DeepSeek Models: A Practical Guide to V3, R1, Coder & More

If you’re hearing a lot about DeepSeek lately, it’s because they’ve gone from “interesting open model” to full model ecosystem: general LLMs, reasoning models, coding models, OCR, and multimodal.

This article gives you a structured, developer-friendly overview of the main DeepSeek model families, what they’re good at, and how to choose the right one for your stack.

1. DeepSeek model families at a glance

DeepSeek’s lineup is bigger than just “V3 vs R1”. On Hugging Face and their API docs you’ll find:

• DeepSeek-V3 / V3.1 / V3.2-Exp – general-purpose LLMs (chat, tools, long context)

• DeepSeek-R1 & R1-Distill – reasoning-first models (math, logic, complex code)

• DeepSeek-Coder V2 – coding-focused models (DevTools, IDE copilots)

• DeepSeek-OCR – 3B OCR / image-to-text model

• Janus-Pro / deepseek-vl – multimodal (image + text)

On the official platform these mostly surface as two API “personalities”:

• deepseek-chat – fast, direct responses (non-thinking mode)

• deepseek-reasoner – chain-of-thought reasoning (thinking mode)

2. DeepSeek-V3, V3.1 and V3.2-Exp – the general LLMs

2.1 Architecture & specs

DeepSeek-V3 is the flagship general model: a 671B-parameter Mixture-of-Experts LLM with about 37B parameters active per token, using Multi-Head Latent Attention (MLA) and a custom DeepSeek-MoE architecture to stay efficient.

Key points:

• Parameters: 671B total, ~37B active

• Context length: up to 128K tokens

• Training data: ~14.8T tokens across many domains

• Open weights: base and instruction models on Hugging Face

V3.1 builds on V3 with a dual-mode design: it can behave like V3 for fast, direct answers, or switch into an R1-style “thinking” mode for harder problems. That’s why some guides call V3.1 the most versatile DeepSeek model for real apps.

V3.2-Exp (2025) is an experimental evolution of V3.1-Terminus that introduces DeepSeek Sparse Attention (DSA) to make long-context training and inference cheaper and faster, with significantly lower API pricing on the hosted service.

2.2 Best use cases for V3 / V3.1 / V3.2-Exp

Use these as your default workhorse models when you need:

• Chatbots and support agents

• RAG over long documents (contracts, wikis, PDFs)

• Tool-calling / function-calling agents

• General content generation and light coding

Rough rule of thumb:

• V3 / V3.2-Exp non-thinking → fast, cheap, production chat & tools (deepseek-chat)

• V3.1 / V3.2-Exp thinking → agent flows that occasionally need deeper reasoning (deepseek-reasoner)

3. DeepSeek-R1 & R1-Distill – the reasoning family

3.1 What R1 is

DeepSeek-R1 is a reasoning-first LLM trained heavily with reinforcement learning to maximize correctness on math, coding, and logic tasks. The team released:

• DeepSeek-R1-Zero – RL-only “cold start” model

• DeepSeek-R1 – full reasoning model

• Six distilled dense models based on Llama and Qwen backbones (1.5B → 70B)

R1 achieves performance comparable to OpenAI’s o1 across many math and reasoning benchmarks, while being dramatically cheaper to run according to both DeepSeek and independent commentators.

3.2 R1-Distill sizes

On Hugging Face you’ll see distills like:

• DeepSeek-R1-Distill-Qwen-1.5B / 7B / 14B / 32B

• DeepSeek-R1-Distill-Llama-8B / 70B

These give you R1-style reasoning in much smaller, self-hostable models—popular in tools like Ollama and LM Studio.

3.3 Best use cases for R1

Reach for R1 / R1-Distill when you care more about getting the reasoning right than answering fast:

• Olympiad-style math, proofs, STEM tutoring

• Complex coding problems and debugging

• Multi-step planning and algorithmic tasks

• Research assistants that must compare evidence and explain why

On the DeepSeek API, this style of model shows up as deepseek-reasoner, which first generates chain-of-thought (CoT) internally before giving an answer. You can also access that CoT if you want to log or distill it.

4. DeepSeek-Coder – models for developers

DeepSeek also maintains dedicated coding models, notably DeepSeek-Coder V2 Lite (Base / Instruct) and earlier deepseek-coder-1.3B / 6.7B / 33B variants.

Typical capabilities:

• Code completion in multiple languages

• Generating and refactoring functions / modules

• Writing tests and explaining code

• Infrastructure as code (IaC), scripts, config generation

When to use them:

• If you’re building an IDE copilot or code review bot, DeepSeek-Coder is often a better primary model than V3, with R1-Distill optionally backing it for harder reasoning steps.

• For pure app logic and business chat, V3.x is usually enough.

5. Vision, OCR & multimodal models

DeepSeek’s ecosystem isn’t just text:

• DeepSeek-OCR (3B) – image-to-text / OCR model often wrapped into higher-level tools; very popular for scanning PDFs and screenshots.

• Janus-Pro-7B – “any-to-any” multimodal model (text–image);

• deepseek-vl-7b-chat – earlier vision-language chat model.

Use cases:

• Parsing invoices, forms, or scanned documents (DeepSeek-OCR)

• Image captioning and simple VQA (Janus / VL)

• Multimodal assistants that mix screenshot reasoning with text tools

6. How to choose the right DeepSeek model

Here’s a simple scenario-based mapping:

If you want, I can turn this into a JSON or table block for your Deepseek.EN site (e.g. “Model Picker” component).

7. Access, pricing and deployment options

7.1 Official DeepSeek platform

From the official site and API docs:

• Web & app chat – free tier access to V3.2-Exp; “thinking” and “non-thinking” modes.

• API – deepseek-chat and deepseek-reasoner endpoints (V3.2-Exp under the hood), 128K context with up to 4K–8K output (chat) or 32K–64K (reasoner).

• Recent updates cut V3.2-Exp API prices by 50%+ to stay highly competitive.

7.2 Open weights & third-party hosting

Most major models (V3-0324, V3.1, V3.2-Exp, R1, R1-Distill, Coder, OCR) are published on Hugging Face and GitHub under permissive licenses, often MIT.

That means you can:

• Run them locally (Ollama, LM Studio, text-generation-webui, vLLM)

• Host them in your own VPC or on GPU clouds

• Integrate them into existing inference platforms (TGI, vLLM, BentoML, etc.)

8. Safety, openness and governance

Two realities to keep in mind:

• Safety & jailbreaking: Security researchers have shown that DeepSeek’s hosted R1 models can be easier to jailbreak than some competitors, successfully bypassing guardrails in a wide range of tests. You should layer your own safety filters, monitoring, and policies on top.

• “Open source” vs open weights: DeepSeek’s models are widely praised for openness, but some analyses note that they don’t fully meet strict open-source transparency criteria (e.g., limited detail on RL stages and data provenance). This is a broader issue across many “open” LLMs, not just DeepSeek.

For sensitive or regulated environments, that usually means:

• Prefer self-hosting or trusted providers over the consumer app.

• Combine DeepSeek models with auditing, logging and evaluation tailored to your domain.

9. Takeaways

If you remember only a few things about DeepSeek models:

• V3 / V3.1 / V3.2-Exp are your general LLMs for chat, tools and long-context RAG.

• R1 & R1-Distill are your reasoning engines for math, logic and complex code.

• Coder, OCR and multimodal round out the stack for dev tools and vision tasks.

• Open weights + aggressive pricing make DeepSeek a core pillar in many modern multi-model AI stacks.

DeepSeek Models FAQs: Everything You Need to Know

1. What DeepSeek models are there, and what are they for?

Reddit threads and guides usually group DeepSeek’s lineup like this:

• DeepSeek-V3 / V3.1 / V3.2-Exp – general LLMs for chat, tools, RAG, coding.

• DeepSeek-R1 & R1-Distill – reasoning-first models for math, logic, complex code.

• DeepSeek-Coder (V2 & older) – code-centric models for IDE copilots & dev tools.

• DeepSeek-OCR (3B) – image-to-text / OCR.

• Multimodal (e.g. Janus / deepseek-vl) – text + image.

Most Reddit “model lists” link to the official Hugging Face repos for each family.

2. How do I choose between DeepSeek-V3.x and DeepSeek-R1?

This “V3 vs R1” question shows up constantly on r/LocalLLaMA and similar subs.

• Use V3 / V3.1 / V3.2-Exp if you want:

  • General chat, support bots, RAG, tools

  • Better stylistic writing and everyday coding

  • Lower latency and cost for most tasks

• Use R1 / R1-Distill if you care about:

  • Hard math, logic, proofs, algorithmic reasoning

  • Deep code reasoning and debugging

  • Multi-step planning and “explain your reasoning” behavior

Reddit consensus: V3.x is the “default chat model”, R1 is the “bring in the heavy reasoning artillery.”

3. Which DeepSeek model should I install locally (Ollama / LM Studio) with limited VRAM?

Beginner posts with 8–24 GB VRAM get answers like:

• 8–12 GB VRAM

  • Try R1-Distill-Qwen-1.5B / 7B, older DeepSeek-Coder 1.3B / 6.7B, or quantized V2/V2.5.

• 16 GB VRAM

  • You can run R1-Distill-Qwen-14B or V3-based quantized models with Q4/Q5 GGUF.

• 24 GB+ VRAM

  • 14B comfortably, and some 32B quantized builds if you’re careful with context size.

Most replies say: start with the small R1-Distill or Coder models, confirm everything works, then move up in size.

4. How do I install and run DeepSeek models locally?

Threads on r/LocalLLM, r/LocalLLaMA, r/linux, r/homelab share roughly the same recipe:

1. Download a model from Hugging Face (e.g. deepseek-ai/DeepSeek-V3, DeepSeek-R1-Distill-Qwen-7B).

2. Pick a runner: Ollama, LM Studio, text-generation-webui, vLLM, koboldcpp, etc.

3. Load the GGUF or safetensors, set context (e.g. 8K–32K) and sampling parameters.

4. Optional: plug it into LangChain / LlamaIndex for tools and RAG.

There are also step-by-step “How to run DeepSeek-R1 locally” tutorials linked from r/LocalLLM and r/LLMDevs.

5. Why do I get “ERROR 400 – (name) is not a valid model ID” when I try DeepSeek?

In the “A Deepseek FAQ” and related JanitorAI comments, people repeatedly hit a 400 error because they pass the wrong model ID string.

Reddit fixes:

• Use the exact model IDs your provider expects (e.g. deepseek/deepseek-r1:free, deepseek-ai/DeepSeek-V3, or the vendor’s alias like deepseek-chat).

• Double-check colons and slashes – typos like deepseekv3 or deepseek-v3:free when the provider expects another name will fail.

• Some platforms (Chutes, JanitorAI, etc.) publish an official “supported models” list – use those names, not the raw Hugging Face repo name, unless docs say so.

6. Do DeepSeek models “phone home” when I run them locally?

This is a common concern in r/LocalLLaMA discussions: “Do DeepSeek models harvest and send data back to their servers?”

Reddit replies usually separate:

• Pure local / open weights (Hugging Face → ollama / LM Studio / vLLM)

  • These are just model files. Unless your runner has telemetry enabled, the weights themselves don’t “call home.”

• Hosted APIs or remote frontends (DeepSeek website, proxies, third-party SaaS)

  • Your prompts go through their servers; you must trust their privacy policy and jurisdiction.

Hence, if privacy is critical, people recommend self-hosting in an isolated environment and disabling any optional analytics in your UI/tool.

7. Why are the free DeepSeek models suddenly slow or “unusable” on some services?

A widely shared post on r/SillyTavernAI complains that the free DeepSeek endpoints via certain proxies (like Chutes) became “completely unusable”: timeouts, errors, partial replies.

Reddit explanations include:

• Rate limits & popularity spikes – lots of users hitting free tiers at once.

• Upstream changes by DeepSeek or the proxy (model moves, quota reductions).

• Aggressive safety / filtering that cuts off responses.

The usual advice: either pay for a stable API, switch proxy provider, or run a local R1-Distill / V3 instance instead of relying on a shared free endpoint.

8. Why is DeepSeek-V3.2-Exp so cheap? Did they cut quality?

On r/LocalLLaMA and r/singularity, people ask why V3.2-Exp is so inexpensive and whether it’s “too good to be true.”

The answers point to:

• DeepSeek Sparse Attention (DSA) and other efficiency tricks that make attention almost linear in sequence length.

• A new “sparse” architecture that reduces compute per token and enables 50%+ API cost cuts while keeping performance close to V3.1.

• Heavy optimization for Chinese-native hardware (Huawei Ascend, Cambricon, etc.), which may reduce infra cost.

Some users do report slightly worse instruction-following vs older V3.x models (see next FAQ), but not a catastrophic quality drop.

9. Why is DeepSeek-V3.2-Exp bad at following instructions, and what can I do?

There’s a specific thread titled “Why is deepseek V3.2-Exp so bad at following instructions?” where users show prompt examples the model mishandles.

Common community tips:

• Be explicit and structured – use clear roles, bullet lists, and “Do / Don’t” sections.

• Lower temperature and increase top-p slightly, or vice versa, to stabilize outputs.

• For strict formats (JSON, code), wrap with:

  • “Reply in valid JSON only, no prose.”

  • Add examples and format: sections.

If instruction-following is critical, some redditors suggest sticking with V3.1 or pairing V3.2-Exp with a lightweight checker/fixer model.

10. What hardware do I need for DeepSeek-V3 and DeepSeek-R1 locally?

“Can I run DeepSeek on 16 GB RAM / my Mac mini / small GPU?” appears in multiple LocalLLM and LocalLLaMA threads.

Typical Reddit guidance:

• Laptop / small GPU (8–12 GB VRAM)

  • Use R1-Distill 1.5B–7B or older DeepSeek-Coder; keep context smaller (4K–8K).

• Gaming PC / 16–24 GB VRAM

  • R1-Distill-14B or similar sized V3 quant, with 8K–16K context.

• Workstation / multi-GPU

  • 32B or 70B distills, or even experimenting with sharded full-size V3.1, though most people use cloud for that.