After Using WorkBuddy for a Month, I Downgraded Chat Models in My 2026 AI Tool Review

After reading that side-by-side review of DeepSeek, Doubao, Kimi, Tongyi Qianwen, and ERNIE Bot, my judgment is: if your main tasks are still Q&A, research, copywriting, or reading papers, these chat models indeed each have their strengths. I've used Doubao for 4 weeks; its lifestyle info and hot topics feel very grounded. Kimi's long-text summarization is indeed suitable for digesting reports. I tested DeepSeek's code and reasoning, and it's stable. But if you handle PDFs, screenshots, Excel sheets, meeting minutes, reimbursement forms, and weekly project reports every week, WorkBuddy feels more like an office than a chat model.

This judgment formed gradually. I'm a junior in college, and club and course project materials are particularly fragmented. Last Thursday night, I had to submit a course project weekly report. I had 18 Feishu-exported PDFs, 9 chat history screenshots, 1 Excel task sheet, and 4 meeting minutes. Initially, I habitually opened Doubao, wanting it to summarize directly. At the Q&A level, it was fast, and the tone felt human, but I had to copy files segment by segment or upload them one by one. As the context grew longer, it started mixing up the "Owner," "Notes," and "Risk" columns. Later, I switched to WorkBuddy. The approach was clumsier: I created a fixed directory, sorted materials into "Meetings, Task Sheets, Screenshots, Risk Records," and then asked it to output a CSV. After running it, I got a draft in about 35 minutes. I estimated it was 70% usable; I mainly needed to fix the status column and a few misaligned owner fields. It saved me an evening, but it wasn't completely hands-off.

If forced to compare, I think WorkBuddy and chat models aren't in the same race. Tools like DeepSeek, Doubao, Kimi, Tongyi Qianwen, and ERNIE Bot excel at "asking." They handle explicit tasks like answering questions, summarizing long texts, and completing code logic well. WorkBuddy excels at "managing." It keeps materials local, organizes them by directory, template, field, and checkpoint, and finally delivers an acceptable structure. I felt this when organizing the large model landscape map earlier: asking chat models directly gives smooth answers, but versions pile up and get messy. Using WorkBuddy to save as JSON and tables made maintenance steadier afterward. This judgment hasn't changed.

There's a statement in that review I agree with.

The AI tool market in 2026 has shifted from showing off technology to scenario implementation.

But I'd add one thing: scenario implementation depends on whether context is managed, which is often more critical than choosing a smarter chat model. I wrote last week that managing context in WorkBuddy is more important than managing prompts, and I still hold that view. When I first started using WorkBuddy, I always tried to state all requirements at once, hoping it would figure it out. The more complex it got, the more likely it failed. Later, I learned to make it do one thing at a time, like extracting meeting to-dos, merging task sheets, or generating weekly report paragraphs. Speed didn't increase much, but rework decreased significantly.

Doubao's advantages are also obvious: it's good for lightweight Q&A, hot topic info, lifestyle content, and short video script inspiration, with a grounded multimodal experience. Kimi's long-text ability is great for reading dozens of pages of reports; students find it comfortable for digesting literature. DeepSeek's code, math, and logical reasoning make it more of a technical player, suitable for questions with definite answers. Tongyi Qianwen and ERNIE Bot have their places in commercial data, Chinese encyclopedias, and templated content. But once the task becomes "turn this pile of local files into a table to show the teacher," the shortcomings of chat models emerge. You need to move materials, paste repeatedly, watch formatting, and worry about context pollution. WorkBuddy isn't as chatty, but it knows where the files are and where the fields should go.

WorkBuddy has pitfalls, and not small ones. A few days ago, I wanted it to turn a bunch of video topic materials directly into a reusable storyboard table, but the exported table kept misaligning. The reason wasn't complex: the task was too mixed. It contained topics and shots, reference images, timestamps, and risk notes. I asked it to do it all at once, and it started guessing. Later, I broke the task into four steps: extract scripts, generate shot numbers, fill fields, and merge tables, and only then was it barely usable. Breaking complex needs into single operations—I've said this before, and I'll say it again. WorkBuddy is suitable as scaffolding for reviewing material organization and generating drafts, but root cause analysis, shot judgment, and copy selection still require human effort.

From a product design perspective, WorkBuddy's value doesn't rely on scary model parameters. It's more like a local file automation workstation. Directories, templates, skill toggles, and checkpoints sound unsexy, but office scenarios eat this up. In my tests, reducing skills from seven or eight to the three or four commonly used ones lowered context pressure and made output steadier. It doesn't give pretty answers to random questions; you need to give it boundaries. The clearer the boundaries, the more reliable it acts like an assistant; the vaguer the boundaries, the more likely it is to act on its own initiative.

Regarding cost-effectiveness, I don't calculate by token price. For students, time is the hard cost. Free quotas or low-price capabilities of Doubao, Kimi, and DeepSeek are enough. If you're just asking questions, reading papers, or writing code, WorkBuddy isn't necessarily a must-have. But if you handle course projects, reimbursements, club materials, and client documents every week, WorkBuddy's cost-effectiveness is actually higher. What it saves is the time spent copying, organizing, and fixing formats in chat windows. In my tests, simple weekly reports were compressed from over two hours to forty or fifty minutes. Although manual review is still needed, the relief of "finally not having to manually merge tables" is quite real.

My choice is to keep the chat models—Doubao, Kimi, and DeepSeek are all useful—but place WorkBuddy at the core of the office workflow. It's not suitable for replacing all AI tools, nor for casual chatting. It's suitable for handling dirty, tedious work: scattered files, repetitive fields, version chaos, and initial drafts that need delivery. If you mainly do Q&A, don't rush to switch to WorkBuddy. If you're tormented by spreadsheets and PDFs every week, try a small task first. Don't throw ten files at it right away; create a fixed directory, let it output a CSV, keep fields minimal, narrow the task, run it once, and see if it can really save you that afternoon.