1. What Is DeepSeek?

DeepSeek is a Chinese AI company that builds large language models (LLMs) and releases them as open-weight systems—meaning the model parameters are publicly available for others to use and host, though they’re not “fully open source” in the traditional software sense.

Key things that make DeepSeek stand out:

• Open-weight models (you can download and self-host many variants).

• Strong reasoning & coding performance, often rivaling leading closed models.

• Highly efficient training — the company claims its flagship V3 model was trained for around US$6 million, far below the often-quoted ~US$100 million for GPT-4, and with far fewer GPUs than Meta’s Llama 3.1.

• Very aggressive pricing on the hosted API, triggering price cuts from other Chinese tech giants and contributing to fears of an AI “price war.”

DeepSeek also offers a consumer chatbot app (based on DeepSeek-R1 and later models) which quickly became one of the most-downloaded free apps in the US iOS App Store in January 2025.

2. Chronology of DeepSeek Models

DeepSeek has iterated extremely fast. The main milestones:

• 2023

  • DeepSeek Coder – a family of code-specialized LLMs (Base + Instruct) for programming tasks, launched November 2023.

• 2024

  • DeepSeek-LLM, MoE, Math – early general and math models.

  • DeepSeek-V2 / V2.5 – improved architecture (including Mixture-of-Experts and Multi-Head Latent Attention) focused on efficiency.

• Late 2024 – Early 2025

  • DeepSeek-V3 (Dec 2024 / Mar 2025 public release): a large open-weight model trained on ~14.8T tokens, extended to 128K context. It delivers state-of-the-art performance among open models on knowledge, reasoning, math, and coding benchmarks.

  • DeepSeek-R1 (Jan–May 2025): a reasoning-focused model launched under the MIT license, with performance comparable to top closed models on many reasoning benchmarks and notably low training/inference cost.

• Mid–Late 2025

  • DeepSeek-V3.1 (Aug 2025): introduces hybrid “Think / Non-Think” modes—a step toward agent-style reasoning. DeepSeek claims >40% improvements over V3 and R1 on benchmarks like SWE-bench and Terminal-bench.

  • DeepSeek-V3.1-Terminus and V3.2-Exp (Sep 2025): refinements focusing on text quality, code/eval performance, and even more efficient inference, with V3.2-Exp also marketed as a cheaper, faster variant on the official chat & API.

3. DeepSeek’s Flagship Families Explained

3.1 DeepSeek Coder

DeepSeek Coder is a set of code-centric models (1.3B–33B parameters) trained on large volumes of source code, dev discussions, and documentation.

Typical use cases:

• Code completion & bug fixing

• Writing unit tests and refactoring

• Explaining complex codebases

• Generating scripts, infrastructure as code, and boilerplate

Because many Coder variants are open-weight, they’re popular in self-hosted dev tools, VS Code extensions, and on-premise AI assistants where companies don’t want code leaving their infrastructure.

3.2 DeepSeek-V3 and V3.x (V3.1, V3.2-Exp)

DeepSeek-V3 is a large general-purpose LLM optimized for both reasoning and efficiency. It was trained on ~14.8T tokens, with a strong emphasis on math and coding data and later extended to 128K context length.

Independent analyses report that DeepSeek-V3:

• Reaches 88.5 on MMLU and 75.9 on MMLU-Pro.

• Scores 59.1 on GPQA.

• Outperforms open models like Llama and Qwen on many benchmarks, while approaching GPT-4o and Claude 3.5 Sonnet performance.

V3.1 and V3.2-Exp build on that:

• Hybrid inference:

  • Think mode: generates internal reasoning traces for harder tasks (similar in spirit to OpenAI’s “reasoning models”).

  • Non-Think mode: shorter, faster answers for simple queries.

• Stronger agent skills: improved tool use, multi-step workflows, and agent-style planning.

• Cheaper & faster inference: V3.2-Exp is explicitly marketed as a more efficient, lower-priced API option.

In practice, V3.x models are ideal for:

• Chatbots and copilots

• Long-context document Q&A (contracts, docs, PDFs)

• Data analysis, research assistance, and content generation

• Complex coding & debugging tasks

3.3 DeepSeek-R1 and R1-Distill

DeepSeek-R1 is the family that pushed DeepSeek into the global spotlight as a serious reasoning competitor to OpenAI’s o1 line.

Key aspects:

• Focused on logical, mathematical, and multi-step reasoning tasks.

• Uses reinforcement learning techniques (like GRPO) and rule-based rewards to train the model to reason step-by-step.

• R1-Distill versions take the reasoning behaviors of the large R1 model and distill them into smaller open-weight models (1.5B–70B parameters) that are easier to run on standard GPUs.

This makes R1-based models attractive for:

• Math tutoring / Olympiad-style problems

• Algorithmic reasoning (e.g., constraints, puzzles, planning)

• Advanced coding and formal reasoning workflows

• Research and scientific problem-solving

4. Why DeepSeek Matters: Efficiency, Cost, and Open Weights

DeepSeek’s impact isn’t just about model quality—it’s about economics and access.

4.1 Training & Efficiency

DeepSeek claims that V3 was trained for about US$6 million on H800 GPUs via aggressive optimization, mixed-precision arithmetic (including 8-bit FP formats), and customized attention mechanisms like Multi-Head Latent Attention (MLA) and sparse MoE layers.

Although later analyses suggest the true all-in cost is much higher (when including hardware amortization, infra, and experiments), the core point remains: DeepSeek showed that near-frontier performance can be achieved with far fewer resources than widely assumed.

4.2 Price Pressure on the AI Market

After DeepSeek-R1 and its cheap API launched, other Chinese giants like ByteDance, Tencent, Baidu and Alibaba quickly cut prices for their own models, while commentators started talking about an AI “price war” and drawing analogies to a new “Sputnik moment” for AI competitiveness.

For developers and startups, this means:

• Lower inference costs

• More choice among strong open-weight models

• Options to self-host or rely on lower-cost APIs instead of only closed US-based providers

5. How to Access and Use DeepSeek

You can interact with DeepSeek in three main ways:

1. Official Chat & Apps

   • Web chatbot and mobile apps that expose the latest V3.x / R1-based models with “Think” and “Non-Think” modes.

2. DeepSeek API Platform

   • Hosted inference for V3.x and R1, with competitive pricing and support for standard LLM workflows (chat, tools, function calling etc.).

3. Open-Weight Downloads

   • Models like DeepSeek-V3 and DeepSeek-R1-Distill can be downloaded from GitHub and Hugging Face for self-hosting on your own infrastructure.

Typical integration scenarios:

• Embedding DeepSeek in your SaaS product as a chat or coding assistant.

• Running a private DeepSeek instance inside your VPC for sensitive data.

• Building research pipelines that use R1-Distill models for reasoning-heavy tasks.

6. Strengths and Limitations

6.1 Strengths

• Top-tier performance among open-weight models, especially in reasoning, math, and coding.

• Open weights + permissive licensing (MIT for recent flagships like V3-0324 and R1-0528), enabling broad research and commercial use with fewer constraints than many other models.

• Highly competitive cost for both training and inference.

• Rapid iteration cadence (V2 → V2.5 → V3 → R1 → V3.1 → V3.2-Exp in under two years).

6.2 Limitations & Concerns

• Content alignment & censorship: analyses note that DeepSeek-R1 and successors often align strongly with official Chinese government positions and may censor or steer answers on certain political or sensitive topics.

• Regulatory risk: reports indicate investigations and potential export-control penalties around access to advanced Nvidia chips and US technology for DeepSeek, which could affect its long-term supply chain and global partnerships.

• Ecosystem tooling: while growing quickly, DeepSeek’s ecosystem (plugins, managed services, enterprise tooling) is still younger and less mature than the ecosystems around OpenAI or Google in some markets.

For many use cases—especially coding, reasoning, and self-hosted deployments—these limitations are manageable, but they should be considered in regulated industries or highly sensitive domains.

7. When Should You Choose DeepSeek?

DeepSeek is particularly compelling if:

• You want near-frontier performance but prefer open-weight models.

• You care about cost-efficient reasoning, especially for math, coding, and complex planning.

• You need to self-host for compliance/security while still getting high-quality outputs.

• You’re building tooling for developers or power-users and want strong code + reasoning combo.

You might lean toward other providers if:

• You require very strict Western regulatory alignment and governance tooling.

• Your organization is locked into an ecosystem (e.g., Azure OpenAI, Google Cloud, AWS Bedrock) and values tighter platform integration more than marginal model quality or price.

8. Final Thoughts

DeepSeek has fundamentally changed expectations about what a relatively small, non-US company can do in frontier AI:

• It proved that open-weight, near-GPT-4-level models can be trained and released quickly.

• It demonstrated that efficient training and low-cost inference can force the entire industry to rethink pricing.

• It accelerated the shift toward reasoning-centric, “thinking” models with its R1 family and the hybrid Think/Non-Think modes in V3.1+.

If you’re a developer, startup founder, or enterprise architect evaluating AI stacks, DeepSeek deserves a serious look—both as a primary model family and as a key part of a multi-model strategy alongside OpenAI, Anthropic, Llama, Qwen, and others.

DeepSeek Downloads & Usage FAQ

1. What is DeepSeek, exactly, and who makes it?

DeepSeek is a Chinese AI lab that trains large language models like DeepSeek-V3 (general LLM) and DeepSeek-R1 (reasoning-focused). Many of these are released as open-weight models you can download (e.g. on GitHub / Hugging Face) and as hosted chat/API services.

2. Is DeepSeek free to use?

Redditors usually distinguish between:

• Free access via the official chat or routing platforms (like OpenRouter / JanitorAI), often with daily message or token limits.

• Paid tiers / Nitro / API where you pay per-token or via subscription for higher limits and faster queues.

When you self-host the open weights, the software is free, but you still pay for your own compute.

3. Does DeepSeek log my data? Is it safe/ private?

One popular Reddit FAQ notes that DeepSeek-based services do log your prompts and responses, typically anonymised, and warns people to assume nothing online is fully private.

At the same time, news reports and Reddit discussions raise extra concerns because:

• User data from the official app is stored on servers in China.

• Chinese law can require companies to share data with state agencies.

So Reddit’s consensus is: don’t paste highly sensitive or regulated data into the hosted app; use self-hosting or a trusted proxy if privacy is critical.

4. Is there a filter or censorship on DeepSeek? Can it do NSFW?

Two different issues show up in threads:

1. NSFW / role-play filters
   On JanitorAI / proxy setups, users say the DeepSeek models themselves aren’t heavily NSFW-filtered, but the platform may still enforce its own rules.

2. Political censorship
   Separate reports show strong filtering on Chinese-sensitive topics (Tiananmen, Taiwan, criticism of Chinese leadership). The model often starts to answer and then replaces it with a safety message, especially in English.

So: it’s relatively loose for adult RP in some frontends, but strict for sensitive politics on the official service.

5. How many messages per day do I get? What’s the context size?

A widely-linked Reddit FAQ for JanitorAI + DeepSeek mentions:

• Roughly ~80 messages/day (or around ~300k tokens) for the free tier, including re-rolls.

• Context for the free DeepSeek model is around 128k tokens, but it can depend on the provider / server.

People also note that hitting the cap on one DeepSeek model can affect all DeepSeek variants on that platform until the daily reset (UTC 00:00).

6. Which DeepSeek model should I use: V3, V3.1, V3.2-Exp, R1, or Coder?

Common pattern on r/DeepSeek and r/LocalLLaMA:

• DeepSeek-V3 / V3.1 / V3.2-Exp – general chat, knowledge, light coding, agents and tools (V3.1 adds Think/Non-Think “agent” mode; V3.2-Exp is cheaper/faster).

• DeepSeek-R1 / R1-Distill – heavy reasoning, math, puzzles, algorithmic tasks.

• DeepSeek Coder – IDE copilots, code generation and refactoring.

Redditors often run V3.x for most tasks, then call R1(-Distill) when they really need a “reasoning boost.”

7. Is DeepSeek better than GPT-4 / Claude Sonnet 3.5 / other models?

You’ll see lots of “personal experience” posts, especially from devs:

• Some users say R1 and V3.x beat Claude 3.5 Sonnet and even GPT-4-class models on coding and math in their own tests, particularly for structured tasks (scripts, data analysis, etc.).

• Others point out censorship, English-language quirks, and occasional hallucinations, so they still keep OpenAI/Anthropic around as backups.

The overall Reddit vibe: DeepSeek is shockingly good for the price and openness, but not a perfect drop-in replacement in every scenario.

8. Why is DeepSeek so cheap? Is it really that fast?

A frequent skepticism thread: “Are DeepSeek’s new models really that fast and cheap?”

• DeepSeek’s papers and repos describe Mixture-of-Experts, Multi-Head Latent Attention, and 8-bit formats to cut compute costs.

• Independent blog posts test latency/cost and conclude that the cost claims (e.g., an order of magnitude cheaper) are directionally true, though headline training-cost numbers might undercount infrastructure & experimentation.

Redditors generally agree it’s one of the best price-performance options, especially via third-party APIs or local quantized versions.

9. How do I run DeepSeek locally (LM Studio, Ollama, vLLM, etc.)?

On r/LocalLLaMA and other communities, the typical steps people share are:

1. Download a checkpoint – e.g., DeepSeek-R1-Distill or DeepSeek-V3.x from Hugging Face / official repos.

2. Load it in a local runner like LM Studio, Ollama, text-generation-webui, vLLM, or llama.cpp (quantized GGUF versions are popular).

3. Adjust context size, temperature, and max tokens to avoid truncation and keep cost/VRAM reasonable.

Reddit threads often trade tips about VRAM requirements, quantization levels, and configs for consumer GPUs and Apple Silicon.

10. What’s the “thinking mode,” and how do I stop it wasting tokens?

Many posts complain that DeepSeek sometimes prints a big “thinking” or chain-of-thought block before the answer (especially R1-style models), which can:

• Eat tokens

• Make role-play chat awkward

Typical workarounds people share:

• Use V3 (non-Think) for RP or simple chat.

• Add system/user instructions like "[skip thinking process]" to suppress visible reasoning.

• In some frontends, disable “show reasoning” if that toggle exists.

11. Why am I getting “server busy”, proxy errors, or cut-off messages?

Reddit is full of error screenshots:

• The official chatbot has had “server is busy” outages during viral spikes and alleged DDoS attacks.

• Proxies / JanitorAI setups report “PROXY ERROR: Unknown response [object Object]”, timeouts, or truncated replies when limits are hit or upstream APIs hiccup.

Most advice is pretty practical: retry later, switch providers, reduce context size, or fall back to a local model.

12. Are there political or legal risks using DeepSeek at work or in government?

This comes up more often now that some governments have acted:

• The Czech cyber-security agency and several US states warn that DeepSeek products may be obliged to share user data with Chinese authorities and have restricted official use.

For corporate/government environments, Reddit’s general advice is:

• Prefer self-hosted open-weight deployments (no data back to DeepSeek).

• Run your own risk assessment and follow your org’s security guidance before using the cloud chatbot/API.