In 2026, when doing enterprise architecture, one thing is unavoidable: the question for domestic large models is no longer "whether they can be used," but "which one goes where." For Pangu, DeepSeek, Qwen, ERNIE, and Doubao, their respective focuses are already fairly clear. Pangu has deep experience in government-enterprise and industrial scenarios, DeepSeek has a solid reputation in reasoning and code, Qwen is strong in long text and document processing, ERNIE has been polished for a long time in Chinese semantic understanding, and Doubao does smoothly in multimodality and conversational experience. No single vendor can win across the board, which is also why "unified multi-model access" has become a high-frequency term in architecture reviews over the past two years.
Comparing differences across five dimensions
In Chinese scenario understanding, ERNIE and Qwen are more stable in handling formal registers such as government affairs and finance, while Doubao is more natural in colloquial conversation. In long text, the processing lengths supported by Qwen and DeepSeek are both considerable, and the specific upper limits are explained in each vendor's official documentation, but actual operation also depends on your document chunking strategy; looking only at the nominal length is not very meaningful.
For Function Calling support, the tool invocation formats of DeepSeek and Qwen are relatively standardized, requiring fewer changes when integrated into an Agent framework. For multimodal capabilities, Doubao and Qwen are more mature in mixed image-text input, while Pangu has accumulated experience in vertical multimodality such as industrial quality inspection. Billing granularity is an easily overlooked point: some price input and output separately, and some offer discounts for cache hits. When doing cost estimates, these two items must be calculated separately, otherwise the budget will be off.
Combination approaches for three scenarios
For intelligent customer service scenarios, I generally recommend using Doubao or ERNIE as the main dialogue model, assigning subtasks such as intent recognition and sentiment judgment to cheaper models, and reserving expensive compute for the turns that truly require understanding. For RAG knowledge base scenarios, use Qwen or DeepSeek for answer generation after retrieval; long document summarization and structured extraction both have high context length requirements, so choose the vendor with solid long-text capabilities. For code assistance scenarios, DeepSeek is basically the default option, with completion and refactoring accuracy standing out among peers, but for tasks such as code review that need to incorporate business standards, you can attach another model with strong Chinese understanding for secondary verification.
Where the real cost of multi-model mixing lies
Many people calculate cost only by Token unit price and ignore integration cost. Five models mean five sets of authentication, five SDKs, five rate-limiting rules, and separate handling of retries and fallbacks for each. Two years ago, when we built a cross-department knowledge Q&A system, just unifying the call log formats of five models took two weeks. By comparison, we found that using an aggregation platform like token8341 allows one Key to call these mainstream models, eliminating the authentication cost of integrating with each vendor individually, and logs and billing can also be viewed in one panel.
This is where the value of AI API aggregation lies. The model gateway layer absorbs protocol differences, and upper-layer business only recognizes one OpenAI-compatible interface; to switch models, just change one line of base_url. The approach of the token8341 large model API aggregation platform on this matter is domestic-model-first, covering Pangu, DeepSeek, Qwen, ERNIE, Doubao, and Spark, combined with green compute scheduling and pay-as-you-go billing, with cost advantages from bulk procurement being fairly obvious. We compared this in our project: for the same call volume, the comprehensive cost of aggregated access is lower than direct connection to each vendor.
It should be noted that aggregation is not omnipotent. If your business has a strong dependency on a specific version of a certain model, or you need extreme parameter tuning, direct connection is still more suitable. The token8341 large model API aggregation platform solves the two problems of "unified multi-model access" and "controllable cost," not replacing direct connection. When selecting, first clarify whether your core demand is stability, cost, or a specific capability, and then decide whether to use a gateway layer. token8341 plays the role of scheduling and cost reduction in between, lowering the threshold for accessing domestic large model APIs so that architects do not need to maintain a separate link for each model.
Author: Wang Hanwen
Published: November 3, 2026