Testing Baichuan 4 Base Model: Search Augmentation is the Strength

I compared Baichuan 4 and Qwen3.8-Max by actually running them. I mainly used the BaiXiaoYing APP to ask three types of questions: looking up Ascend compiler documentation, explaining IR optimization notes (records of model computation graph intermediate representation optimizations), and organizing meeting minutes. I also reviewed Baichuan’s public statements regarding the positioning of Baichuan4-Turbo, focusing on enterprise high-frequency scenarios, cost-performance ratio, and speed. I haven’t integrated the official API yet, so I’m assessing capabilities via the APP and public docs.

First, let’s define two terms. The base large model is the underlying model responsible for generation, reasoning, and understanding; BaiXiaoYing is the AI assistant packaged by Baichuan combining the model with search. Ordinary users typically interact via the APP or API; fewer people call the model directly.

Opening BaiXiaoYing on my phone, the homepage after login is a chat box. I entered "Why is operator fusion important in the Ascend AI compiler?" It didn’t stop at generic explanations but broke down the points, with a tone like a search summary. This approach aligns with Baichuan’s public claims. Search feels embedded in the answer rather than bolted on. In compiler terms, operator fusion merges multiple small computations into one step to reduce intermediate result transfer. This is just an analogy.

The surprise was in Chinese technical expression. When discussing memory bandwidth bottlenecks (where data transfer can’t keep up with computation) and operator fusion, there were no obvious stiff translations; the phrasing was natural. I switch between Ascend and NVIDIA regularly and have seen many models drift when touching hardware terminology. This round, it didn’t drift.

The sticking points are also clear. When I asked for "a practical optimization sequence," it started giving generic advice: look at hot operators first, then memory access, then fusion. The direction wasn’t wrong, but it lacked detail. It didn’t proactively distinguish hardware backend differences or warn which IR passes might interfere with each other. It’s sufficient for general users, but for those doing low-level optimization, the information density isn’t enough.

Search augmentation is smooth. For real-time questions, you can see traces of "it checked," rather than just fabricating from memory. Chinese technical Q&A is stable. Explaining compilers, Ascend, and memory bandwidth feels more natural than some general-purpose models. Product positioning is clear. Baichuan4-Turbo emphasizes enterprise scenarios, suitable for treating BaiXiaoYing as a front-end entry point rather than just staring at base model parameters. Compliance awareness is strong. Usage requirements state that "Powered by Baichuan" must be displayed, and outputs affecting accuracy cannot be modified. This is good for enterprise integration, though it limits secondary packaging.

Long tasks aren’t solid enough. Reading notes, summarizing, and converting to client explanations often miss details. Previously, I encountered missing table titles and initially thought the model was dumb, but later realized it looked more like input truncation. This time had a similar vibe. Local deployment paths are unfriendly. Having just started with Hugging Face a few days ago, I searched for Baichuan-related open-source projects. I saw single-card deployment guides for models like M2, but Baichuan 4 base model resources aren’t centralized. Privatization requires significant homework. Vertical capabilities need separate evaluation. Baichuan has finance and healthcare materials, but this doesn’t mean the Baichuan 4 general base is naturally strong in these domains; application layers and eval sets differ greatly. There’s still a gap compared to top-tier models. On the same questions, Qwen3.8-Max is more stable in multi-turn code and long contexts. Baichuan 4’s advantage feels more like a search assistant experience.

Recommendations depend on the situation. Suitable for Chinese Q&A, enterprise search assistants, and internal knowledge base front-ends, especially for business units wanting models to speak substantively without building a complete retrieval pipeline themselves. Versions like Baichuan4-Turbo, leaning towards enterprise scenarios, might be easier to implement than bare base models.

Not suitable for two groups. Those wanting to run full-power Baichuan 4 locally with low latency, high throughput, and modifiable outputs. Those primarily doing complex code, long document analysis, or multi-step agents. In these tasks, search augmentation may not offset context and tool-call complexity.

Actionable advice: Don’t connect to production yet. Take 30 real business questions, run them through BaiXiaoYing, and record whether answers need manual editing, if cited facts are accurate, and if response times are acceptable. If most are usable directly, discuss integration; if most need rewriting, the problem likely lies in the knowledge base, prompts, or retrieval quality. If Baichuan continues binding search and models, BaiXiaoYing will have more distinctiveness than a pure base model.