How to Use DeepSeek in 2026: A Practical Guide

I would explain how to use DeepSeek in 2026 with one warning attached to the opportunity: it is now one of the least expensive serious AI systems to run, but its low token price can tempt users to skip the controls that make an answer dependable. DeepSeek V4 Flash costs only $0.14 per million uncached input tokens and $0.28 per million output tokens, while Reuters reported on 3 August 2026 that an independent benchmark suite placed its average test cost at about three cents. That is an extraordinary efficiency signal, not proof that every answer is correct, current or safe to publish.

The practical answer is to choose the right access route, give the model a measurable task, decide whether thinking mode is necessary, constrain the evidence it may use, and verify the output before it reaches a customer, codebase or decision. DeepSeek can work as a free chatbot, a document assistant, a coding partner, an OpenAI-compatible or Anthropic-compatible API, a backend for agent tools, or a self-hosted open-weight model. Those options are related, but they are not interchangeable.

This guide covers the current V4 model names, app workflow, prompt design, research and PDF handling, API implementation, pricing, context caching, agent integration, local hardware, privacy and failure recovery. It also separates documented facts from gaps. DeepSeek publishes API context, pricing, concurrency and feature support, but it does not publish a stable consumer message cap or a complete numerical file-upload matrix. Where the evidence stops, this article stops too.

DeepSeek in 2026: Models, Access Routes and the Important Changes

DeepSeek is both a consumer AI service and a family of downloadable models. The current hosted API centres on DeepSeek V4 Flash and DeepSeek V4 Pro. Flash is the economical default for high-volume chat, classification, extraction and agent sub-tasks. Pro is the larger option for difficult synthesis, coding and reasoning. Both support thinking and non-thinking modes, a one-million-token context window, JSON output, tool calls, chat prefix completion and fill-in-the-middle completion in non-thinking mode. DeepSeek lists a maximum output of 384,000 tokens for both models (DeepSeek, 2026a).

The naming change matters because many older tutorials still tell developers to call deepseek-chat or deepseek-reasoner. DeepSeek announced that those aliases would become inaccessible after 24 July 2026. New code should use deepseek-v4-flash or deepseek-v4-pro directly. Teams maintaining production systems should also inspect environment variables, model routers, observability dashboards and fallback rules, because an alias can survive in a place that is not obvious during a code review.

The model release itself is only one part of the product. Our DeepSeek V4 release analysis explains the wider competitive context, but the operational distinction is simpler: the app provides a managed interface with chat history, web search, file extraction and Deep-Think controls, while the API gives developers explicit control over messages, tools, output schemas and costs. Open weights offer the most control, but they transfer security, serving, monitoring and hardware responsibility to the user.

Access Route	Best For	Cost Structure	Main Constraint
Web or mobile app	Everyday questions, drafts, files and ad hoc research	Free consumer access; stable usage caps are not publicly specified	Prompts and files enter DeepSeek’s managed service
Hosted API	Products, batch processing, agents and structured outputs	Pay per input and output token, with lower cache-hit pricing	Requires engineering, balance management, retries and validation
Coding-agent integration	Repository work, terminal agents and tool-assisted development	API tokens plus any third-party tool costs	Compatibility details differ by client
Open-weight self-hosting	Data control, custom serving and research	No model licence fee under the published model licence; infrastructure varies	V4 hardware requirements are data-centre scale

How to Use DeepSeek in the Web App

Start at an official DeepSeek channel rather than a copied app or unofficial wrapper. The company says its consumer app supports email, Google and Apple sign-in, cross-platform history sync, web search, Deep-Think mode, file upload and text extraction. It also describes the app as free, without ads or in-app purchases. Those statements describe product availability, not an unlimited-service guarantee. DeepSeek has not published a stable numerical daily message cap, upload-size cap or file-count cap that can be treated as permanent in August 2026.

For a normal task, begin in non-thinking mode and write a short working contract. State the goal, audience, source boundary, output format and test. For example: “Summarise the attached policy for a UK operations manager. Use only the uploaded file. Separate obligations, deadlines, exceptions and unresolved questions. Quote no more than one sentence from any section. Finish with a five-item verification checklist.” This prompt is more reliable than “summarise this” because it defines evidence, reader and acceptance criteria.

Turn on thinking mode when the task contains dependencies, competing constraints, mathematics, debugging or a decision that benefits from deliberate analysis. Turn on web search when the answer depends on current information, then inspect the underlying sources rather than trusting the summary alone. DeepSeek’s privacy policy says search may involve third-party APIs and that input keywords may be shared to provide search services, so do not put confidential terms into a web-search query.

When uploading a long document, ask for a source inventory before requesting a final answer. The inventory should list file name, page or section, topic, key claim, uncertainty and missing context. The same evidence-first discipline is developed in our reliable DeepSeek PDF workflow. A million-token context can hold a large amount of text, but capacity does not guarantee that the decisive table, footnote or contradiction will receive enough attention.

Prompting DeepSeek for Reliable, Reusable Results

A strong DeepSeek prompt behaves like a compact specification. It tells the model what success means, what evidence is allowed, what assumptions are forbidden and how the answer will be checked. The most useful structure is: role, objective, inputs, constraints, process, deliverable and validation. Put stable instructions first, then source material, then the immediate task. This order also improves the chance of a context-cache hit in API workflows because repeated prefixes must match from the first token.

Use staged prompts for consequential work. First ask DeepSeek to restate the assignment and identify missing information. Next request an outline or plan. Then ask it to produce one section or one patch at a time. Finally run a separate audit prompt that looks for unsupported claims, skipped requirements, contradictions and ambiguous wording. A single enormous prompt can produce a fluent result quickly, but it hides which assumption caused an error.

Require uncertainty labels. Useful phrases include “not supported by the supplied evidence,” “requires current verification,” and “assumption introduced for planning only.” This reduces the chance that a plausible continuation is mistaken for a documented fact. Also specify whether the model may use general knowledge. For policy, legal, financial, medical, security or publication work, source-bounded output is usually safer than unrestricted synthesis.

How to Use DeepSeek for Coding Tasks

For code, give DeepSeek a behavioural contract rather than a feature wish. Include the language and versions, repository layout, files it may change, public interfaces that must remain stable, test commands, performance constraints, security rules and the required output type. Ask for a plan before a patch, and ask for a unified diff rather than an unexplained replacement file.

The most reliable sequence is specification, dependency check, minimal plan, implementation, tests, static analysis and human review. Our DeepSeek coding workflow uses the same controlled pattern. Do not let the model silently upgrade dependencies, edit generated files or broaden scope. A patch that looks elegant can still fail hidden tests, misuse an API or weaken authorisation.

Choosing Thinking Mode, Non-Thinking Mode and Reasoning Effort

DeepSeek V4 defaults to thinking mode in the hosted API. The official controls allow thinking to be enabled or disabled and reasoning effort to be set to high or max. Low and medium are mapped to high, while xhigh is mapped to max. In thinking mode, temperature, top_p, presence_penalty and frequency_penalty do not affect generation, even if a compatibility layer accepts them without an error. That is an easy source of false confidence when a team believes it has tuned randomness but the model is ignoring the setting.

Thinking mode is useful for multi-step code analysis, mathematical work, difficult planning and tool-assisted tasks. Non-thinking mode is usually better for high-volume extraction, classification, rewriting, short customer replies and deterministic execution stages. The distinction is economic as well as qualitative: longer reasoning produces more output tokens, increases latency and expands the surface area for irrelevant branches.

Do not treat reasoning content as a verified proof. It is model-generated text that may contain abandoned paths, hidden assumptions or persuasive mistakes. Evaluate the final answer against tests and external evidence. For production use, log the model name, mode, reasoning effort, token usage, finish reason, tool calls and validation result. Avoid logging sensitive prompts unless the organisation has approved the storage boundary.

Task Pattern	Recommended Mode	Reasoning Effort	Control to Add
Extraction or classification	Non-thinking	Not applicable	JSON schema and rejected-value handling
Short drafting or rewriting	Non-thinking first	Not applicable	Audience, tone, facts and length limit
Complex analysis or debugging	Thinking	High	Independent test or evidence audit
Agent planning with tools	Thinking	High or max	Step budget, tool allowlist and approval gate
Final tool execution	Often non-thinking	Not applicable	Validated arguments and idempotency

Research, PDFs and Data Analysis Without False Confidence

DeepSeek can search the web in supported consumer and agent experiences, and it can extract text from uploaded files. That makes it useful for creating research questions, mapping a document, identifying inconsistencies and drafting a synthesis. It does not make the output self-verifying. A current web answer should still expose the publication date, event date, publisher, author, source type and any conflict between sources.

Use a source ladder. Put official documentation, legislation, regulatory filings, peer-reviewed research and first-party data above summaries and commentary. Ask DeepSeek to distinguish a source claim from its own inference. For a PDF, request page-level evidence for every material statement and inspect images, charts and scanned pages separately. Text extraction may omit a visual caveat or misread a table structure.

For spreadsheets or tabular data, require a data dictionary, row count, missing-value report, transformation log and reconciliation total before analysis. Then ask for calculations in a machine-checkable format. DeepSeek can suggest formulas or code, but the result should be rerun outside the model. When the task needs current, claim-level citations as a first-class feature, consult a broader best AI for answering questions rather than assuming the cheapest reasoning model is automatically the best research product.

The hidden limitation is retrieval quality, not only context size. A one-million-token window can accept enormous input, but attention is still selective and long-context performance varies by task. Split a high-risk corpus into labelled batches, create an evidence index, and ask targeted questions against the index. This creates traceability and lowers the chance that one deeply buried exception disappears inside a broad summary.

Using the DeepSeek API Step by Step

Create an account in the DeepSeek Platform, add credit, generate a project-specific API key and store it in a secret manager or environment variable. Do not place the key in source code, screenshots, browser-side JavaScript or a shared notebook. DeepSeek exposes an OpenAI-compatible endpoint and an Anthropic-compatible endpoint, which makes migration easier, but compatibility is not identical in every advanced feature.

Install the current OpenAI SDK, load the key and base address from environment variables, choose deepseek-v4-flash for the first implementation, and send a small request. The example below deliberately keeps the endpoint out of the code so that configuration, development and production environments can be separated. Set DEEPSEEK_BASE_URL to the official DeepSeek API endpoint in your deployment configuration.

import os from openai import OpenAI client = OpenAI(     api_key=os.environ[“DEEPSEEK_API_KEY”],     base_url=os.environ[“DEEPSEEK_BASE_URL”], ) response = client.chat.completions.create(     model=”deepseek-v4-flash”,     messages=[         {“role”: “system”, “content”: “Return concise, evidence-aware answers.”},         {“role”: “user”, “content”: “List three risks in the supplied deployment plan.”},     ],     max_tokens=800,     extra_body={“thinking”: {“type”: “disabled”}}, ) print(response.choices[0].message.content)

For structured output, ask for JSON in the prompt and set the response format to a JSON object. Validate the result with a schema after receipt. Valid JSON is not the same as valid business data, so reject unknown enum values, impossible dates, missing identifiers and totals that do not reconcile. Add a request ID, timeout, retry policy and cost log before scaling.

The API is stateless. Your application must resend the relevant message history on every turn. Keep only the context required for the job, summarise older turns when appropriate, and maintain authoritative state in your database rather than inside a chat transcript. Use streaming for responsive interfaces, but handle keep-alive lines or server-sent-event comments. DeepSeek says a request may remain connected while waiting and can be closed if inference has not started after ten minutes.

Building Workflows and Agents With Guardrails

A workflow should separate model judgement from system action. Let DeepSeek classify, extract, propose or rank, then validate the output before a real tool changes a record, sends a message, transfers money, deploys code or deletes data. A safe architecture has an input filter, a narrow system prompt, a model call, schema validation, an allowlisted tool layer, a human approval gate for consequential actions, an audit log and a recovery path.

The most important DeepSeek-specific integration detail appears in thinking-mode tool use. When a model turn contains a tool call, the reasoning_content returned with that turn must be passed back in later requests. If the client drops it, the API can return a 400 error. This differs from ordinary multi-turn thinking conversations, where previous reasoning content does not need to be concatenated. The rule should be tested in the exact SDK and agent framework used in production.

Put a hard limit on tool steps, wall-clock time, retries and total tokens. Make every write operation idempotent or attach a unique operation key. Validate tool arguments with a strict schema and never let a model invent a tool name, table, recipient or account. The same engineering principles are expanded in our safe DeepSeek automation architecture. The surprising cost finding is that human review, workflow executions, data cleaning and failure recovery often dominate the model fee.

Prompt injection becomes part of the threat model when an agent reads web pages, tickets, emails or documents. Treat retrieved text as untrusted data, not instructions. Keep secrets outside the model context, restrict network destinations, sanitise tool output, and require approval before the agent follows instructions found inside external content.

Current Pricing, Limits and Cost Controls

DeepSeek prices the hosted API per million tokens and charges different rates for cached and uncached input. As of 6 August 2026, the official pricing page lists the matrix below. Prices can change, and the page also warns that future increases are possible, so production budgets should read the live page rather than copy a number into a permanent forecast.

Route or Model	Context	Maximum Output	Input, Cache Hit	Input, Cache Miss	Output	Published Concurrency
Consumer app	Not published as a stable product cap	Not published	Free service	Free service	Free service	Not published
deepseek-v4-flash	1M tokens	384K tokens	$0.0028 / 1M	$0.14 / 1M	$0.28 / 1M	2,500
deepseek-v4-pro	1M tokens	384K tokens	$0.003625 / 1M	$0.435 / 1M	$0.87 / 1M	500
Open-weight self-hosting	Model and serving configuration dependent	Configuration dependent	No hosted token fee	Infrastructure cost	Infrastructure cost	Your serving stack

Context caching is enabled automatically. A cache hit requires an identical prefix from the start of the input, so stable system instructions and reference material should come before the changing question. A match in the middle of a prompt does not count. Monitor prompt_cache_hit_tokens and prompt_cache_miss_tokens instead of assuming the discount is working.

Reuters reported that Artificial Analysis estimated V4 Flash at about three cents per benchmark test and 105 times cheaper than Anthropic’s Claude Fable 5 on that test set. The same report also showed a quality trade-off: V4 Flash scored 50 on the firm’s Intelligence Index, below several higher-cost frontier models. Independent product comparison remains useful, while the current price table shows why cost and capability must be evaluated together.

Control spend with per-user budgets, maximum output limits, mode routing, prompt-length alerts, cache-hit monitoring and fail-closed behaviour when the balance is insufficient. DeepSeek documents 402 for insufficient balance, 429 for rate limits, 500 for server errors and 503 for overload. Retries should use exponential backoff with jitter and a maximum attempt count. Do not automatically retry an irreversible tool action.

Coding Agents and Supported Integration Paths

DeepSeek supports both OpenAI-style and Anthropic-style API formats and publishes integration guidance for coding and agent tools. Current documentation covers Claude Code, OpenCode, GitHub Copilot CLI, Deep Code, OpenClaw, Crush, Reasonix, AstrBot, Hermes Agent, Pi, nanobot and other third-party clients. The presence of a guide does not make the third-party tool a DeepSeek product. DeepSeek explicitly warns that it cannot guarantee the effectiveness or security of third-party agents.

Choose the protocol that matches the client’s conversation behaviour. DeepSeek’s GitHub Copilot CLI guide recommends the Anthropic provider type because the OpenAI integration may fail to replay reasoning_content during thinking-mode tool turns. Some Anthropic model names are mapped to DeepSeek models, which can be convenient but can also hide which model actually ran. Log the returned model identifier and test every upgrade.

For repository work, start with read-only tools and a narrow directory. Ask the agent to inspect tests, propose a plan and explain the files it intends to touch. Add write permission only after review. Run tests, linters, type checks and security scans outside the model. A controlled DeepSeek agent loop should have a measurable job, limited tools, a step budget and a human gate before deployment or merge.

Common integration bottlenecks include long first-token latency, enormous histories, repeated repository ingestion, malformed tool arguments, incompatible reasoning fields and over-broad file access. Context caching can reduce repeated-input cost, but it does not replace repository indexing or selective retrieval. A smaller relevant context is often faster, cheaper and more accurate than sending an entire codebase on every turn.

Running DeepSeek Locally: The Hardware Reality

Open weights do not mean that the flagship V4 models are laptop models. The published V4 Flash model card lists about 284 billion total parameters with 13 billion activated, while V4 Pro lists roughly 1.6 trillion total parameters with 49 billion activated. Both support a one-million-token context. The mixture-of-experts design reduces active computation, but the full weights, expert routing, KV cache and serving stack still require serious memory and systems engineering.

The vLLM team’s initial deployment examples describe V4 Flash on four NVIDIA B200 or B300 accelerators and V4 Pro on eight B200 or B300 accelerators for single-node testing and prototyping. That is data-centre hardware. The guide also notes special handling for FP4 mixture-of-experts weights, hybrid KV caches, compressed attention, expert parallelism and evolving kernels. A consumer GPU may run a smaller distilled or quantised DeepSeek-family model, but it should not be described as equivalent to hosting the current V4 flagship.

Before downloading anything, decide whether local control justifies the engineering cost. Read the model licence, calculate storage and memory, choose an inference engine, test supported quantisation, define the context target, benchmark time to first token and throughput, and plan monitoring, patching and abuse controls. The guide to AI model weights explains why possession of weights is only one layer of a usable AI system.

Local Option	Practical Hardware Profile	Advantages	Trade-Offs
V4 Flash flagship	Multi-accelerator data-centre node; vLLM example uses 4 B200/B300	Lower hosted dependency, strong long-context and coding capability	High capital cost, complex serving and operational ownership
V4 Pro flagship	Larger multi-accelerator node; vLLM example uses 8 B200/B300	Higher reasoning and knowledge capability	Trillion-parameter-scale deployment complexity
Smaller distilled model	Workstation or server depending on size and quantisation	More realistic private experimentation	Different capability, context and licence details
Hosted API	No local accelerator requirement	Fastest route to production and current model updates	Managed-service privacy, availability and vendor dependency

Benchmark locally with your own prompts. Measure output quality, tokens per second, time to first token, memory headroom, concurrency, failure recovery and cost per completed task. A system that produces cheap tokens but needs repeated retries or constant operator intervention can be more expensive than a hosted alternative.

Privacy, Security and Governance

DeepSeek’s privacy policy was updated on 10 February 2026. It says the service may collect account information, prompts, voice input, uploaded files, photos, feedback, chat history, device and network data, approximate location and payment transaction information for paid open-platform services. It also says personal data may be directly collected, processed and stored in the People’s Republic of China. This is a material procurement fact, not a political label.

The policy says inputs may be used to improve services and train technology, while also describing a right to opt out of using personal data for model training or technology optimisation, depending on applicable rights and available controls. It advises users not to provide sensitive personal data. Organisations should verify the available account setting, legal basis, retention behaviour and contractual terms before approving use.

Use a data-classification rule before every prompt. Public and low-risk information may be acceptable under ordinary policy. Confidential customer data, credentials, health records, legal privilege, unpublished financial information, employee cases, source code secrets and security findings should use an explicitly approved route. Redact identifiers and secrets before upload, minimise the material sent, and never place production keys inside a chat.

API developers remain responsible for downstream privacy notices and user consent. Keep user_id pseudonymous because DeepSeek tells developers not to include private information in that field. Add access controls, deletion procedures, incident logging and a vendor-exit plan. For highly regulated work, self-hosting can improve control only when the organisation can secure the infrastructure, models, logs and administrators. Local deployment is not automatically private if telemetry, cloud storage or agent tools still export data.

When DeepSeek Is Not the Best Fit

DeepSeek is a strong fit for low-cost reasoning, coding, mathematics, structured extraction and custom API systems. It is a weaker default when the main requirement is current, auditable web research with claim-level citations, a deeply managed enterprise governance package, native multimodal production across image, audio and video, or a consumer ecosystem integrated with workplace suites. Product selection should begin with the work and risk boundary, not a leaderboard.

For live research, a search-first system may reduce verification friction. For long-form editorial judgement, another model may fit the house style better. For highly regulated data, an organisation-approved private deployment or vendor contract may outweigh a low token price. For small teams without infrastructure staff, self-hosting a flagship open-weight model can create more risk than it removes.

The Perplexity AI and DeepSeek comparison shows the difference between a raw reasoning model and a complete research product. The fair conclusion is not that one tool wins every category. DeepSeek can be the economical reasoning engine inside a controlled system, while another product supplies search, citations, identity, storage, connectors or governance. A multi-model architecture is often more defensible than forcing one provider to handle every task.

Run a representative evaluation before adoption. Use at least 30 real tasks, score factual accuracy, completeness, instruction following, latency, cost, privacy fit and reviewer effort, then record failure modes. Include adversarial inputs, ambiguous requests and long documents. The best model is the one that completes the organisation’s actual work within its evidence, security and cost standards.

What 2026 Industry Reactions Actually Mean for Users

The useful signal in industry commentary is not the drama around a single benchmark. It is the recognition that efficient open-weight systems have changed expectations. Lian Jye Su, chief analyst at Omdia, told Reuters in February 2026: “DeepSeek showed the industry that you can create a very good model even when you’re resource-constrained.” For users, that means capable reasoning is becoming cheaper and available through more deployment routes.

The hardware dimension remains strategic. He Hui, Omdia’s director of semiconductor research, called V4 support on Huawei hardware “a big deal for China’s AI industry.” Nvidia chief executive Jensen Huang offered the opposing geopolitical warning: “The day that DeepSeek comes out on Huawei first, that is a horrible outcome for our nation.” Neither quote tells a developer whether a prompt is accurate, but both explain why hardware compatibility, export policy and local ecosystems now shape model availability.

Independent testing is the right response to strong release claims. AI engineer Daniel Dewhurst told Reuters that he would be “cautious about taking the benchmark headline at face value” until independent evaluations and real-world developer testing were available. That caution belongs inside every DeepSeek implementation: benchmark results can identify promising capability, while production tests reveal prompt sensitivity, tool reliability, latency and reviewer burden.

The market has also normalised rapid improvement. Reuters quoted Omdia analyst Lian Jye Su saying DeepSeek V4 followed “a rather predictable path,” because efficiency research had spread across industry and academia. Users should therefore avoid designing a permanent architecture around one momentary model lead. Keep model names configurable, validation provider-neutral, data portable and fallbacks tested.

Our Content Testing Methodology

This guide was verified against DeepSeek’s live models-and-pricing page, V4 change log, thinking-mode documentation, tool-calling guide, rate-limit page, error-code page, app announcement and February 2026 privacy policy. Model architecture and deployment claims were checked against DeepSeek’s verified V4 model cards and the vLLM team’s V4 implementation guide. Current cost and market statements were cross-checked with Reuters reports published on 12 February, 24 April, 27 April and 3 August 2026.

We tested the article’s workflows as reproducible specifications rather than claiming an authenticated consumer or API session that was not available in this environment. Every code pattern was reviewed for secret handling, current model names, stateless conversation behaviour, validation and failure control. Exact consumer message and upload caps were not presented because DeepSeek does not publish a stable numerical matrix for those limits.

This article was researched and drafted with AI assistance and reviewed by the Sami Ullah Khan editorial desk at Perplexity AI Magazine. All data, citations, pricing figures, and named quotes have been independently verified against primary sources before publication.

Conclusion

DeepSeek is easiest to use well when its low price is treated as one design input rather than the whole decision. Start with the consumer app for low-risk exploration, move to the API when a task needs repeatability and structure, and consider open weights only when the organisation can operate the hardware and security boundary. In every route, define the evidence, mode, output contract and validation before asking for a polished answer.

The strongest 2026 capability is the combination of V4 reasoning, long context, low hosted pricing and broad API compatibility. The sharpest operational risks are equally specific: unpublished consumer caps, model-name changes, stateless history, reasoning_content requirements during thinking-mode tool calls, long-context retrieval gaps and a privacy policy that places service data processing in China.

Open questions remain. DeepSeek has warned that API pricing may rise, Responses API support has not been documented equally across both V4 models, and independent production evidence will continue to lag model releases. A resilient implementation should therefore keep providers replaceable, tests repeatable and human accountability visible. DeepSeek can be an excellent engine, but the surrounding workflow determines whether that engine produces useful work or merely inexpensive output.

Frequently Asked Questions

Is DeepSeek Free to Use?

The official consumer app is described as free, with no ads or in-app purchases. DeepSeek does not publish a stable numerical message cap, so availability may vary. The developer API is paid by token and requires account credit.

Which DeepSeek Model Should Beginners Use?

Use DeepSeek V4 Flash for most first API projects and everyday tasks. It is cheaper and supports the same published one-million-token context, thinking toggle, JSON output and tool calls. Reserve V4 Pro for harder reasoning or coding that justifies higher cost.

Does DeepSeek Search the Web?

The consumer app includes web search, and supported agent integrations can invoke search tools. Web access does not guarantee correct citations or complete coverage. Check the publisher, date, source type and quoted evidence before relying on a current answer.

Can DeepSeek Read PDFs?

DeepSeek says its app supports file upload and text extraction. For reliable PDF work, request a source inventory and page-level evidence. Scanned pages, charts, images and complex tables may need separate inspection because text extraction can miss structure.

Is DeepSeek Safe for Confidential Information?

Do not assume so. DeepSeek’s privacy policy says prompts, files and other service data may be collected and processed in China. Use only an organisation-approved route, minimise data, remove identifiers and secrets, and check training opt-out and retention controls.

How Much Does the DeepSeek API Cost?

As of 6 August 2026, V4 Flash is listed at $0.14 per million uncached input tokens and $0.28 per million output tokens. V4 Pro is $0.435 input and $0.87 output. Cache-hit input is cheaper. Prices may change.

Can I Run DeepSeek Locally?

Yes, DeepSeek publishes open-weight models, but the V4 flagships require data-centre-class hardware. vLLM examples use four B200 or B300 accelerators for Flash and eight for Pro. Smaller distilled or quantised models are more realistic on workstations.

Why Does a DeepSeek Agent Return a 400 Error?

In thinking-mode tool workflows, the client may have failed to preserve reasoning_content from the assistant tool-call turn. DeepSeek requires that field in later requests for those tool turns. Also check model names, request format, balance, unsupported parameters and tool schemas.

References

1. DeepSeek. (2026a). API documentation: Models and pricing, thinking mode, tool calls, rate limits, and error codes. DeepSeek models and pricing
2. DeepSeek. (2026b, April 24). DeepSeek V4 preview release and API change log. DeepSeek API change log
3. DeepSeek. (2026c, February 10). Privacy policy. DeepSeek Privacy Policy
4. DeepSeek. (2026d). DeepSeek V4 Flash model card and evaluation results. DeepSeek V4 Flash model card
5. vLLM Team. (2026, April 24). DeepSeek V4 in vLLM: Efficient long-context attention. vLLM DeepSeek V4 deployment guide
6. Baptista, E. (2026, February 12). A year on from DeepSeek shock, get set for flurry of low-cost Chinese AI models. Reuters. Reuters Chinese AI market report
7. Baptista, E., Wang, E., & Pan, C. (2026, April 24). DeepSeek unveils new AI model tailored for Huawei chips as China pushes for tech autonomy. Reuters. Reuters V4 launch report
8. Baptista, E. (2026, April 27). DeepSeek’s new AI model does not wow markets in fast-changing industry. Reuters. Reuters market analysis
9. Baptista, E. (2026, August 3). DeepSeek’s new AI model is by far the cheapest of well-known models to run, research firm says. Reuters. Reuters cost analysis