DeepSeek V4 Tutorial: The Honest Hands-On Walkthrough

This DeepSeek V4 tutorial is the real, unfiltered walkthrough — no hype, no sponsorship, just what happened when I actually used it.

DeepSeek V4 dropped the exact same day GPT 5.5 landed.

Same day.

That's not a coincidence — that's a shot across the bow.

I've been testing it for the last few hours, so let me save you the trial and error.

Video notes + links to the tools 👉

What DeepSeek V4 Actually Is

DeepSeek V4 is an open-source mixture-of-experts (MoE) model from the Chinese AI lab that shook the world back in January 2025.

It comes in two flavours, and you need to know the difference before you pick one.

V4 Pro vs V4 Flash

V4 Pro — 1.6 trillion total parameters, only 49 billion active per token
V4 Flash — 284 billion total parameters, 13 billion active per token
Both are MoE (mixture of experts)
Both are fully open source
Both support a 1 million token context window

That 1M context is not a typo.

You can drop an entire codebase in there.

Where You Can Run It

Three places, pick your poison:

chat.deepseek.com — the official web chat (free)
platform.deepseek.com — API for devs
LM Studio or Hugging Face — run it locally if your rig can handle it

Heads up — the old deepseek-chat and deepseek-reasoner endpoints retire after July 24.

Migrate now or your scripts break later.

How to Actually Use DeepSeek V4 (Step-by-Step)

This is the part everyone glosses over.

Let me walk you through it like you're sitting next to me.

Step 1: Open chat.deepseek.com

Head to chat.deepseek.com and log in.

You'll see two mode toggles at the top of the chat box.

Instant — fast replies, non-think mode
Expert — slower, more careful, with optional Deep Think reasoning

Step 2: Pick the Right Mode

Here's the rule of thumb I use:

Quick factual questions → Instant
Complex reasoning, coding, maths → Expert + Deep Think

Deep Think uses up to 384K tokens of thinking budget on hard problems.

That's bonkers.

For comparison, most reasoning models use 16K-32K thinking tokens.

Step 3: The Three Reasoning Modes (API)

On the API side at platform.deepseek.com, you get three modes:

Non-think — fast, no reasoning chain
Think high — step-by-step reasoning
Think max — up to 384K reasoning tokens

Pick based on how hard the task is and how much you want to pay.

Now — if you want the actual prompt templates I use to get the most out of Deep Think (including the prompts I use to get agent swarms building with Kimi K2.6), they're inside the Boardroom.

🔥 Want my exact DeepSeek V4 prompt library? Inside the AI Profit Boardroom, I've got a full DeepSeek V4 prompts and workflows section — including the Deep Think templates, the agent setup I use for cheap inference, and side-by-side comparisons with Claude and GPT. Plus weekly coaching calls where you can share your screen and get help with YOUR setup. → Get access to the full training here

My Live Test — The Pong Game

I tested DeepSeek V4 on two classic "can it code?" tasks.

First test: build a Pong game in one prompt using Deep Think mode.

What Happened

It worked.

Kind of.

The paddle was laggy.

Generation was slow — even for Deep Think, slower than I expected.

But the game ran.

For a one-shot generation with no follow-up prompting, that's actually solid.

Not Claude Opus 4.6 solid, but solid.

My Second Test — The Landing Page

I switched to Instant mode and asked for a landing page mockup.

The Verdict

Honestly?

Felt dated.

Like V3-level output.

Compared to what Claude Opus 4.6 produces in my AI SEO workflows, it was clearly behind on UI quality.

Compared to GPT 5.5 Pro, also behind.

The HTML was clean.

The design choices were... safe.

Safe as in boring.

Benchmarks — Where DeepSeek V4 Actually Wins

Here's where it gets interesting.

On certain benchmarks, DeepSeek V4 beats the big boys.

Simple QA Verified

DeepSeek V4: 57.9
Claude Opus 4.6 Max: 46.2
GPT 5.4 high: 45.3

That's a meaningful lead on factual accuracy.

Coding Benchmarks

Codeforces: 93.5% (ranked 23rd vs human competitive coders)
Apex shortlist: 90.2%

The codeforces number is wild — 23rd against humans is top-tier.

MMLU Pro

V4 Pro: 87.5
V4 Flash: 86.2
Kimi K2.6: close behind

So on pure benchmarks, this thing competes.

On vibes and UI polish?

Not quite.

The Architecture Innovations That Matter

This is the part most tutorials skip.

But if you're going to pick this model, you should understand why it's different.

Compressed Sparse Attention

DeepSeek V4 compresses 4 tokens into 1 for attention.

That means less memory, faster inference.

Heavily Compressed Attention

On top of that — 128 tokens compressed to 1 on some layers.

This is how they get 1M context without the memory exploding.

Manifold Constrained Hyperconnections

Fancy name, simple idea.

4x wider layer connections.

More information flows between layers.

Muon Optimizer (Not AdamW)

They ditched AdamW for the Muon optimizer.

Faster convergence, more stable training.

Training Data

32 trillion tokens trained on
Progressive context length: 4K → 16K → 64K → 1M

That progressive scaling is a trick worth stealing for anyone training their own models.

Efficiency — Why You Should Actually Care

Here's the kicker.

V4 Pro uses 27% of the compute of V3.2 and 10% of the KV cache memory
V4 Flash uses 10% of compute and 7% of memory

That's an absurd efficiency jump.

Translation: inference is way cheaper.

Should You Actually Use DeepSeek V4?

Honest answer based on my testing:

Use It For

Agent workflows where cost matters
Factual lookups (Simple QA beats Claude and GPT)
Long context tasks (1M window, cheap)
Competitive coding problems
Running locally via LM Studio

Skip It For

Polished UI / landing page generation — Claude Opus 4.6 still wins
Creative writing — GPT 5.5 is ahead
Agentic coding where output quality matters more than price

How to Run DeepSeek V4 Locally

Want it running on your own rig?

LM Studio

Open LM Studio
Search "DeepSeek V4 Flash" (Pro is too big for most setups)
Download the quantised version that fits your VRAM
Load it, chat away

Hugging Face

Pull the weights directly:

Model repo: deepseek-ai/DeepSeek-V4-Flash
Requires enough VRAM for the active params (~26GB for a decent quant)

Pairs well with Ollama + Hermes if you want multiple local models running side by side.

Pricing — The Real Killer Feature

Cheap.

Like, "why would I even use GPT" cheap.

Exact per-token pricing is on platform.deepseek.com but expect somewhere around 5-10x cheaper than Claude and GPT for equivalent output.

For agents firing thousands of calls a day, this matters.

A lot.

DeepSeek V4 vs The Competition

Quick summary of how I'd use each:

DeepSeek V4 → cheap high-volume agent calls, factual tasks
Claude Opus 4.6 → polished output, creative, long code
GPT 5.5 → general purpose, best overall vibes
Kimi K2.6 → agent swarms, open source alternative

Frequently Asked Questions

Is DeepSeek V4 free?

Yes — chat.deepseek.com is free to use.

API usage is paid but very cheap.

You can also run V4 Flash locally for free via LM Studio or Hugging Face.

Is DeepSeek V4 better than GPT 5.5?

On benchmarks — some yes, some no.

On real-world UI / creative tasks in my testing, GPT 5.5 is ahead.

On factual Simple QA benchmarks, DeepSeek V4 beats both GPT and Claude.

What's the difference between DeepSeek V4 Pro and Flash?

Pro is 1.6T parameters with 49B active.

Flash is 284B with 13B active.

Pro is more capable.

Flash is cheaper and easier to run locally.

What is Deep Think mode in DeepSeek V4?

Deep Think is the optional reasoning mode inside Expert mode on chat.deepseek.com.

It uses up to 384K thinking tokens for hard problems.

Use it for maths, complex coding, and multi-step reasoning.

When do the old DeepSeek endpoints retire?

deepseek-chat and deepseek-reasoner retire after July 24.

Migrate your scripts to the V4 endpoints before then.

Can I run DeepSeek V4 locally?

Yes — V4 Flash runs on consumer GPUs via LM Studio or Hugging Face.

V4 Pro is too big for most home rigs.

Final Verdict

DeepSeek V4 is a brilliant engineering achievement with world-class benchmarks and absurd efficiency — just don't expect Claude-level UI polish yet, and you'll get real value out of this deepseek v4 tutorial.