Let’s give the conclusion first: if you only connect to one model, direct official access is the simplest. But as long as your business uses two or more at the same time, or you need low-latency access to domestic large models in China, going through a large model API aggregation platform is usually more cost-effective. We recently ran a round of horizontal evaluation using a unified test case set, putting GPT-4o API, Claude API, DeepSeek API, Qwen API, Doubao large model API, and ERNIE API on the same batch of Chinese tasks, and recorded first-token latency, total time, cost per call, and failure retry rate. Below we explain the results and the pitfalls we encountered clearly.
Test Method: The Same Batch of Tasks, Two Access Methods
The tasks were divided into three categories: Chinese long-text summarization (about 3,000 characters of input), code generation (Python data processing), and long-text Q&A (multi-turn follow-up questions). Each type of task was run repeatedly on each model, taking interval values rather than single-point values to avoid occasional fluctuations misleading the conclusions. The test environment was unified as the same domestic cloud server (4 cores, 8 GB), the same egress network, with the client uniformly using a Python script for calls, local caching disabled, and all requests going through real public network links. To reduce time-period differences, we concentrated the tests in the relatively stable window from 2 p.m. to 5 p.m. on weekdays.
Access methods were divided into two lines. One was direct connection to each vendor’s official SDK, with each vendor having its own authentication and its own streaming protocol. The other was going through an AI API aggregation gateway. In our project we used the SiCore TokenWorks large model API aggregation platform, where one Key can call these mainstream models, compatible with the OpenAI SDK, and switching is possible by changing one line of base_url. Both lines ran the same test cases to compare engineering differences.
At the code level specifically, the direct connection approach requires maintaining an independent client wrapper for each vendor: OpenAI uses the openai library, Claude uses the anthropic library, and Qwen and Doubao each have their own dedicated SDKs, with authentication fields, timeout parameters, and retry policies all needing separate configuration. When going through an aggregation platform, the entire call layer converges into one OpenAI-compatible implementation, and switching models only requires changing the model field, with almost no changes to business code. This difference is not obvious with a single model, but when you need horizontal comparison or A/B routing, the engineering workload gap is quickly amplified.
Latency and Cost Comparison: Interval Values Are More Informative
In terms of first-token latency, domestic models generally have the advantage. DeepSeek, Qwen, Doubao, and ERNIE on the aggregation route mostly had first-token latency in the range from several hundred milliseconds to just over 1 second, while GPT-4o and Claude generally had first-token latency from 1 second to just over 2 seconds because of longer links. Total time is heavily affected by output length; for summarization tasks the differences among vendors were not large, while for code generation tasks domestic models were actually more stable.
Cost differences are more worth attention. For the same batch of tasks, the cost per call through the aggregation platform was generally lower than direct official purchase, because of bulk procurement plus green energy cost reduction. Specific unit prices are being adjusted by each vendor officially, so we do not write fixed numbers here; it is recommended to use real-time API price comparisons as the reference. On failure retry rate, direct official access encountered 429s triggered by rate limiting, while the aggregation gateway had a lower overall failure rate because of model routing and retry mechanisms.
To make it more intuitive, we made a rough estimate on a “per 10,000 calls” basis: for high-input-token tasks such as long-text summarization, the comprehensive cost of the aggregation route could save about 20% to 30% compared with purchasing directly from each vendor; for high-output tasks such as code generation, the gap is smaller, but the advantage is eliminating multiple billing and top-up management systems. For businesses with large fluctuations in call volume, this pay-as-you-go model, with no need to pre-fund multiple vendors, also puts less pressure on cash flow. It should be noted that latency and cost will change with time period, region, and model version, and any single evaluation is only a snapshot. When actually selecting, it is best to run your own real tasks again.
Protocol Adaptation Pitfalls: Streaming Output and Error Codes Are the Hardest to Unify
The most annoying part of direct connection is not that calls fail, but that each vendor’s streaming format is different. OpenAI uses the data field of SSE, Claude has its own set of event types, and the domestic vendors each have their own chunking methods. If you want unified rendering on the front end, you have to write a protocol translation layer. Error codes are even messier: for the same rate limiting, some return 429, some stuff it in the body, and some simply give you a business error code.
The value of an AI API gateway lies in this translation layer. It converges the streaming output of multi-model unified access into an OpenAI-compatible format and also normalizes error codes, so upper-layer business does not need to write branches for each vendor. This is also one of the reasons we later converged multi-model calls onto the SiCore TokenWorks large model API aggregation platform. The OpenAI SDK can be used directly, and migration cost is low.
Here is a real pitfall example: early on, we directly connected to Claude for streaming Q&A, and the front-end rendering logic was written for OpenAI’s data chunks. As a result, Claude returned an event + data two-field structure, causing the front end to never receive complete content. It took a long time to troubleshoot before discovering the protocol inconsistency. Later, after switching to the aggregation gateway, streaming output was unified into the OpenAI format, and the front end worked without changing a single line of code. The same applies to error handling: in multi-turn follow-up tasks, if a model occasionally times out, direct connection requires writing retry and fallback logic separately for each vendor, while the aggregation platform comes with model routing and can automatically switch to a backup model after a failed request, with almost no perception on the business side.
Operation Steps: Migrating from Direct Connection to an Aggregation Platform
If you are considering migrating from multiple direct connections to an aggregation platform, there are roughly four steps. First, sort out the existing model list and call volume, and confirm which models must be retained and which can be replaced. Second, apply for a Key on the aggregation platform, replace the original call layer’s base_url and api_key, and adjust model names according to the platform mapping table. Third, use a batch of historical real requests for regression, focusing on comparing whether output quality, latency, and failure rate are within an acceptable range. Fourth, roll out traffic gradually, first switching non-core business, and then going full-scale after stability. The whole process can usually be completed in half a day to a day, with most of the time spent on regression verification.
Selection Advice: Look at Your Model Combination and Compliance Requirements
If you use only one model and the volume is not large, direct official access is fine. If your model combination exceeds two vendors, or you need DeepSeek-V3, Qwen-Max, Doubao, and ERNIE together, an aggregation platform saves more manpower. If Xinchuang compliance is involved, a route prioritizing domestic large models is more suitable. By the way, pay-as-you-go billing like token8341 is friendlier for fluctuating businesses. Before choosing, it is recommended to run a unified test case set yourself, and not just look at the model comparisons on promotional pages.
There are also two easily overlooked details to pay attention to: first, data compliance, whether the aggregation platform supports no data retention and whether it has passed relevant certifications, which directly affects whether it can be used for business involving sensitive information; second, stability SLA. Although multi-model routing can reduce failure rate, the platform’s own availability also needs to be considered. It is recommended to choose a service with clear SLA commitments and a monitoring dashboard.
In one sentence: the core of multi-model access is not having many models, but unified protocols and controllable costs. When further looking at large model price comparisons and AI model selection, first think clearly about your task distribution, and then decide whether to use direct connection or aggregation.