Who Should Use the Free DeepSeek Entry Point?

I spent two days trying DeepSeek's web version and App. This discussion focuses on the entry point for regular users who chat, edit drafts, and read materials daily.

Day one, I started with the web version. Opening the page pops up a Cookie consent box. Cookies are small bits of data in browsers recording login status and preferences—not viruses, but wrong choices affect functionality. I clicked necessary cookies and continued logging in. Phone number verification went smoothly; I didn't get stuck. The interface is plain, with an input box in the center and model selection nearby. The default model I saw was DeepSeek-V4-Pro, plus a "Deep Thinking" toggle. Deep Thinking can be understood as making the model break down steps before answering—slower, but more stable for complex problems.

I first asked the most beginner question: What is an LLM? It answered properly, giving a definition then examples. An LLM is a Large Language Model trained on massive text, primarily capable of generating text. It didn't throw jargon at me, which is good for novices. Next, I asked it to translate an English abstract into Chinese and compress it into three bullet points. Output was clean, no fabricated sources. However, when I asked it to write "risk control rules for a certain payment platform," it gave a general framework first, then held back, refusing to provide actionable bypass methods. This isn't bad; free entry points follow rules, but novices need to get used to it not making decisions for you.

Day three, I switched to the App. Mobile login interface was straightforward; after entering phone number, there was an extra confirmation step—I didn't get stuck. The App's advantage is asking on the go; the disadvantage is editing long texts. I pasted a meeting minute into it, and line breaks and indentation got messy. Code indentation relies on spaces for hierarchy; once messy, asking the model to fix code later leads to misjudgments. Later, I switched back to the web version and put content in Markdown code blocks, reducing issues significantly. Markdown is a simple notation for formatting, suitable for distinguishing code, tables, and titles.

Same day, I tested Agent capabilities. An Agent is an intelligent entity that breaks steps according to goals. Recently, I'm building an agent demo in AI Lab, so I tried it for light tasks. I asked it to "Help me prepare a DeepSeek usage sharing session." It listed what the audience would ask, gave an outline, then wrote the script. Steps were clear, and Chinese was smooth. But it mixed up "Web Entry" and "API Access," requiring correction. API is a programmatic interface; Codex leans towards code task integration methods. These two aren't things you can mix up by just clicking in a chat box.

I also tested long context. Million-token context means the model can remember a lot of text at once, equivalent to stuffing a batch of materials into the same conversation turn. I combined an experiment description, meeting minutes, and some chat records, then asked, "Which part mentions insufficient computing power?" It located it accurately and reminded me the original text didn't give specific quantities. I was genuinely surprised here. A free entry point achieving this level is convenient for students writing reviews or product managers organizing requirements. But it's not omnipotent. Sometimes it interprets "Section 3" as what I said, rather than the third title numbered in the document. Long context solves "capacity," not automatically guaranteeing "accuracy."

After a week, I viewed it separately from development integration. My colleague integrated APIs in dev environments; I mainly use web and App for light tasks. My conclusion is conditional recommendation. Suitable for those new to LLMs, editing Chinese drafts, reading long docs, or doing preliminary research. Not suitable for treating it as a production system, strict compliance audits, or casually dumping sensitive data. Free entry points save costs, but boundaries, privacy, and stability require self-assessment.

Light entry point, stable Chinese expression, long context helps with reading materials, Deep Thinking toggle breaks down complex problems. App handles long text worse than web, avoids sensitive topics, and still needs manual proofreading for locating details in long texts.

DeepSeek web and App are suitable as free Chinese companions, not as boundary-less production systems.