DeepSeek V4 Beginner's Guide: How to Choose Between Flash and Pro

After DeepSeek V4 launched, search interest around it clearly surged, but many people's questions aren't about technical details—they're about the most basic things: How exactly do you use this version of the model? And between the Flash and Pro tiers, which one should you pick? This article is written for ordinary users and developers just getting started with the API. No parameter dumps—just clear paths and selection logic.

What Is DeepSeek V4

DeepSeek^Ad V4 is the latest generation large model series from DeepSeek, continuing the two main threads of previous generations: Chinese language expression capability and cost control for API calls. Based on official and community discussions, V4 is divided into different tiers by capability and cost. The most frequently mentioned are Flash, which leans toward speed and low cost, and Pro, which leans toward the upper limit of capability.

It's worth noting that V4's specific parameter count, context window, and pricing standards are continuously being adjusted, and different sources don't always agree. This article doesn't cite unverified benchmark scores or price figures. For specifications, it's best to consult DeepSeek's official documentation directly. For ordinary users, remembering the basic impression of "Flash is fast and cheap, Pro is strong but slow" is already enough.

Three Ways to Use It, From Lowest to Highest Barrier

Web Version: Register and Go

Open the chat page on DeepSeek's official website and register with your phone number to get started. It's directly accessible within China, suitable for everyday Q&A, writing, translation, learning assistance, and similar scenarios.

One reminder: when searching for "DeepSeek," you may easily encounter counterfeit sites. It's recommended to enter through official channels and not log into your account through redirect links of unknown origin.

Mobile App: Check the Developer

Search for "DeepSeek" in a legitimate app store—the developer field should show Hangzhou DeepSeek. There are quite a few similarly named "wrapper" apps; some try to induce payment, others collect personal information. Taking one extra look at the developer info before downloading will help you avoid most pitfalls.

API: Compatible with OpenAI Format

The developer route is to register on the DeepSeek open platform, top up, and create an API Key. The interface is compatible with the OpenAI format, which means many existing tools only need to change the base_url and model name to connect. The migration cost is lower than you'd expect. Billing is calculated by token, suitable for integrating into your own applications, writing tools, or code assistants.

OpenAI format refers to a widely adopted set of HTTP interface conventions: requests are sent to paths like /v1/chat/completions, messages are organized as a messages array, fields include model, temperature, max_tokens, etc., and the response body is a choices list. Because OpenAI first established this as the de facto standard, a large number of tool chains—including LangChain, various AI clients, IDE plugins, and custom scripts—have hardcoded their calling logic to this protocol. Most compatible implementations retain the vast majority of common fields, but a few vendor-specific fields may not work. So when connecting to DeepSeek, you typically only need to change two things: point base_url to DeepSeek's endpoint, and replace model with the corresponding model name. If your original code uses function calling or structured output, it's best to verify with a minimal request first before migrating in bulk.

Token is the smallest unit of measurement for text processed by the model, and it's not equal to character count. In English, one token roughly corresponds to 0.75 words; in Chinese, a common character takes about 1 to 2 tokens, depending on the tokenizer. Billing accumulates input and output separately, so long prompts, long document uploads, and long responses all drive up costs. Understanding this helps with budget control: truncating unnecessary conversation history, avoiding repeatedly pasting the same material, and setting reasonable max_tokens for batch tasks are often more cost-effective than switching models.

Flash or Pro: A Good-Enough Decision Criterion

No need to study architectural differences—choosing by task type is sufficient:

• Chat, translation, summarization, batch text processing: Choose Flash. These tasks don't demand deep reasoning, speed and unit price are the main concerns, and Flash offers better value.
• Complex reasoning, long code, math problems, multi-step tasks: Choose Pro, or enable deep thinking mode. These scenarios are more sensitive to the correctness of intermediate steps, and the upper limit of capability matters more than response speed.
• When unsure: Run Flash first, and if you're not satisfied with the output, switch to Pro and compare results using the same prompt. This "low-cost trial and error" order usually gets you an answer faster than going back and forth.

Web version users generally don't need to manually select specific models—the mode toggles on the page already do the mapping.

Common Questions

Is it free? The web version and App are currently free for individual users, though there may be queuing or busy messages during peak hours; API is billed by actual usage.

What to do when you encounter "server busy"? Try a different time slot, or use a third-party platform that has integrated DeepSeek models. When choosing, prioritize legitimate cloud providers and well-known AI assistants, and avoid obscure small sites.

Can it be deployed locally? DeepSeek has a history of open-sourcing weights, but the VRAM requirements for the full large model far exceed ordinary computers. If you want to run it locally, a more realistic approach is to deploy a distilled small model. Tools like Ollama can lower the barrier to entry.

How to ensure data security? The core principle is not to upload sensitive materials like ID numbers, original contracts, or company secrets. This rule applies to all online AI services—it's not a problem unique to DeepSeek.

Alternatives When You Can't Use It

If you encounter unstable access or limited quota, there are other large models available domestically that you can use directly, such as Doubao, Kimi, Tongyi Qianwen, Tencent Yuanbao, and Zhipu Qingyan. Different products have different emphases on long text, web search, and document processing. It's worth trying each one according to your own use case rather than sticking to just one.

Prompting Tips: Take Output Quality Up a Notch

• Explain the background, goal, and output format all at once. For example, "I'm a sophomore, please explain XX in 300 words, divided into three points" is noticeably more effective than just throwing out a term.
• Break complex questions into several steps, confirming each step before continuing. Throwing out five layers of requirements at once makes it easy for the model to lose track.
• For key facts, ask it to provide evidence or sources, and verify them yourself. Fluent expression doesn't mean the content is necessarily accurate.

Models iterate quickly. For specific features and pricing mentioned in this article, please refer to DeepSeek's official announcements.

Background Supplement

Open-source weights and distilled models are two different things that are easily confused. Open-source weights mean the model parameter files can be downloaded and run on your own; but even if a large model with hundreds of billions of parameters has its weights publicly available, it requires tens or even hundreds of GB of VRAM, which consumer-grade GPUs basically can't handle. Distillation is a different approach: use a large model to generate large amounts of high-quality Q&A data, then train a model with far fewer parameters, letting the small model imitate the large model's performance on specific tasks. Distilled small models commonly come in sizes like 7B, 14B, and 32B. After quantization and compression, they can run on a single consumer-grade GPU or even pure CPU. Ollama is a typical example of this kind of tool—it packages model downloading, quantization, and inference services into a few commands, lowering the barrier to local deployment to "install and start chatting." The trade-off is a clear gap in capability compared to the full model. It's suitable for privacy-sensitive, offline, or learning purposes, but not for replacing cloud models in complex reasoning tasks.

One-line Summary

DeepSeek V4 comes in Flash and Pro tiers. This article clarifies the selection logic for the three ways to use it: web, App, and API.

Paragraph Summary

This article is aimed at ordinary users and developers just getting started with the API, addressing two fundamental questions about DeepSeek V4: how to use it, and how to choose between Flash and Pro. It first explains that V4 is divided into tiers by capability and cost—Flash leans toward speed and low cost, Pro leans toward the upper limit of capability—and notes that specific parameters and pricing should be based on official documentation. It then introduces three usage paths from lowest to highest barrier: web version registration, mobile App with developer verification, and API compatible with OpenAI format with low migration cost. For selection criteria: choose Flash for chat, translation, and summarization; choose Pro for complex reasoning and long code; when unsure, try Flash first and compare. Finally, it covers common questions about free policy, server busy issues, local deployment, and data security, offers alternative models, and includes prompting tips.