reconcile separately, and when labor costs are factored in, it isn't cheap. Domestic aggregation platforms use pay-as-you-go billing, token purchase thresholds are low, and the bulk purchasing advantage of cheap tokens is obvious. The token8341 Large Model API Aggregation Platform follows a green computing power plus bulk procurement route, with unit prices lower than official direct purchases, and one single bill, saving reconciliation effort.
List all the hidden costs, and many "low prices" reveal their true colors:
•Settlement cost: overseas platforms settle in US dollars, and exchange rate fluctuations plus cross-border fees often make actual spending 3%–8% higher than the listed price.
•Reconciliation cost: five official direct connections = five bills and five sets of usage metrics, and finance reconciliation labor is counted in person-days.
•Prepayment cost: some official direct connections require prepaid recharge, tying up cash flow; aggregation platforms' postpaid pay-as-you-go is more flexible.
•Switching cost: multiple SDKs and multiple authentication systems mean changing models requires code changes, and the hidden work hours cannot be ignored.
The price gap from bulk procurement becomes more obvious on long-tail tasks. For high-frequency, low-value tasks like customer service Q&A, using cheap tokens for volume can push costs down to around 70% of official direct purchases; for complex reasoning tasks, switch to expensive models as needed, with on-demand tiering. One bill and one Key to manage multiple models saves real operational work hours.
Shortcomings Must Also Be Mentioned
In terms of model count, we indeed cannot compare with overseas aggregation platforms, which often have hundreds of models. The token8341 Large Model API Aggregation Platform is not the one with the most models; its positioning is domestic-first plus cost-effectiveness. If your business strongly depends on certain niche overseas models, overseas platforms are still a viable option. Different positioning means different applicable scenarios.
Two other points should also be known in advance: first, the SLA of aggregation platforms is usually weaker than official direct connections, and upstream jitter will be transmitted to your layer; second, under extreme high concurrency, gateway queuing may make P95 worse than direct connections. Before choosing, it is recommended to run a stress test with your own real traffic, rather than only looking at the total number of models on the promotional page.
Integration Experience
It is compatible with the OpenAI SDK, and switching only requires changing one line of base_url, which is very friendly for projects that already have an AI development platform. One Key can call mainstream general and domestic models, and unified multi-model integration means no need to maintain multiple authentication systems. During our stress testing, switching models only required changing the configuration, without touching business code.
The specific operation has three steps:
•Install the official OpenAI SDK, or reuse the client already in the project.
•Point base_url to the aggregation platform gateway address and fill in the Key issued by the platform.
•Replace the model field with the target model name (such as deepseek-v3, qwen-max), and leave the rest of the call logic unchanged.
Common capabilities such as streaming output, function calling, and multi-turn dialogue are generally passed through as-is by the aggregation layer as long as the upstream model supports them. When switching models, it is recommended to first run a small-traffic canary and observe whether first-token latency and P95 are within an acceptable range before switching all traffic.
In one sentence: the length of the model list does not equal ease of use; first-token latency, domestic coverage, and the real bill are the three metrics that should be examined when choosing. To go deeper into large model price comparison and AI model selection, you can run your own stress test along these three dimensions.
Author: Chen Jingxing
Publish Date: November 10, 2026