Most users comparing DeepSeek and Qwen are confused because both ecosystems are strong, open-source, and fast-moving—yet they are built to solve completely different problems. DeepSeek focuses on deep reasoning, chain-of-thought stability, math/coding accuracy, and MoE-based efficiency, while the Qwen family focuses on full-stack deployment, covering everything from huge MoE models to tiny edge models, plus multimodal, RAG, embedding, coding, and enterprise-ready tools.
This article clarifies these differences by examining their flagship models, distilled variants, efficient series, RAG models, and hardware requirements so users can understand what each ecosystem is actually trying to achieve and which one fits their operational needs.
DeepSeek vs Qwen is Really Trying to Do What？
If you’re wondering which open-source Chinese LLM ecosystem fits your needs, the two biggest players right now are DeepSeek and Qwen family. They’re both extremely strong, but they’re solving different problems and heading in different directions.
DeepSeek：“We Want Models That Can Actually Think Deeply”
Think of DeepSeek as the “reasoning specialist.”
What they care about most:
- Making models that are genuinely good at hard, step-by-step thinking — math proofs, science problems, complex coding, logical puzzles.
- Pushing the limits of chain-of-thought (CoT) reasoning so the model doesn’t just sound smart… it actually solves the problem correctly and can show its work.
- Using clever tricks like Mixture-of-Experts (MoE) + reinforcement learning so the model is powerful without needing to turn on billions of parameters for every single token (this keeps inference cheaper and faster).
- Releasing smaller “distilled” versions of their best reasoning models so normal people and smaller companies can actually run them.
The real-world problems they’re attacking:
- Most giant models are great at writing essays but still fail basic math or logic questions. DeepSeek wants to fix that.
- Bigger isn’t always better for reasoning — they’re trying to get more reasoning power from fewer active parameters (more bang for your GPU buck).
- High-end reasoning models are usually too expensive to run outside of big labs. DeepSeek wants to democratize that capability.
- When you need the model to explain how it arrived at an answer (legal, medical, education, etc.), you want transparent chain-of-thought — DeepSeek exposes this really well.
Best for: research, education, coding assistants, math/science tools, any situation where “getting the right answer + showing the work” matters more than being a general chatbot.
Qwen：“We Want a Complete Toolbox for Real Companies”
Qwen is more like the “Swiss Army knife” of LLMs.
What they care about most:
- Offering every size and flavor you might need: tiny models for phones, medium ones for servers, huge ones for maximum power, dense or MoE versions, vision models, coder models, embedding models, reranker models… you name it.
- Strong multilingual performance (especially Chinese + 100+ other languages).
- Very long context windows (up to 128k or even 1M tokens in some versions).
- Ready-for-business deployment: easy API, on-prem options, edge-device support, enterprise-grade security and tooling.
The real-world problems they’re attacking:
- Companies don’t just want a chatbot — they need document understanding, search, retrieval-augmented generation (RAG), image+text apps, multilingual customer support, etc. Qwen provides the whole stack.
- Older models choke on long documents or break when you switch languages. Qwen handles both gracefully.
- You often need tiny models for mobile/edge and giant models for heavy analysis — Qwen gives you a smooth ladder of sizes so you’re never stuck.
- Building a proper enterprise search or knowledge-base system requires great embeddings + reranking. Qwen’s embedding and reranking models are some of the best openly available.
Best for: enterprise search engines, multilingual customer service bots, document-heavy workflows, RAG pipelines, apps that combine vision + text, or any production system where reliability and easy deployment matter.
So Which One Should You Pick?
- If your project lives or dies by logical reasoning, math, or code accuracy → go DeepSeek (especially DeepSeek-R1 or the new DeepSeek-V3 reasoning models).
- If you’re building a real product with search, long documents, multiple languages, images, or need models from 0.5B to 72B → go Qwen.
DeepSeek Model Ecosystem
The DeepSeek models are primarily focused on maximizing reasoning power through large-scale Mixture-of-Experts (MoE) architectures and intensive Reinforcement Learning (RL) pipelines, resulting in precise, high-performance models (671B–685B) and specialized smaller versions (Distill models).
DeepSeek Flagship Models
Here are detailed architecture summaries of each DeepSeek model variant in English:
| Variant | Total Params / Activated Params | Context Window | Key Architecture & Enhancements |
|---|---|---|---|
| DeepSeek V3 | 671B total, 37B active per token | 128K tokens | Mixture-of-Experts (MoE) architecture; uses Multi-Head Latent Attention (MLA) to reduce KV-cache size; uses Multi-Token Prediction (MTP) objective; uses auxiliary-loss-free load balancing. |
| DeepSeek R1 | 671B total, 37B active per token | 128K tokens | Same base architecture as V3 (MoE + MLA) but with intensive RL pipeline (SFT → RL → SFT → RL) to enhance reasoning/logical capabilities. |
| DeepSeek V3.1 | 671B total, 37B active per token | 128K tokens | Hybrid inference modes: supports “Think” (chain-of-thought) and “Non-Think” modes; combines V3 general capability with R1 reasoning strength; extended long-context training. |
| DeepSeek R1 0528 | 685B total parameters (active subset unspecified) | 64K tokens | Updated R1 version with heavier parameter count and reduced context window to ~64K for improved inference speed/stability (rather than full 128K). (Data from variant listing) |
| DeepSeek V3 0324 | 671B total, 37B active per token | 128K tokens | Same architecture as V3 but optimized for multilingual processing (especially Chinese), enhanced Function Calling, improved frontend/web development use-cases. |
DeepSeek Distilled Models
Transfer DeepSeek’s reasoning ability (logic, math, step-wise thinking, CoT stability) into smaller, dense models that are cheaper, faster, and runnable on consumer GPUs.
| Distilled Model | Base Model | Strengthened Capabilities |
|---|---|---|
| R1-Distill Qwen 32B | Qwen 2.5–32B | Strong CoT, better logic stability, improved multilingual reasoning |
| R1-0528 Qwen3 8B | Qwen3 8B | High reasoning accuracy (AIME 86%), efficient CoT, fast inference |
| R1-Distill Qwen 7B | Qwen 2.5 Math-7B | Exceptional math accuracy (MATH-500 92.8%), structured step-wise reasoning |
| R1-Distill Llama 8B | Llama-8B | Better instruction following + compact reasoning behavior |
| R1-Distill Llama 70B | Llama-70B | Strong general reasoning, stable long-form CoT, consistent outputs |
Qwen Model Ecosystem
The Qwen family (Qwen 2.5 and Qwen 3) offers a highly flexible range of models from 0.6B to 480B parameters, emphasizing multilingual support, extensive context handling, and specialized variants for coding, embedding, and multimodal tasks.
Qwen Flagship Models
| Variant | Total Params / Active Params | Context Window | Key Focus / Features |
|---|---|---|---|
| Qwen3-Coder 480B-A35B-Instruct | 480B / 35B (MoE) | 256K native, extendable to ~1M tokens | Agentic coding and multi-file repository understanding; function-call/tool use optimized; non-thinking mode only |
| Qwen3-VL-235B-A22B | 235B / 22B (MoE) | 256K native (expandable to ~1M) | Multimodal vision-language (images/videos) model; excels at visual-to-code, 3D reasoning, OCR; has Instruct/Thinking variants |
| Qwen3 32B | 32B / dense | 128K tokens | General-purpose reasoning + multilingual support; dense backbone for lower cost deployment |
| Qwen2.5-72B Instruct | 72B (Dense or MoE variant) | 128K tokens | Strong multilingual support (29+ languages); |
Qwen 3 Efficient Models
The Qwen 3 series introduced a comprehensive set of smaller models, all supporting the highly efficient “Hybrid Thinking Modes” (Thinking vs. Non-Thinking) and broad multilingual support (119 languages).
| Variant | Total Parameters | Context Window | Key Focus / Features |
|---|---|---|---|
| Qwen3-14B | 14.8B | 32,768 tokens native; extendable up to 131,072 | General-purpose strong mid-size model; supports “thinking” & “non-thinking” modes; multilingual and agent capabilities |
| Qwen3-8B | 8.19B | 128K tokens | Lightweight reasoning model; competitive in math & general reasoning tasks |
| Qwen3-4B | 4.0B | 32K tokens native (extendable) | Optimized for efficiency; lower-resource deployments, maintaining strong performance |
| Qwen3-1.7B | 1.7B | 32K tokens | Suitable for edge use / fast chatbots; minimal footprint |
| Qwen3-0.6B | 0.6B | 32K tokens | Ultra-light model for high-concurrency / on-device deployment |
Qwen 3 RAG Models
he Qwen3 Embedding line reflects a recognition that retrieval + embeddings + retrieval-augmented workflows are central to modern AI applications (search, QA, RAG, code).
| Variant | Total Parameters / Active | Context Window | Key Focus / Features |
|---|---|---|---|
| Qwen3-Embedding 8B | 8B | 32K tokens | Text-embedding model; multilingual (>100 languages); long-input support; configurable embedding dims up to 4096; excels on MTEB benchmark (70.58) |
| Qwen3-Reranker 8B | 8B | 32K tokens | Cross-encoder reranking model; sorts retrieved documents by relevance in RAG pipelines; high precision in multilingual retrieval |
How to Access Deepseek and Qwen in Cheap and Fast Way?
1. Web Interface (Easiest for Beginners)
2. API Access (For Developers)
Step 1: Log In and Access the Model Library
Log in to your account and click on the Model Library button.
Step 2: Choose Your Model
Browse through the available options and select the model that suits your needs.
Step 3: Start Your Free Trial
Begin your free trial to explore the capabilities of the selected model.
Step 4: Get Your API Key
To authenticate with the API, we will provide you with a new API key. Entering the “Settings“ page, you can copy the API key as indicated in the image.
Step 5: Install the API
Install API using the package manager specific to your programming language.
After installation, import the necessary libraries into your development environment. Initialize the API with your API key to start interacting with Novita AI LLM. This is an example of using chat completions API for python users.
3. Local Deployment (Advanced Users)
| Model | Total VRAM (FP16 Inference) | Minimum Consumer Setup |
|---|---|---|
| DeepSeek-V3 / R1 / V3.1 671B MoE | ~780–820 GB | 8× RTX 4090 (24 GB) barely possible with heavy offloading |
| DeepSeek-R1-0528 685B | ~800–850 GB | 8× H100 80 GB (tight) |
| DeepSeek-V3-0324 671B | ~780–820 GB | 8× RTX 4090 (24 GB) barely possible with heavy offloading |
| Model | Quantization | VRAM Required | Feasible Consumer Setup |
|---|---|---|---|
| DeepSeek-R1/V3 671B | 4-bit (NF4/GPTQ/AWQ) | 170–190 GB | 8× RTX 4090 or 4× H100 80 GB |
| DeepSeek-R1/V3 671B | INT8 | 340–380 GB | 6–8× RTX 4090 or 4× A100/H100 80 GB |
| Model | VRAM (FP16) | Consumer GPU That Can Run It |
|---|---|---|
| R1-Distill-Qwen-32B | 64 GB | 2× RTX 4090 |
| R1-0528-Qwen3-8B / Llama-8B | 16 GB | 1× RTX 4090 / 3090 Ti |
| R1-Distill-Qwen-7B Math | 14 GB | 1× RTX 4080/4090 |
| R1-Distill-Llama-70B | 140 GB | 4× RTX 4090 or 2× A100 80 GB |
| Model | Total VRAM (FP16/BF16) | Minimum Consumer Setup |
|---|---|---|
| Qwen3-Coder 480B MoE | 560–600 GB (35B active) | 8× H100 80 GB |
| Qwen3-VL-235B MoE | 280–320 GB (22B active) | 4× H100 80 GB |
| Qwen2.5-72B / Qwen3-32B Dense | 140–160 GB | 4× RTX 4090 or 2× A100 80 GB |
| Qwen3-14B | 28–32 GB | 1× RTX 4090 |
| Qwen3-8B | 16–18 GB | 1× RTX 4080/4090 |
| Qwen3-4B | 8–10 GB | 1× RTX 4060 Ti / 4070 |
| Qwen3-1.7B & 0.6B | 4 GB | Mobile phones, RTX 3050 |
| Qwen3-Embedding / Reranker 8B | 16 GB | 1× RTX 4090 |
Installation Steps:
- Download model weights from HuggingFace or ModelScope
- Choose inference framework: vLLM or SGLang supported
- Follow deployment guide in the official GitHub repository
4. Integration
Using CLI like Trae,Claude Code, Qwen Code
If you want to use Novita AI’s top models (like Qwen3-Coder, Kimi K2, DeepSeek R1) for AI coding assistance in your local environment or IDE, the process is simple: get your API Key, install the tool, configure environment variables, and start coding.
For detailed setup commands and examples, check the official tutorials:
- Trae : Step-by-Step Guide to Access AI Models in Your IDE
- Claude Code:How to Use Kimi-K2 in Claude Code on Windows, Mac, and Linux
- Qwen Code:How to Use OpenAI Compatible API in Qwen Code (60s Setup!)
Multi-Agent Workflows with OpenAI Agents SDK
Build advanced multi-agent systems by integrating Novita AI with the OpenAI Agents SDK:
- Plug-and-play: Use Novita AI’s LLMs in any OpenAI Agents workflow.
- Supports handoffs, routing, and tool use: Design agents that can delegate, triage, or run functions, all powered by Novita AI’s models.
- Python integration: Simply set the SDK endpoint to
https://api.novita.ai/v3/openaiand use your API key.
Connect API on Third-Party Platforms
OpenAI-Compatible API: Enjoy hassle-free migration and integration with tools such as Cline and Cursor, designed for the OpenAI API standard.
Hugging Face: Use Modeis in Spaces, pipelines, or with the Transformers library via Novita AI endpoints.
Agent & Orchestration Frameworks: Easily connect Novita AI with partner platforms like Continue, AnythingLLM,LangChain, Dify and Langflow through official connectors and step-by-step integration guides.
DeepSeek targets maximal reasoning power with models such as DeepSeek-V3, DeepSeek-R1, and DeepSeek-V3.1, supported by lightweight distillations like R1-Distill-Qwen-32B and R1-Distill-Qwen3-8B. Qwen aims for versatility and enterprise readiness with models like Qwen3-Coder-480B-A35B-Instruct, Qwen3-VL-235B-A22B, efficient models from Qwen3-14B to Qwen3-0.6B, and RAG-oriented models such as Qwen3-Embedding-8B and Qwen3-Reranker-8B. In short: DeepSeek optimizes for deep reasoning performance; Qwen optimizes for a complete, deployable, multilingual, multimodal AI toolbox.
Frequently Asked Questions
DeepSeek-V3 uses a MoE architecture with MLA and MTP to maximize reasoning quality, while Qwen models focus more on multilingual coverage, deployment range, and application versatility.
DeepSeek-V3.1 offers hybrid “Think / Non-Think” reasoning modes optimized for chain-of-thought depth, while Qwen3-14B prioritizes general-purpose inference, multilingual tasks, and efficient deployment.
Qwen excels with models like Qwen3-Coder-480B-A35B-Instruct and Qwen3-VL-235B-A22B offering context up to 256K–1M tokens, whereas DeepSeek focuses on reasoning rather than ultra-long-context document handling.
Novita AI is an AI cloud platform that offers developers an easy way to deploy AI models using our simple API, while also providing an affordable and reliable GPU cloud for building and scaling.
Recommend Reading
- How to Access Qwen3-Next-80B-A3B in Trae with Extended Context Support
- Comparing Kimi K2-0905 API Providers: Why NovitaAI Stands Out
- How to Use GLM-4.6 in Cursor to Boost Productivity for Small Teams
Discover more from Novita
Subscribe to get the latest posts sent to your email.
