In this article＋
01 / ARTIFICIAL INTELLIGENCE
What DeepSeek AI actually is
That last sentence matters more than it looks. A person trying DeepSeek in a browser is using a hosted service. A developer calling deepseek-flash is buying API access. Someone downloading weights from Hugging Face is taking on an infrastructure project. All three may say “DeepSeek”, and all three come with different limits, privacy questions and costs.
DeepSeek became impossible to ignore when its R1 reasoning model set off a global argument in early 2025. The arguments were about benchmark scores, open weights, training efficiency, chip restrictions, market valuations and whether frontier AI really needed the enormous budgets everyone had been treating as natural law. A fair bit of the conversation got silly. That happens when technology meets a stock market headline.
The useful way to look at DeepSeek now is less dramatic and more practical: it is one of the most important open-model labs in the current AI market, and its design choices have pushed the whole industry to talk more seriously about inference cost, reasoning, long context and agent work. The question is not whether DeepSeek “beat” every other assistant. It did not, and nobody does that across every task. The better question is where its particular mix is a good fit for you.
This article walks through the company’s current model direction, what the technical terms mean in ordinary language, how you can use the service, where the budget advantage comes from, and the bits I would not hand over blindly — especially private documents, high-stakes decisions and claims that sound too smooth to be true.
02 / HOW WE GOT HERE
From the R1 shockwave to V4.1 Flash
DeepSeek’s reputation was built in layers. R1 was the loudest layer because it made “reasoning model” feel like a consumer story, not only a research-paper phrase. It showed longer visible reasoning, strong results on mathematics and coding tests, and a model-weight release that invited people to run, inspect and adapt it. The exact experience depended on the version and the interface, but the wider point landed: a lab did not need to keep every capability behind a closed chat window.
Before and after R1, the V-series supplied the general-purpose backbone. V3 and later releases focused on a broad mixture of language, coding, reasoning and agent tasks. V4, announced in April 2026, moved the public conversation toward a one-million-token context window and stronger agent work. By September, V4.1-Flash became the official current Flash model, adding native visual understanding and a new architecture intended to reduce the cost of long, input-heavy work.
| Stage | What people noticed | Why it still matters |
|---|---|---|
| DeepSeek-V3 | A serious general model with an efficiency story | It gave the lab a broad foundation beyond a single reasoning demo. |
| DeepSeek-R1 | Reasoning, mathematics, coding and open model weights | It made the cost and openness of frontier-style reasoning part of the mainstream debate. |
| DeepSeek-V4 | One-million-token context and agent-oriented capability | It shifted attention from chat answers to long-running work with tools and documents. |
| DeepSeek-V4.1-Flash | Native vision, thinking controls and lower-cost serving | It is the current practical Flash direction for multimodal and agent workloads. |
There is a small trap in this timeline. People often compare a 2025 R1 chat experience with a 2026 V4.1 API page as if they were two buttons in the same app. They are not. Models, wrappers, prompts, safety layers, search tools and account limits all affect what you actually feel. When someone says “DeepSeek is amazing” or “DeepSeek is terrible”, I want to know which version, which interface and which task before I take the sentence seriously.
That is not pedantry. It is the difference between reviewing a car and reviewing a particular taxi ride.
03 / UNDER THE BONNET
Why the architecture matters
The technical phrase you will see most often is MoE: Mixture of Experts. Imagine a large workshop with many specialist teams, but only a few teams are called to handle each part of an order. The full workshop can be huge; the active work for one token can be much smaller. This is not exactly how the model “thinks”, but it is a useful first picture.
DeepSeek-V4.1-Flash is described by the official release as a 552-billion-parameter Mixture-of-Experts model. The same release says that only 8 billion parameters are activated during input processing and 16 billion during output generation. Total parameters and activated parameters answer different questions: the first hints at the size of the whole set of learned weights; the second is closer to how much computation is used for a particular part of a request.
The model also uses a Causal Encoder-Decoder structure, sparse attention and a heavily compressed KV cache. KV cache is the short-term working memory a model keeps while it processes a long conversation or document. Shrinking it matters because long context is not free: memory bandwidth, storage and movement of data can become the bottleneck before raw arithmetic does.
The broad size of the model’s learned weight collection. Impressive, but not a direct estimate of the cost of every response.
The portion used for a token at a given stage. Lower activation can improve serving efficiency, though it does not make the whole model small.
How much input and conversation can fit into one request. A large number is useful only if the model can find the important passage inside it.
Temporary information retained during generation. Compressing it can reduce memory pressure, especially in long or repeated agent tasks.
This is where DeepSeek’s cost story becomes more interesting than a simple claim that it used fewer chips. Efficiency can come from the model architecture, the training recipe, the serving stack, cache reuse, scheduling and pricing decisions. One company can publish a cheap API while another spends more for a different capability, uptime promise or support model. Do not turn one price table into a law of physics.
The official V4.1 technical material reports a much smaller KV-cache footprint than earlier DeepSeek generations and gives benchmark results under specific settings. Those numbers are useful clues. They are not a guarantee that a long PDF from your company will run at the same speed, or that your particular coding task will resemble the benchmark. The boring details — prompt length, cache hits, output length and tools — are usually where the bill hides.
04 / THE CURRENT SPECIFICATION
What the current model can do
For ordinary users, the current DeepSeek-V4.1-Flash feature list is more useful than the parameter headline. The official API documentation lists a one-million-token context length and a maximum output of 384K tokens. It supports both non-thinking and thinking modes, JSON output, tool calls, the Responses API, an Anthropic-compatible API format and vision input. The website and app may expose a different set of controls, so do not assume that an API feature automatically appears in the chat box.
| Capability | What it means in plain language | The catch |
|---|---|---|
| 1M-token context | A very large document set or long-running conversation can fit into one request. | More context is not the same as better attention. Important facts can still get buried. |
| Thinking and non-thinking | You can trade speed and cost against a deeper reasoning pass where supported. | Longer reasoning is not automatically more correct; it can simply be longer. |
| Native vision | The model can process images together with text rather than relying on a separate description step. | Charts, tiny text and screenshots still need careful checking. |
| Tool calls and JSON | Developers can connect the model to software and ask for structured output. | A valid JSON object can still contain a wrong answer. |
| OpenAI and Anthropic formats | Existing integrations may be easier to adapt. | Compatibility at the API shape does not mean identical behaviour or limits. |
The API pricing page currently lists deepseek-flash as the model name for DeepSeek-V4.1-Flash. Its displayed price is charged per one million tokens and varies between peak and off-peak periods, as well as cache hits and cache misses. At the time checked, the Flash table showed US$0.003 per million cached-input tokens off-peak, US$0.15 per million uncached-input tokens off-peak, and US$0.60 per million output tokens off-peak; peak rates were higher. The page itself says prices may change, so treat those numbers as a snapshot, not a promise carved into stone.
For a feel rather than a sales pitch: 100,000 uncached input tokens plus 20,000 output tokens at the displayed peak Flash rates would be about US$0.054 before any other service cost. That is cheap compared with many hosted AI calls, but it is not the price of the consumer chat service, and it tells you nothing about the engineering time needed to build a useful application around it.
One more distinction: official benchmark tables are measurements, not personality tests. A model can be strong at long-horizon coding while producing a clumsy marketing paragraph, or handle a giant technical file while missing a short but ambiguous question. Test the actual work you care about.
05 / THREE WAYS TO USE IT
Chat, API, local model: three different things
“I want to try DeepSeek” can mean three completely different things. Choose the wrong route and you may spend an afternoon solving a problem you did not actually have.
| Route | What you get | Who it suits |
|---|---|---|
| Official web or app | A ready-made chat experience with whatever tools and model the current account exposes. | People who want to ask questions, analyse ordinary files and test the feel of the assistant. |
| Official API | Programmatic access, token billing, model parameters, structured output and tool integration. | Developers building a product, internal workflow or repeatable automation. |
| Open weights / local serving | More control over infrastructure, model routing and where the request runs. | Teams with GPU capacity, engineering time, monitoring and a real reason to operate the stack themselves. |
The official DeepSeek site links to the web chat, app download, API platform and documentation. The API docs use an OpenAI-compatible base URL and also document an Anthropic-compatible endpoint. DeepSeek Harness is a separate open-source desktop and web-oriented project in public preview, built around plugins, workspaces, files, coding and automation. It is closer to an agent workbench than a plain chat window.
Local deployment deserves a reality check. The model card says the weights are released under the MIT licence and provides paths for Transformers, vLLM, SGLang and Docker. That is genuinely useful for research and for organisations that need control. It does not mean a normal laptop can casually run a 552B-class model at full quality. Quantisation, memory planning, multi-GPU networking, storage, cooling, inference software and maintenance all enter the picture.
If your actual need is “summarise 20 pages every Friday”, use the hosted service or API first. If your need is “keep sensitive internal material inside our controlled environment and serve a model at scale”, then local deployment might be worth the trouble. Start with the requirement, not the romance of owning the weights.
Also check which licence belongs to which component. A model weight licence, a desktop harness licence, a third-party plugin licence and the terms of the hosted API are separate documents. “Open source” is not a magic umbrella that covers every piece of the stack.
06 / WHERE IT EARNS ITS KEEP
Where DeepSeek is genuinely useful
DeepSeek is most interesting when the task is large enough for efficiency or context to matter, but ordinary enough that you do not need a research lab to begin. The following are not promises of perfect performance; they are sensible places to run a comparison.
Compare several versions of a proposal, find changed obligations, extract a decision list or ask which claims still need a source.
Explain an unfamiliar function, propose a test plan, trace an error across files or turn a rough requirement into a first implementation.
Translate technical material, preserve names and numbers, and produce different versions for Hong Kong, Mainland China or international readers.
Run repeated classification, extraction or drafting jobs where the cost per token affects whether the workflow is viable.
Use tool calls and structured output to let a model move between search, files, code or internal systems under defined rules.
Test a model inside your own environment when control, auditability or custom adaptation matters more than convenience.
A Hong Kong example: imagine a small team comparing a Chinese supplier, an English product page and a technical PDF before a client meeting. Ask DeepSeek to build a four-column table — confirmed specification, source location, unresolved question and suggested follow-up — then check the original pages yourself. That is useful. Asking “which supplier is best?” and forwarding the first answer to a client is a different, much less sensible activity.
For SEO and marketing work, I would use it for first-pass clustering, content briefs, bilingual rewrites, technical explanations and pulling a long source document into a working outline. I would not let it decide whether a statistic is true, invent a product feature, or publish a sensitive claim without a human opening the source. Cheap tokens make it affordable to check more things; they do not remove the need to check them.
For students, it can act as a patient tutor if you ask it to question your reasoning rather than simply hand you an answer. For developers, it can reduce the blank-page problem. For business owners, it can make a messy pile of notes look like a set of decisions. The value is usually in the second pass — the part where someone says, “Fine, now show me what is missing.”
07 / ASK FOR A WORKING ANSWER
How to ask it without getting a tidy mess
You do not need a secret prompt that sounds like it was found in a wizard’s cupboard. You need to tell DeepSeek what job it is doing, what material it is allowed to use and what a usable answer looks like.
- Name the job.Say compare, extract, challenge, rewrite, debug, plan or draft. “Tell me about this” leaves too much room for theatre.
- Give it the right material.Attach the document, paste the relevant section or provide the exact URL. Do not make it guess which version you mean.
- Set the audience and language.Say Hong Kong customer, Mainland Chinese engineer, international buyer, Traditional Chinese or English.
- Define the evidence rule.Ask it to separate what is in the source, what it infers and what it cannot verify.
- Specify the shape.Use a table, numbered actions, a draft email, a list of risks or a short executive summary.
A prompt I would actually use is: “Read the attached product comparison. Create a table with model name, confirmed specifications, source page, missing information and questions for the supplier. Do not fill gaps from memory. Keep numbers exactly as written. Use Traditional Chinese for Hong Kong readers. End with a five-line recommendation that clearly labels any inference.”
For code, include the language, runtime, error message, expected behaviour and what you already tried. For a long report, ask for page numbers. For a translation, say whether names and quoted sentences must remain unchanged. For a current topic, give a cutoff date and ask the model to distinguish an official announcement from a discussion post.
I like this approach because it makes the model show its seams. A confident paragraph can hide a bad assumption; a table with a source column has fewer places to hide. Not none. Fewer.
08 / OPEN DOES NOT MEAN FREE
Open weights do not mean zero cost
DeepSeek’s open-model approach is a major part of its appeal, but the phrase “open source” often gets stretched until it means whatever the speaker wants. The current V4.1-Flash model card lists an MIT licence for the repository and model weights. That gives developers meaningful rights to use, adapt and distribute the material under the licence terms. It does not make electricity, GPUs, storage, engineering salaries or support disappear.
There are at least four separate costs: downloading and storing weights, serving requests, maintaining the inference stack, and dealing with failures. A local model can be cheaper at high volume, or much more expensive at low volume. If one GPU sits idle for most of the week, your “free” inference has simply been paid for in a less visible currency.
| Claim you may hear | The more accurate version |
|---|---|
| “The model is open, so I can run it anywhere.” | The weights and licence may allow it, but hardware, supported runtimes and memory requirements still decide whether it is practical. |
| “The API is cheap, so the whole project is cheap.” | Token cost may be low; data preparation, monitoring, retries, evaluation and product integration still cost time. |
| “It has one million context, so it understands one million tokens.” | It can accept a large context under the documented conditions; retrieval and attention quality still need testing. |
| “The benchmark says it wins.” | The score comes from a dataset, prompt, scaffold and evaluation rule. Your work may be a different sport. |
If you are considering local deployment, run a small proof of concept first. Measure time to first token, tokens per second, memory use, failure recovery, output quality on your own documents and the cost of keeping the service alive for a month. A spreadsheet is less exciting than a model announcement, but it is much better at stopping a bad procurement decision.
09 / PRIVACY AND THE PARTS YOU OWN
Privacy, bias and the parts to check yourself
Privacy is not a footnote for DeepSeek. It is one of the first practical questions to answer before you drag a contract, customer list or unreleased product plan into the chat box.
The policy also describes choices around model training and data deletion, but settings, service type and applicable law matter. Read the current policy and the specific API or Harness terms before using confidential material. This is a workflow decision, not a political slogan.
That does not mean “never use DeepSeek”. It means separate low-risk experiments from sensitive work. A public product description is one thing. A private tender, passport scan, customer complaint containing a phone number or a production API key is another. The model cannot put the secret back in the bottle after you submit it.
The same caution applies to accuracy and content boundaries. DeepSeek can produce an answer that is coherent, detailed and wrong. On politically sensitive or culturally loaded topics, its response may also differ from what you would get from another assistant. You do not need to turn every conversation into a geopolitical argument; you do need to know that a refusal, omission or oddly careful paraphrase is itself part of the product behaviour you are evaluating.
For medical, legal, financial or safety questions, use DeepSeek to organise questions and locate documents, then use qualified human advice for the decision. For company use, create a simple rule: what may be uploaded, what must be redacted, which account may access it, and who checks the output before it leaves the organisation. Four short rules beat a two-hour lecture that nobody remembers.
If you use an API or local model, do not assume the privacy position is identical to the consumer chat service. The route, provider, logging settings, plugins and downstream tools can change the data path. Trace the whole path, not just the logo in the corner.
10 / WHICH TOOL SHOULD GET THE JOB?
DeepSeek beside ChatGPT, Gemini, Claude and Perplexity
DeepSeek is not automatically the best replacement for every other assistant. It is a strong option when open weights, Chinese-language work, long context, coding, agent experiments or token economics matter. Another tool may be the better choice when you need a particular file workflow, a more mature ecosystem, a different writing style or a source-first research experience.
| Tool | A useful default tendency | When I would try it first | What I would still check |
|---|---|---|---|
| DeepSeek | Efficient open-model research, coding, Chinese-language work and API experimentation | You care about model access, long context, cost or local deployment options | Current model, data route, output quality and tool behaviour |
| ChatGPT | Broad assistant and workbench with many connected tools | The task moves between writing, files, analysis and several kinds of creation | Which model, mode and account limits are actually active |
| Gemini | Strong fit for work connected to Google’s ecosystem | Your source material already lives in Google services or the task needs that integration | Permissions, region and whether the specific integration is enabled |
| Claude | Measured long-form analysis and coding conversation | You want careful drafting, large-context reading or a particular coding workflow | Freshness, integrations and the exact plan’s limits |
| Perplexity | Search-first answers with a visible source trail | The main deliverable is finding and comparing current sources | Whether the citation supports the whole sentence, not just one fragment |
These are tendencies, not permanent identities. Products change, models are routed behind friendly names, and a wrapper can alter the experience more than a version number suggests. Use a small test set from your real work: one document task, one writing task, one coding task and one current-information task. Score accuracy, useful detail, correction effort, speed and total cost. Then choose based on the work, not the loudest fan club.
My short version: DeepSeek is worth trying if you want to understand where open models and efficient inference are going. It is especially worth trying through a controlled, low-risk task. It is not a permission slip to stop reading sources or start uploading everything.
11 / START WITH ONE REAL TASK
A sensible way to start
Open the official web service or app, or create a small API test. Pick something mildly annoying and concrete — a long product manual, a messy comparison, a bug report or a bilingual piece of copy. A toy prompt can make any assistant look clever; your actual work is the better judge.
- Choose material you are allowed to upload.Begin with public information or a cleaned sample. Remove names, credentials and confidential numbers.
- Ask for extraction before judgement.Make the model list facts, quotations, dates and missing pieces before asking for a recommendation.
- Run one follow-up.Ask which claim is weakest, what it may have misunderstood and what you should verify manually.
- Compare the answer with another tool or the original source.The goal is not to crown a winner; it is to see where DeepSeek saves time and where it creates more checking work.
- Write down the boundary.Keep a small note of what your team may upload, which model or endpoint was used, and who approves the final output.
The best result may not be a dazzling paragraph. It may be a clean list of 17 questions you should have asked yesterday. That still counts. In real work, removing confusion is often more valuable than adding more words.
DeepSeek matters because it has made model efficiency, reasoning and open access part of the everyday conversation. V4.1-Flash shows the direction clearly: longer context, multimodal input, agent-oriented tools and aggressive attention to serving cost. Use those strengths with a bit of discipline, and it can be a very capable workmate. Treat the benchmark as a personality certificate, and you will probably end up doing more cleanup than work.
12 / COMMON QUESTIONS
Questions people usually ask
Is DeepSeek free?
The official chat service and app may offer free access, while API usage is billed by tokens and local deployment has hardware and operating costs. Plans, limits and availability can change, so check the official service page rather than relying on an old screenshot.
Is DeepSeek the same thing as DeepSeek-R1?
No. DeepSeek is the company and product family. R1 is a reasoning model that became famous in 2025. The current official model direction includes the V4 series and V4.1-Flash. A website, app or API may route you to a current model without presenting the name in the same way.
Can I use DeepSeek in Hong Kong?
Access depends on the current official web service, app distribution, network, account and product route. The official homepage links to the web chat, app download and API platform. Check the live page at the time you want to use it; an old regional workaround may no longer be necessary or may no longer work.
Does one million tokens mean I can upload anything?
No. It is a context capacity, not a privacy exemption or a promise that every page will be understood equally well. You still need to check file limits, account settings, data policy and the model’s ability to locate the relevant material.
Can DeepSeek run locally?
The current V4.1-Flash model card provides local-serving paths and lists an MIT licence for the weights and repository. Whether it is practical depends on your hardware, quantisation, inference stack, performance target and maintenance budget. For most individuals, trying the hosted service or a small API call is the easier first step.
Is DeepSeek better than ChatGPT or Claude?
There is no honest universal answer. DeepSeek may be the better fit for a particular mix of open weights, Chinese-language work, context, coding or cost. Another assistant may be better for your files, tools, writing style or source workflow. Test the same small tasks in each service and compare the work left after the answer.
Should I upload confidential company documents?
Not until you have checked the relevant privacy policy, account settings, contract and company rules. Redact sensitive information, use an approved environment and understand whether downstream tools or plugins also receive the material.
SOURCES
Sources checked
- DeepSeek official homepage
- DeepSeek-V4.1-Flash announcement
- DeepSeek API models and pricing
- DeepSeek Harness
- DeepSeek-V4.1-Flash model card and technical report
- DeepSeek transparency centre
- DeepSeek privacy policy
- DeepSeek app download
Model names, access routes, pricing, policies and account features can change. This article reflects the official DeepSeek pages checked on 2026-10-13; verify the live product and policy pages when a current detail matters.