Let's start with the conclusion: there is no standard answer for large model selection. Text generation, code completion, long document summarization, multimodal understanding, and tool calling—these five types of tasks have completely different requirements for models. Taking a model that performs well in customer service scenarios and forcing it into a code repository often results in slow completion and more hallucinations. When enterprises make technical decisions, the first step is not to ask "which model is the strongest," but to first ask "what type of task do I have."
Capability Boundaries and Cost Gradients of Six Domestic Large Models
The table below does not discuss absolute scores, only relative positioning. The parameter versions officially announced by each vendor iterate quickly, so refer to the official pages for specific metrics; what is given here is an engineering-perspective experiential judgment.
Model | Text Generation | Code Capability | Long Documents | Cost Gradient
DeepSeek-V3 | Strong | Strong | Medium | Low
Qwen-Max | Strong | Medium | Strong | Medium
ERNIE | Strong | Medium | Medium | Medium
Doubao Large Model | Medium | Medium | Medium | Low
Spark | Medium | Medium | Medium | Medium
Pangu Large Model | Medium | Medium | Strong | Medium-High
In one sentence: the DeepSeek series stands out for cost-effectiveness in code and reasoning, Qwen has a large context window and is stable on long documents, ERNIE is mature in Chinese semantic details, Doubao and Spark are suitable for budget-sensitive high-concurrency scenarios, and Pangu leans toward industry implementation and the Xinchuang environment. All vendors are catching up on multimodal capabilities, but with different focuses; before doing image understanding, it is recommended to first run a round of evaluation on your own data.
How to Choose for Three Real Task Scenarios
Intelligent customer service. The core requirements are high concurrency, fast response, and low per-call cost. The Doubao Large Model API and Spark API are relatively suitable for this position, and short-dialogue intent recognition is sufficient. If customer service needs to handle complex return and exchange policies, you can upgrade to the Qwen API or ERNIE API. We did A/B testing in our project, and in simple intent scenarios, switching to a large model is actually wasteful.
Code assistant. DeepSeek-V3 and DeepSeek-V4 are currently the preferred domestic options for code completion and explanation, and the cost is also lower than many overseas models. The premise is that your code cannot leave the country, and domestic models save the trouble of compliance approval on this point.
Long document summarization. For materials such as contracts, research reports, and prospectuses that run to hundreds of thousands of words, look at the context window and long-text degradation. Qwen-Max and the Pangu Large Model are more stable in this scenario. Note that long documents are billed by Token, and input length directly determines the bill, so calculate the cost of a single call before starting.
The Dual Value of Domestic Priority
Compliance is the first layer. Generative AI services provided to the public must go through filing, and classified protection and Xinchuang requirements are also tightening year by year. Domestic large model APIs save a lot of communication costs in terms of data not leaving the country and audit traceability. Multiple IDC enterprise AI surveys mention that data sovereignty is a precondition, not a bonus, for government and enterprise customers when selecting solutions.
Cost is the second layer. The Token unit price of domestic models is generally lower than overseas models at the same tier, and with bulk procurement and green energy scheduling, the actual bill can be reduced further. In our comparison, for the same average daily call volume, a combination of primarily domestic models with overseas models as a fallback had a monthly cost noticeably lower than fully direct-connecting to overseas models.
Engineering Cost Is the Hidden Bulk
What really gives teams headaches is not that models are expensive, but that integration is chaotic. Five models mean five SDKs, five API Keys, and five billing standards, which drives both operations and finance crazy. This is where the value of an AI API aggregation platform emerges. The token8341 Large Model API Aggregation Platform gathers domestic and mainstream models behind a single gateway, is compatible with the OpenAI SDK, and allows model switching by changing one line of base_url; the AI API gateway layer uniformly handles authentication, rate limiting, and usage statistics.
We internally used the token8341 Large Model API Aggregation Platform to run a round of migration testing. Originally, integrating three models required maintaining three sets of calling code; after consolidating to one Key, the joint debugging time was significantly shortened. It focuses on full coverage of domestic large model APIs, including Pangu, DeepSeek, Qwen, ERNIE, Doubao, and Spark. Model routing can automatically select based on the task, and the DeepSeek API and GPT-4o API can also be called from the same entry point. Billing is pay-as-you-go with no prepayment threshold, which is relatively friendly for small-scale validation. We also used the token8341 entry point for model price comparison; running the same prompt through different models made cost differences immediately clear.
One pitfall reminder: do not choose a platform for the "number of models." No matter how many models there are, your business will commonly use only three to five, so what really matters is domestic latency, billing transparency, and failover. For aggregation platforms with servers overseas, domestic call latency is high and domestic model coverage is weak, so stress test before choosing.
In summary: tasks determine the model, compliance determines the scope, and the gateway determines efficiency. For unified multi-model access, consolidate through an AI API aggregation platform early so that changing models later will not cause major disruption.
Author: Zhao Qiming
Published: November 3, 2026