A year ago, if you mentioned DeepSeek or Qwen in a planning meeting, you would have gotten blank stares. Maybe a "isn't that the Chinese one?" from someone who skimmed a Hacker News thread. Today, those two names represent roughly 15% of the global AI market -- up from barely 1% at the start of 2025. That is not a typo. That is the single fastest market share grab in the history of AI infrastructure.

I want to talk about what happened, why it matters, and what it means for those of us who actually build things with these models.

DeepSeek and Qwen market share surge in 2026 Open-source AI from China went from afterthought to serious contender in twelve months

The Numbers That Broke My Mental Model

Here is the market share picture as of January 2026, pieced together from Hugging Face download data, API usage reports, and enterprise adoption surveys:

Provider Estimated Global Share (Jan 2025) Estimated Global Share (Jan 2026) Change
OpenAI (GPT) ~55% ~40% -15%
Google (Gemini) ~15% ~14% -1%
Anthropic (Claude) ~10% ~12% +2%
Meta (Llama) ~8% ~10% +2%
Alibaba (Qwen) ~0.5% ~9% +8.5%
DeepSeek ~0.5% ~6% +5.5%
Mistral ~4% ~4% Flat
Others ~7% ~5% -2%

OpenAI still leads. But the story is the bottom of that table. Qwen and DeepSeek combined went from background noise to a force that is reshaping pricing, licensing, and deployment strategy across the industry.

The single most staggering number: Qwen has surpassed 700 million cumulative downloads on Hugging Face. That makes it the most downloaded open-source AI model family in the world, ahead of Llama, ahead of Mistral, ahead of everything.

What Actually Happened

DeepSeek: Efficiency as a Weapon

DeepSeek came out of nowhere in late 2024 and early 2025 with a simple thesis: you do not need trillion-dollar compute budgets to build frontier-class models. Their approach focused on training efficiency -- getting more intelligence per GPU hour than anyone thought possible.

The key innovations:

  • Multi-head Latent Attention (MLA) -- A rethinking of the attention mechanism that reduces memory overhead during inference without sacrificing quality. This is not a minor optimization. It changes the economics of serving models at scale.
  • Mixture-of-Experts done right -- DeepSeek-V3 and its successors use MoE architectures where the total parameter count is large (600B+), but only a fraction of parameters activate per forward pass. The result: frontier-level reasoning at mid-tier compute costs.
  • Aggressive open-weight releases -- They did not just publish papers. They dropped full model weights on Hugging Face under permissive licenses, repeatedly, with documentation that actually explained how to run the models.
DeepSeek Model Timeline
├── DeepSeek-V2 (May 2025)
│   ├── 236B total params, 21B active
│   ├── MLA + MoE architecture
│   └── Competitive with GPT-4 on most benchmarks
│
├── DeepSeek-V3 (September 2025)
│   ├── 671B total params, 37B active
│   ├── State-of-the-art reasoning
│   └── Trained for reportedly $5.6M (!!!)
│
├── DeepSeek-R1 (November 2025)
│   ├── Reasoning-specialized variant
│   ├── Chain-of-thought built into architecture
│   └── Matches o1-class reasoning on benchmarks
│
└── DeepSeek-Coder-V3 (January 2026)
    ├── Code-specialized model
    ├── Top-tier on HumanEval, SWE-bench
    └── Direct competitor to Claude for code tasks

That $5.6 million training cost number for DeepSeek-V3 deserves a moment. Even if the real cost is 2-3x higher once you account for failed runs and experimentation, compare it to the hundreds of millions that OpenAI, Google, and Anthropic spend per frontier model. DeepSeek proved that efficiency of approach can compensate for raw compute budget, and that changed how every AI lab thinks about resource allocation.

Qwen: The Breadth Play

Alibaba's Qwen team took a different path. Where DeepSeek focused on doing one thing exceptionally well -- efficient frontier models -- Qwen bet on breadth. They released models at every size point, for every use case, with every optimization technique applied.

Model Parameters Use Case License
Qwen2.5-0.5B 0.5B Edge/mobile, classification Apache 2.0
Qwen2.5-1.8B 1.8B On-device assistants Apache 2.0
Qwen2.5-7B 7B General purpose, chat Apache 2.0
Qwen2.5-14B 14B Complex tasks, reasoning Apache 2.0
Qwen2.5-32B 32B Enterprise workloads Apache 2.0
Qwen2.5-72B 72B Frontier performance Apache 2.0
Qwen2.5-Coder-7B 7B Code generation Apache 2.0
Qwen2.5-Coder-32B 32B Advanced code tasks Apache 2.0
Qwen2.5-Math-7B 7B Mathematical reasoning Apache 2.0
Qwen-VL-Plus Multimodal Vision + language Apache 2.0
Qwen-Audio Multimodal Audio understanding Apache 2.0

That is not a product line. That is an ecosystem. And every single one of those models ships under Apache 2.0, which means you can use them commercially, modify them, fine-tune them, and build products on top of them without calling a lawyer first.

The 700 million download number becomes less surprising when you see the range. Need a tiny model for mobile? Qwen has it. Need a coder? Qwen has it. Need multimodal? Qwen has it. Need something you can actually afford to fine-tune on a single A100? Qwen has it.

Why Developers Should Care

I have been building with various LLMs for the past two years, and here is my honest assessment of where things stand today.

The Model Comparison You Actually Need

Forget synthetic benchmarks for a moment. Here is how these models perform in the real-world tasks I care about:

Task GPT-4o Claude 3.5 Sonnet Llama 3.3 70B Qwen2.5-72B DeepSeek-V3
Code generation Excellent Excellent Very good Very good Excellent
Long doc analysis Good Excellent Good Very good Good
Reasoning chains Excellent Excellent Good Good Excellent
Instruction following Excellent Excellent Good Very good Very good
Multilingual Very good Good Good Excellent Very good
Math/Logic Good Good Fair Very good Excellent
Cost (self-hosted) N/A N/A Low Low Medium
Cost (API) High High Medium Low Very low
Data privacy Cloud only Cloud only Full control Full control Full control

A few things jump out:

  1. DeepSeek-V3's reasoning is genuinely frontier-class. I ran it against a set of tricky coding challenges and logic puzzles, and it matched or beat GPT-4o on most of them. That was not the case a year ago.

  2. Qwen2.5-72B is the best multilingual open-source model, period. If your user base is not English-first, Qwen is probably your best option right now.

  3. The self-hosted cost advantage is massive. Running Qwen2.5-72B on your own hardware costs a fraction of GPT-4o API calls at scale. For high-volume applications, we are talking about 10-50x cost savings.

The Real Advantage: No Vendor Lock-In

This is the part that matters most to me as someone who has been burned by API deprecations, surprise pricing changes, and model behavior shifts after updates. With open-weight models:

  • You control when and if you upgrade
  • Your costs are predictable (hardware + electricity, not per-token metering)
  • Your data never leaves your infrastructure
  • Your fine-tuned models belong to you
  • If the company behind the model disappears, your deployment keeps running

That last point is not theoretical. The AI landscape moves fast, and betting your product on a single closed API is a risk that open-source models now let you avoid without sacrificing much quality.

Running These Models: A Practical Guide

Enough theory. Let me show you how to actually run DeepSeek and Qwen models locally or on your own infrastructure.

Option 1: Ollama (Easiest)

Ollama is the fastest way to go from zero to running a model locally. It handles downloading, quantization, and serving.

# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh

# Pull and run DeepSeek models
ollama pull deepseek-v3
ollama run deepseek-v3 "Write a Python function to merge two sorted arrays"

# Pull and run Qwen models
ollama pull qwen2.5:72b
ollama run qwen2.5:72b "Explain the CAP theorem in distributed systems"

# Run smaller variants for constrained hardware
ollama pull qwen2.5:7b
ollama pull deepseek-coder-v3:7b

# Use the API for integration
curl http://localhost:11434/api/chat -d '{
  "model": "qwen2.5:72b",
  "messages": [
    {
      "role": "user",
      "content": "Refactor this function to handle edge cases"
    }
  ]
}'

Option 2: vLLM (Best for Production)

If you are serving models to multiple users or integrating into a production application, vLLM is the standard.

# Install vLLM
# pip install vllm

from vllm import LLM, SamplingParams

# Load DeepSeek-V3 with tensor parallelism across GPUs
llm = LLM(
    model="deepseek-ai/DeepSeek-V3",
    tensor_parallel_size=4,      # Spread across 4 GPUs
    max_model_len=32768,         # 32K context window
    gpu_memory_utilization=0.90,
    trust_remote_code=True
)

sampling_params = SamplingParams(
    temperature=0.7,
    top_p=0.9,
    max_tokens=2048
)

# Single inference
outputs = llm.generate(
    ["Write a comprehensive test suite for a REST API authentication module"],
    sampling_params
)

for output in outputs:
    print(output.outputs[0].text)
# vLLM also supports OpenAI-compatible API serving
# Start the server:
# python -m vllm.entrypoints.openai.api_server \
#     --model Qwen/Qwen2.5-72B-Instruct \
#     --tensor-parallel-size 4 \
#     --port 8000

# Then use it with any OpenAI-compatible client
import openai

client = openai.OpenAI(
    base_url="http://localhost:8000/v1",
    api_key="not-needed"
)

response = client.chat.completions.create(
    model="Qwen/Qwen2.5-72B-Instruct",
    messages=[
        {"role": "system", "content": "You are a senior software architect."},
        {"role": "user", "content": "Design a rate limiting system for a microservices architecture"}
    ],
    temperature=0.7,
    max_tokens=2048
)

print(response.choices[0].message.content)

Option 3: Hugging Face Transformers (Most Flexible)

For fine-tuning, research, or maximum control:

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load Qwen2.5-7B (fits on a single GPU)
model_name = "Qwen/Qwen2.5-7B-Instruct"

tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)

messages = [
    {"role": "system", "content": "You are a helpful coding assistant."},
    {"role": "user", "content": "Implement a thread-safe LRU cache in Python"}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

inputs = tokenizer([text], return_tensors="pt").to(model.device)
outputs = model.generate(
    **inputs,
    max_new_tokens=1024,
    temperature=0.7,
    top_p=0.9,
    do_sample=True
)

response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
print(response)

Hardware Requirements: What You Actually Need

Hardware Requirements by Model Size
│
├── Qwen2.5-7B / DeepSeek-Coder-7B (Quantized INT4)
│   ├── VRAM: 6 GB
│   ├── RAM: 16 GB
│   ├── GPU: RTX 3060 or better, Apple M1+
│   └── Cost: $0 (your existing dev machine)
│
├── Qwen2.5-32B (Quantized INT4)
│   ├── VRAM: 20 GB
│   ├── RAM: 32 GB
│   ├── GPU: RTX 4090, A5000, Apple M2 Pro+
│   └── Cost: ~$1,500-2,000 (GPU)
│
├── Qwen2.5-72B (Quantized INT4)
│   ├── VRAM: 48 GB
│   ├── RAM: 64 GB
│   ├── GPU: A6000, 2x RTX 4090, Apple M3 Ultra
│   └── Cost: ~$4,000-6,000 (GPU)
│
├── DeepSeek-V3 (Full precision)
│   ├── VRAM: 4x 80 GB (A100/H100)
│   ├── RAM: 256 GB
│   ├── GPU: 4x A100 or 4x H100
│   └── Cost: ~$2-4/hr on cloud
│
└── Budget Option: Cloud GPU Rental
    ├── Lambda Labs: $1.10/hr per A100
    ├── RunPod: $0.74/hr per A100
    ├── Vast.ai: $0.50/hr per A100 (spot)
    └── Together AI: API access, pay-per-token

The Geopolitical Elephant in the Room

I want to address this directly because it comes up every time I mention these models: Yes, DeepSeek and Qwen are Chinese AI models. No, that does not automatically make them unusable or dangerous.

Here is the nuanced reality:

The Export Control Paradox

The US government has imposed increasingly strict export controls on advanced AI chips, specifically targeting China. The logic: limit access to cutting-edge hardware, slow down Chinese AI development.

What actually happened:

  1. Chinese labs got more creative with efficiency. DeepSeek's entire approach -- MLA, efficient MoE routing, aggressive quantization -- was partly born from necessity. When you cannot just throw more H100s at the problem, you optimize harder.

  2. Open-source became a strategic choice. By open-sourcing their models, Chinese labs build global developer communities and adoption. This makes it politically harder to restrict the software side of AI, since millions of Western developers now depend on these models.

  3. The hardware bottleneck shifted the competition. Instead of a race to build the biggest model, it became a race to build the most efficient one. And efficiency benefits everyone.

What To Actually Worry About

If you are considering DeepSeek or Qwen for production use, here are the real concerns (not the paranoid ones):

Legitimate concerns:

  • Training data composition is less transparent than Western models
  • Certain topics may have built-in alignment biases reflecting Chinese regulations
  • Long-term support and update cadence is harder to predict
  • Documentation quality varies (though Qwen's is generally excellent)

Less legitimate concerns:

  • "The model will spy on me" -- Open-weight models run on YOUR hardware. There is no phone-home mechanism unless you build one.
  • "The code will be backdoored" -- Thousands of researchers have inspected these weights. The open-source community is very good at finding this kind of thing.
  • "China will revoke the license" -- Apache 2.0 is irrevocable. The weights you downloaded are yours.

The Practical Developer Stance

My recommendation: evaluate these models on their technical merits, just like you would any other tool. Run your own benchmarks on your own data. If Qwen2.5-72B produces better results for your multilingual chatbot than Llama 3.3, use Qwen. If DeepSeek-V3's reasoning is what your application needs, use DeepSeek.

The beauty of open-source is that you do not have to trust the company. You just have to trust the code.

Enterprise Adoption Patterns

The enterprise adoption of these models has followed a predictable pattern that is worth understanding:

Phase 1: Shadow AI (Early 2025)

Individual developers started using DeepSeek and Qwen models on their local machines. No formal approval, no procurement process. Just developers solving problems with the best tools available.

Phase 2: POC and Evaluation (Mid 2025)

Engineering teams ran formal evaluations. The results surprised a lot of people:

Enterprise Evaluation Results (Composite of Public Reports)
│
├── Task: Customer Support Classification
│   ├── GPT-4o:    94.2% accuracy, $0.038/request
│   ├── Qwen2.5-72B: 92.8% accuracy, $0.003/request (self-hosted)
│   └── Winner: Qwen (12.7x cheaper, 1.4% accuracy gap)
│
├── Task: Code Review Automation
│   ├── Claude 3.5: 89.1% useful feedback
│   ├── DeepSeek-V3: 87.3% useful feedback (self-hosted)
│   └── Winner: Depends on volume (DeepSeek wins above ~10K reviews/month)
│
├── Task: Document Summarization (Multilingual)
│   ├── GPT-4o:    88.5% quality score
│   ├── Qwen2.5-72B: 91.2% quality score
│   └── Winner: Qwen (better quality AND cheaper)
│
└── Task: Complex Reasoning / Analysis
    ├── Claude Opus: 96.1% accuracy
    ├── DeepSeek-R1: 93.8% accuracy
    └── Winner: Claude (but DeepSeek is closing the gap fast)

Phase 3: Production Deployment (Late 2025 - Now)

Enterprises are now running these models in production, but typically with a hybrid architecture:

// Common enterprise pattern: tiered model routing
interface ModelTier {
  name: string;
  model: string;
  costPer1kTokens: number;
  maxComplexity: 'low' | 'medium' | 'high' | 'frontier';
}

const modelTiers: ModelTier[] = [
  {
    name: 'edge',
    model: 'qwen2.5-7b-instruct',     // Self-hosted
    costPer1kTokens: 0.0001,
    maxComplexity: 'low'
  },
  {
    name: 'standard',
    model: 'qwen2.5-72b-instruct',    // Self-hosted
    costPer1kTokens: 0.001,
    maxComplexity: 'medium'
  },
  {
    name: 'advanced',
    model: 'deepseek-v3',              // Self-hosted or API
    costPer1kTokens: 0.005,
    maxComplexity: 'high'
  },
  {
    name: 'frontier',
    model: 'claude-3.5-sonnet',        // API
    costPer1kTokens: 0.015,
    maxComplexity: 'frontier'
  }
];

async function routeRequest(request: AIRequest): Promise<AIResponse> {
  const complexity = await classifyComplexity(request);
  const tier = modelTiers.find(t => t.maxComplexity >= complexity)
    || modelTiers[modelTiers.length - 1];

  console.log(`Routing to ${tier.name} tier (${tier.model})`);
  return await inference(tier.model, request);
}

This is the pattern I see most often in production: open-source models handle 80-90% of requests at a fraction of the cost, while frontier closed models handle the remaining edge cases where quality is non-negotiable.

What This Means for the AI Market

Pricing Pressure Is Real

OpenAI has cut GPT-4o pricing three times in the past year. That is not a coincidence. When a viable open-source alternative exists, the pricing power of closed-model providers evaporates. DeepSeek and Qwen are not just competitors -- they are price anchors that cap what anyone can charge for AI inference.

The "Good Enough" Threshold

For many production applications, we have crossed a critical threshold: open-source models are good enough. Not for everything, not for the hardest reasoning tasks, but for the bulk of real-world AI applications. Customer support bots, code assistance, document processing, translation, summarization -- Qwen and DeepSeek handle these at a quality level that would have been considered state-of-the-art eighteen months ago.

The Next Twelve Months

Here is where I think this is heading:

2026 AI Market Predictions
│
├── Q1 2026 (Now)
│   ├── DeepSeek + Qwen at ~15% combined share
│   ├── Enterprise adoption accelerating
│   └── OpenAI responds with aggressive pricing
│
├── Q2-Q3 2026
│   ├── Combined share reaches 20-25%
│   ├── Major cloud providers offer managed DeepSeek/Qwen
│   ├── Fine-tuned variants flood Hugging Face
│   └── First Fortune 500 companies go public about adoption
│
└── Q4 2026
    ├── Open-source total (including Llama, Mistral) hits 45-50%
    ├── Closed-model providers pivot to enterprise services
    ├── Hybrid architectures become the default
    └── The "which model" question becomes "which models"

My Honest Take

I have been doing this long enough to be skeptical of hype cycles. So here is my honest take, without the breathless "open source is going to kill OpenAI" energy that I see on Twitter:

DeepSeek and Qwen are legitimately impressive. Not because they are perfect, but because they proved that open-source AI can compete at the frontier. A year ago, using an open-source model meant accepting significantly worse quality. Today, the gap is small enough that cost, privacy, and control considerations tip the balance for many use cases.

This is not the end of closed models. Claude, GPT, and Gemini still lead on the hardest tasks. Their safety research, RLHF pipelines, and alignment work are more mature. For applications where getting it wrong has serious consequences -- medical advice, legal analysis, safety-critical systems -- I would still reach for a frontier closed model with strong safety guarantees.

The real winner is developers. A year ago, you had maybe three serious options for building AI-powered applications. Today, you have a dozen, across a spectrum of cost, quality, and deployment flexibility. That is unambiguously good, regardless of where the models come from.

Resources

Looking to integrate open-source AI models into your product or enterprise infrastructure? Whether it is model selection, deployment architecture, or fine-tuning strategy, we have done this before. Contact CODERCOPS to discuss your use case.

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