“I’ve used GPT-4, Claude, Gemini… and then I stumbled upon DeepSeek R1. And honestly? Something clicked. This wasn’t just another LLM—it felt like a signal.”
Let’s talk about that moment.
You know the one—where a model you never heard of does something smarter, faster, cleaner than the big names. That’s DeepSeek R1.
It doesn’t show off. It doesn’t flood your feed. But it’s here. And it’s already quietly rewriting the AI narrative in China—and maybe the world.
🧠 What Is DeepSeek R1, Really?
Okay, let’s cut through the buzzwords.
DeepSeek R1 is an open-source language model developed by the Chinese research group DeepSeek AI, designed to go toe-to-toe with giants like GPT-4 and Claude 3.
But here’s the catch — it’s not just powerful, it’s radically transparent. Think: 236B-token training data openly documented. GitHub access. Full inference stack released.
In other words? It’s not locked in a black box. It’s not “demo-only.” You can actually build on it.
“Open-source LLMs are the future of sovereign AI ecosystems. DeepSeek R1 might be China’s ticket.”
— A Stanford AI researcher I chatted with over Signal
And here’s why that’s huge.
🚀 What Makes DeepSeek R1 Special?
Let’s break it down like a real person, not a datasheet:
It’s Free. Like, actually free. No API fees. No credit card walls. Download it, run it, fork it.
Trained with Fire. 1.4 trillion tokens. 16k context window. Multilingual. Code fine-tuning. It’s smart in the ways that matter.
China’s Flagship Open Model. This isn’t a side project—it’s a nation-scale chess move.
Competitive Benchmarks. On tasks like MMLU, HumanEval, and GSM8K? It’s giving GPT-4 a real chase.
💡 Real-world test: I asked DeepSeek R1 to generate an RFP for a FinTech startup in both Mandarin and English. The result? Fluent. Precise. Culturally contextual. That’s rare.
🤔 So… Is It Better Than GPT-4?
Let’s not pretend there’s a simple answer.
In some reasoning tasks, GPT-4 still flexes harder. But in code, multilingual support, and fine-tuning flexibility? DeepSeek R1 doesn’t just compete—it sometimes wins.
Here’s the spicy part:
If you’re building in China (or for Chinese markets), R1 is the closest thing to local LLM sovereignty you’ll get today.
Plus, you’re not betting on a closed corp with shifting API prices.
🌍 Why This Really Matters (Zoom Out)
Imagine a world where:
AI agents are localized and culturally aware.
Startups don’t depend on US-based APIs.
Researchers can peer-review LLMs instead of trusting faceless benchmarks.
DeepSeek R1 is a glimpse into that world.
And I get it—everyone’s distracted by GPT-5 rumors or Claude 3.5 teasers.
But DeepSeek R1? It’s like that quiet startup founder working nights while everyone else is networking. The one who builds the thing that actually scales.
🔧 Want to Try It?
Here’s your starter pack:
GitHub Repo: https://github.com/deepseek-ai
Model Weights: Available under the OpenRAIL license
DeepSeek Playground: Simple UI to test prompts
Fine-Tune Support: Supports QLoRA, PEFT, and even custom embeddings
Pro tip: If you’re an indie dev, try running it on a rented A100 with 80GB RAM. You’ll see what I mean.
Creativity & Professional Development
What is the R1 model in DeepSeek?
I remember the exact moment I stumbled across DeepSeek R1.
I was doomscrolling through GitHub late one night (like any normal person, right?) when I spotted it: “DeepSeek R1: Open-Source Reasoning Model.”
No fancy press release. No hype monster. Just raw code, open weights, and a wild claim — better reasoning than OpenAI’s o1.
Naturally, I had to dive in.
And wow… this thing hits different.
In Simple Words: What is DeepSeek R1?
DeepSeek R1 is China’s bold open-source large language model (LLM) built by DeepSeek AI.
It’s their no-holds-barred attempt to create a reasoning-first AI model that doesn’t just spit out generic text — it thinks.
How’d they do it?
They trained R1 using massive-scale reinforcement learning (RL) across math, code, and complex language tasks.
The secret sauce? Instead of just stuffing it with internet text (like most models), they made it practice thinking — solving, predicting, improving.
Imagine raising a child who isn’t just memorizing flashcards but actually solving riddles all day.
That’s R1’s vibe.
Why Is Everyone Whispering About It?
Here’s the part that made me sit up:
Performance on Fire:
R1 smashes benchmarks like MMLU and HumanEval, hanging neck-and-neck with OpenAI’s o1 model in key reasoning tasks.Costs Pennies:
Believe it or not, DeepSeek trained R1 for ~96% less cost compared to Western models.
(Yeah, I had to double-check that too.)Open to the Bones:
Not just “here’s a half-baked API.”
They dropped full weights, inference code, and docs under the MIT License.
No gatekeeping. No “Apply to Join Our Beta.” Just… here you go.
Real-World Moment: When R1 Flexed on Me
I threw a tricky finance case study at R1 — one that ChatGPT-4 usually nails, but Gemini stumbles on.
Not only did R1 solve it, but it also explained the reasoning path without hallucinating extra assumptions.
It felt eerily like talking to a PhD student who was actually good at tutoring — concise but not arrogant.
It wasn’t perfect (sometimes it drifts when you push it too abstract),
but man, for an open-source model? It’s criminally good.
Okay, But Is It Better Than GPT-4?
Short answer?
Not universally, no.
GPT-4 still reigns supreme in nuanced creativity, emotional writing, and super-deep reasoning.
But when it comes to:
Multilingual support
Coding tasks
Cost-efficiency and open deployability
R1 punches way above its weight class.
For devs, startups, researchers? It’s a godsend.
Honestly, if you’re building a scrappy AI product and don’t want OpenAI breathing down your neck, R1 feels like freedom.
Is DeepSeek R1 Better Than V3?
If you’re expecting a polite “it depends” kind of answer, buckle up — because honestly? It’s a street fight.
And here’s my take:
👉 On raw reasoning, math, and coding tasks? DeepSeek R1 can absolutely go blow-for-blow with V3.
But — and it’s a big but — V3 (especially DeepSeek-V3, the upgraded model) shows more muscle in broader language creativity, multi-turn conversation, and subtle context shifts.
Here’s the real-world flavor:
I threw the same prompts at both — complex math proofs, Rust coding tasks, creative writing, and an emotional letter to a startup founder.
Result?
🧠 DeepSeek R1: Freakishly good at structure-heavy tasks. Super disciplined. Solves problems like a battle-tested engineer who’s had too much coffee.
🎨 DeepSeek V3: Wears two hats — logic and art. It reasons, but also crafts stories, jokes, nuanced advice with that human-ish “feel” that R1 sometimes misses.
Quick Punch-by-Punch Breakdown:
| Feature | DeepSeek R1 | DeepSeek V3 |
|---|---|---|
| 🔢 Math/Code | 9.5/10 | 9/10 |
| 🗣️ Storytelling/Creativity | 7/10 | 9.5/10 |
| 📚 Multi-Turn Consistency | 8/10 | 9/10 |
| 💸 Open-Source Access | Full & Free | Limited (until recently) |
| ⚡ Speed & Cost | Super Efficient | Heavier Model |
Here’s the Real Tea:
If you’re building a hardcore app — say a finance bot, a coding tutor, or a math coach? DeepSeek R1 is your no-nonsense partner.
But if you’re crafting something that needs heart + brain — like a life coach bot, an AI storyteller, or an emotional support agent?
V3 is clearly the grown-up in the room.
💡 Think of R1 like a laser-focused samurai.
💡 V3? It’s the seasoned philosopher-warrior.
Is DeepSeek R1’s Benchmark Better Than o3?
Short, no-fluff answer?
👉 Yes… but with serious context you need to know.
When we talk pure benchmarks — like MMLU, HumanEval, GSM8K, all that hardcore academic stuff — DeepSeek R1 actually edges out OpenAI’s o3 (that’s the new lightweight OpenAI model, not GPT-4).
Crazy, right?
And no, you’re not hallucinating. Let’s break it down street-level:
Numbers You’ll Wanna Tattoo on Your Brain:
| Benchmark | DeepSeek R1 | OpenAI o3 |
|---|---|---|
| MMLU (Knowledge Reasoning) | Higher | Lower |
| HumanEval (Coding Ability) | Higher | Lower |
| GSM8K (Math Word Problems) | Competitive | Competitive |
👉 On knowledge-based reasoning and coding ability, DeepSeek R1 pretty much owns o3.
👉 On common sense conversation and casual dialogue, o3 feels a little lighter and snappier — but it’s also more shallow.
What This Really Means for You:
If you’re building hardcore apps like financial advisors, coding copilots, math tutors — DeepSeek R1 wins without blinking.
If you just want smooth casual bots for chit-chat, basic customer support, or memes? o3 might “feel” slightly faster and friendlier.
Real Talk: Why This Matters (More Than You Think)
DeepSeek R1 is open-source.
o3? Nope.
You can’t even touch o3 under the hood without OpenAI’s API gate.
So not only does R1 out-benchmark o3, it also out-frees it.
You can literally download R1, tweak it, fine-tune it, and deploy it wherever you want — no corporate babysitter required.
Freedom and performance? Yeah.
That’s a power combo Silicon Valley doesn’t want you tweeting about.
What Is DeepThink R1?
DeepThink R1 is a specialized mode within the DeepSeek R1 AI model that enhances its reasoning capabilities. When activated, this mode enables the model to perform more complex reasoning tasks by simulating a human-like thought process. It’s designed to tackle challenges in areas like mathematics, coding, and logical problem-solving by breaking down problems into intermediate steps before arriving at a final answer.
How Does It Work?
When you enable DeepThink R1 mode, the model doesn’t just provide an answer; it walks you through its reasoning process. This approach, often referred to as “chain-of-thought” reasoning, allows users to see the steps the model takes to arrive at a conclusion. This transparency can be particularly useful for understanding complex solutions or for educational purposes.
Is DeepSeek R1 a foundation model?
Yes — DeepSeek R1 is a foundation model, and quite a solid one at that. But let me give you the real story, not just textbook definitions.
So… What’s a Foundation Model Anyway?
A foundation model is like the mothership — a giant AI model trained on a massive, diverse dataset that can be fine-tuned for tons of specific tasks:
Chatbots ✅
Code generators ✅
Math tutors ✅
Content writers ✅
Agents, assistants, tools — you name it.
Think: GPT-4, Claude, Gemini… all foundation models.
Why DeepSeek R1 Qualifies as One
Now let’s talk DeepSeek R1:
Trained on 1.4 trillion tokens — that’s borderline bonkers.
Multimodal-ish capabilities (text, code, logic).
Can be fine-tuned into smaller specialized agents (finance bots, math solvers, etc.).
Performs well across domains: reasoning, coding, academic Q&A, and multilingual tasks.
It ticks every box that makes a model foundational.
And here’s the kicker: it’s fully open-source — meaning you can literally build your own empire on top of it.
How Does DeepSeek R1 Work?
Think of DeepSeek R1 as a giant brain trained to reason — not just predict the next word like a parrot, but actually think through stuff.
Here’s how it gets the job done under the hood:
1. Massive Pretraining (Like Teaching a Super Curious Kid)
DeepSeek R1 was trained on a 1.4 trillion token dataset — that’s everything from:
Books
Math problems
Programming code
Scientific papers
Web content (multilingual)
But here’s the twist:
Instead of just memorizing patterns, R1 learned to reason through them using reinforcement learning and advanced fine-tuning.
It wasn’t just fed answers — it practiced solving problems step by step.
This is what gives it such sharp reasoning chops.
2. Transformer Architecture (The Brains of the Operation)
Like GPT-4 or Claude, DeepSeek R1 runs on a transformer model —
61 layers deep, to be exact.
What does that mean?
Each layer acts like a filter, refining the context and logic behind every token.
It can look at huge stretches of context (16k tokens at once), allowing it to track long conversations, complex documents, or code snippets.
It’s like a chess master planning 15 moves ahead — not just reacting but strategizing.
3. Chain-of-Thought Reasoning (This is the Secret Sauce)
Here’s where DeepSeek R1 really flexes:
When faced with a complex query (like “solve this math proof” or “write a Python function to reverse-engineer a password hash”), it doesn’t just output the answer…
👉 It walks through the reasoning steps — out loud.
That’s called chain-of-thought prompting, and R1 handles it like a pro.
You can even turn on DeepThink Mode, where it slows down to “think” before answering.
4. Training Efficiency (Seriously Impressive)
Here’s what blew my mind:
Despite its scale, DeepSeek R1 was trained 96% cheaper than Western counterparts like GPT-4.
They pulled this off with custom optimizations, smarter data curation, and lean infrastructure.
Translation?
You get GPT-4-level performance without the $20/month price tag or API locks.
5. It Learns From Feedback (Reinforcement Learning FTW)
During training, R1 didn’t just guess answers and move on.
It used reinforcement learning — kinda like trial and error — to reward good reasoning paths and punish lazy guesses.
Over time, this made it really, really good at logic-heavy tasks.
🔹 Can DeepSeek R1 generate images?
No. It’s a pure text-based language model — no image generation like DALL·E or MidJourney.
🔹 Is there a limit on DeepSeek R1?
Yes. It has a 16k token context window, and performance depends on your compute if self-hosted.
🔹 What is the difference between R1 and R1M?
R1 is the full model (~64B params); R1M is a lighter version (~7B) for faster, low-resource use.
🔹 Is DeepSeek R1 a small language model?
Nope. It’s a large language model (LLM) — comparable in scale to GPT-3.5+.
🔹 What is the use of R in DeepSeek?
“R” stands for Reasoning — DeepSeek R1 is optimized for tasks that require logic, math, and step-by-step thinking.
🔹 Is ChatGPT better than DeepSeek?
Depends.
For casual chat and creativity, ChatGPT (especially GPT-4) is better.
For math, code, open-source flexibility, DeepSeek R1 holds its ground — and it’s free.
🔹 What is DeepSeek R1 good for?
Advanced reasoning, coding, math, multilingual tasks, and building AI tools — especially when you want full control.
🔹 Is DeepSeek worth it?
Absolutely.
If you want an open, powerful GPT-4 alternative without API restrictions, DeepSeek R1 is a no-brainer.
🔹 What is special about R1?
It’s open-source, extremely good at logical reasoning, and trained 96% cheaper than GPT-4-level models.
🔹 Is DeepSeek R1 better than V3?
Depends.
R1 crushes V3 in math, code, and pure logic, but V3 has better language creativity and conversation flow.
🔹 Is DeepSeek R1 an LLM?
Yes.
It’s a large language model (around 64B parameters) — not some small chatbot.
🔹 What are the limitations of DeepSeek-R1?
No image generation.
Slightly slower in casual conversation.
Needs solid compute (especially for full model inference).
🔹 How much does it cost to train DeepSeek-R1?
Way less than GPT-4.
Estimates suggest ~4% of what Western LLMs like GPT-4 cost to train, thanks to smart optimizations.
🔹 Is DeepSeek R1 a transformer?
Yes.
A full transformer architecture with 61 layers — like GPTs, just tuned differently.
🔹 Is DeepSeek safe?
Mostly, yes.
It follows safe generation practices, but since it’s open, you are responsible for fine-tuning and deploying it properly.
🔹 Is DeepSeek R1 a foundation model?
100%.
It’s designed as a base model you can fine-tune for tons of downstream tasks — like OpenAI’s GPT models.
🔹 Why is DeepSeek always busy?
Because it’s free, powerful, and in high demand — especially among developers, startups, and researchers wanting open access.
🔹 Is DeepSeek R1 free?
Yes.
The full model weights are open-source and free to download under the MIT license.
🔹 Can DeepSeek R1 reason?
Yes, and it’s scary good.
It can walk through logical steps, math problems, and code challenges using chain-of-thought techniques.
🔹 Is DeepSeek-R1 free?
Yes. It’s fully open-source, MIT-licensed, and free to download, fine-tune, and deploy.
🔹 How is DeepSeek so cheap?
They optimized everything — from training infrastructure to token selection — making it ~96% cheaper to train than GPT-4-tier models.
🔹 Which AI API is free?
DeepSeek offers some free access, and others like Hugging Face Inference API or Mistral (limited tiers) can also be free with restrictions.
🔹 Why is DeepSeek completely free?
It’s part of China’s open-source LLM strategy — giving developers a powerful, no-strings-attached alternative to US-based models.
🔹 How much does it cost to run R1?
If self-hosted, it depends on hardware. Expect $1–$4/hour on GPUs like A100s.
Cheaper if quantized or running R1M (lite version).
🔹 Do you have to pay for DeepThink R1?
Nope.
DeepThink is just a mode within DeepSeek R1 that enables detailed reasoning — and it’s free if you’re running it yourself.
🔹 How to access DeepSeek R1 for free?
Download from Hugging Face
Use DeepSeek’s playground (if not overloaded)
Clone from GitHub and run locally or on Colab
🔹 Can DeepSeek run locally?
Yes.
You can deploy it on your own server or local GPU — just be sure your hardware can handle the model size (~64B params).
🔹 How much does DeepSeek cost?
The model itself is free, but running it will cost compute. That’s your only cost unless you use paid hosting or cloud GPUs.
🔹 Is DeepSeek R1 better than o1?
Yes, in reasoning and coding.
Benchmarks show R1 outperforms OpenAI’s o1 in math, logic, and human-eval tasks.
🔹 Is DeepSeek safe to use?
Generally yes.
But because it’s open, safety depends on how you use and fine-tune it. You control the output — not DeepSeek.
🔹 Is ChatGPT better than DeepSeek?
Depends on use-case.
ChatGPT (esp. GPT-4) is smoother in casual conversation.
DeepSeek R1 is stronger in reasoning, code, and open-source customization.
🔹 Is DeepSeek a free app?
No official app yet.
But you can use it online for free (when servers aren’t full) or build your own app using its model.
🔹 Is DeepSeek-R1 better than ChatGPT?
It depends.
DeepSeek R1 is better at math, code, and logical reasoning, especially if you want full control. But ChatGPT (especially GPT-4) is smoother, more creative, and more consistent in conversations.
🔹 How is DeepSeek so cheap?
They trained it using smart data filtering, efficient compute usage, and localized infrastructure — reportedly at ~96% less cost than GPT-4-tier models.
🔹 Which AI is better than ChatGPT?
There’s no single winner, but contenders include:
Claude 3.5 (more “human” in tone)
Gemini 1.5 Pro (huge context window)
DeepSeek R1 (reasoning + open-source power)
🔹 Why is DeepSeek cheaper than ChatGPT?
Because it’s open-source and doesn’t rely on a massive cloud-hosted API business model.
Also, you host it, so they’re not charging you for infrastructure.
🔹 How much does DeepSeek cost?
The model is free.
But if you want to run it yourself, you’ll need hardware — costs vary from $1–$5/hr on cloud GPUs unless you use a quantized version locally.
🔹 Is DeepSeek a copy of ChatGPT?
No.
It’s a separate, independently trained large language model. Similar architecture (transformer-based), but not a clone — it’s China’s open-source alternative.
🔹 Which app beats ChatGPT?
For specific use-cases:
Perplexity for real-time research
Claude 3.5 for emotional intelligence and writing
DeepSeek Playground (when not overloaded) for reasoning
🔹 Why is DeepSeek free?
Because it’s part of a strategic push for open-source AI leadership — especially in China. They want devs, startups, and researchers to build freely.
🔹 Is DeepSeek using NVIDIA?
Yes.
They likely trained it on NVIDIA A100/H100 GPUs — industry standard for LLMs.
🔹 Is DeepSeek AI safe?
It’s as safe as you make it.
It doesn’t have ChatGPT’s safety layers out of the box — so if you’re deploying it, you need to implement safety filters and usage policies.
🔹 Is the DeepSeek app free?
There’s no official “DeepSeek app” — but their web playground is free when available, and you can deploy the model yourself at no cost.
🔹 Is DeepSeek R1 paid?
No.
The model weights are 100% free, and you can run them locally or on cloud hardware.
🔹 Can DeepSeek be detected by an AI detector?
Yes — text generated by DeepSeek can be flagged by AI detectors (just like GPT or Claude).
But it depends on prompt style, structure, and use case.
