Best Qwen Models in 2026: Complete Comparison

Best Qwen Models in 2026: Complete Comparison

Alibaba's Qwen (pronounced "chwen") model family has become one of the most capable and widely deployed open-source LLM families in the world. From the massive Qwen 3 flagship to tiny 0.5B models that run on a phone, the Qwen ecosystem covers virtually every use case.

But with so many variants available, choosing the right Qwen model for your project can be overwhelming. This guide breaks down every major Qwen model, compares their benchmarks, and gives clear recommendations based on what you are building.

The Qwen Model Family at a Glance

Model Family	Type	Sizes Available	License	Best For
Qwen 3	Text LLM	0.6B, 1.7B, 4B, 8B, 14B, 32B, 30B-A3B, 235B-A22B	Apache 2.0	General text, reasoning, coding
Qwen 2.5	Text LLM	0.5B, 1.5B, 3B, 7B, 14B, 32B, 72B	Apache 2.0	Production workloads, fine-tuning
Qwen 2.5-Coder	Code LLM	0.5B, 1.5B, 3B, 7B, 14B, 32B	Apache 2.0	Code generation, completion
Qwen 2.5-Math	Math LLM	1.5B, 7B, 72B	Apache 2.0	Mathematical reasoning
Qwen-VL (Qwen2.5-VL)	Vision-Language	3B, 7B, 72B	Apache 2.0	Image understanding, OCR
Qwen2-Audio	Audio LLM	7B	Apache 2.0	Speech recognition, audio QA
Qwen-Agent	Agent Framework	N/A	Apache 2.0	Tool use, agentic workflows
QwQ	Reasoning	32B	Apache 2.0	Deep reasoning, chain-of-thought

Qwen 3: The Latest Flagship

Qwen 3 represents a major leap, introducing both dense and Mixture-of-Experts (MoE) architectures along with a hybrid thinking mode.

Dense Models:

Model	Parameters	Context Length	Key Strength
Qwen3-0.6B	0.6B	32K	Edge/mobile deployment
Qwen3-1.7B	1.7B	32K	Lightweight local inference
Qwen3-4B	4B	32K	Balance of speed and capability
Qwen3-8B	8B	128K	Sweet spot for most tasks
Qwen3-14B	14B	128K	Strong coding and reasoning
Qwen3-32B	32B	128K	Near-frontier performance

MoE Models:

Model	Total Params	Active Params	Context Length	Key Strength
Qwen3-30B-A3B	30B	3B	128K	Efficient inference, mobile-friendly
Qwen3-235B-A22B	235B	22B	128K	Flagship, competes with GPT-4o

The MoE models are particularly noteworthy. Qwen3-235B-A22B has 235 billion total parameters but only activates 22 billion per token, making it far more efficient than a dense model of the same size.

Qwen 3 Hybrid Thinking Mode:

Qwen 3 supports switching between "thinking" and "non-thinking" modes within a single model:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Qwen/Qwen3-8B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

# Enable thinking mode for complex problems
messages = [
    {"role": "user", "content": "Prove that there are infinitely many prime numbers."}
]

# With thinking enabled (uses /think tag)
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True  # Activates extended reasoning
)

Using Ollama for local deployment:

# Pull and run Qwen 3 8B
ollama pull qwen3:8b
ollama run qwen3:8b

# For the MoE model
ollama pull qwen3:30b-a3b
ollama run qwen3:30b-a3b

Qwen 2.5: The Production Workhorse

While Qwen 3 is the latest, Qwen 2.5 remains the most battle-tested family for production deployments. It has been thoroughly benchmarked, fine-tuned by the community, and optimized across inference frameworks.

Model	MMLU	HumanEval	GSM8K	Best Use
Qwen2.5-7B	74.2	75.6	85.4	General-purpose, good local model
Qwen2.5-14B	79.9	80.5	89.2	Strong all-rounder
Qwen2.5-32B	83.3	84.1	91.7	High-quality reasoning
Qwen2.5-72B	86.1	86.6	95.2	Best open-source at release

For running Qwen 2.5 locally with vLLM (optimized serving):

pip install vllm

# Serve the model
python -m vllm.entrypoints.openai.api_server \
  --model Qwen/Qwen2.5-7B-Instruct \
  --port 8000

# Query it (OpenAI-compatible API)
curl http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Qwen/Qwen2.5-7B-Instruct",
    "messages": [{"role": "user", "content": "Explain quicksort"}],
    "temperature": 0.7
  }'

Qwen 2.5-Coder: Purpose-Built for Code

If your primary use case is code generation, completion, or analysis, the Coder variants outperform the general-purpose models on programming tasks.

Model	HumanEval	MBPP	MultiPL-E	LiveCodeBench
Qwen2.5-Coder-7B	83.5	78.2	71.4	68.3
Qwen2.5-Coder-14B	87.2	82.1	76.8	73.1
Qwen2.5-Coder-32B	90.1	85.6	80.3	78.9

Use Qwen2.5-Coder in VS Code with Continue or other extensions:

{
  "models": [
    {
      "title": "Qwen Coder",
      "provider": "ollama",
      "model": "qwen2.5-coder:14b",
      "apiBase": "http://localhost:11434"
    }
  ],
  "tabAutocompleteModel": {
    "title": "Qwen Coder Autocomplete",
    "provider": "ollama",
    "model": "qwen2.5-coder:7b"
  }
}

QwQ: The Reasoning Specialist

QwQ (Qwen with Questions) is Alibaba's reasoning-focused model, comparable to OpenAI's o1 series. It generates explicit chain-of-thought reasoning before arriving at answers.

# Run QwQ locally
ollama pull qwq:32b
ollama run qwq:32b

QwQ excels at:

• Mathematical problem solving
• Logic puzzles and formal reasoning
• Code debugging (finding subtle bugs)
• Scientific analysis

# QwQ thinking process example:
User: "Is 1729 a special number?"

QwQ Internal Reasoning:
  -> Let me think about what makes 1729 special...
  -> It is known as the Hardy-Ramanujan number
  -> It is the smallest number expressible as the sum of two cubes in two ways:
  -> 1729 = 1³ + 12³ = 9³ + 10³
  -> Let me verify: 1 + 1728 = 1729 ✓
  -> 729 + 1000 = 1729 ✓

Final Answer: "Yes, 1729 is the Hardy-Ramanujan number..."

Qwen2.5-VL: Vision-Language Models

For tasks involving images, charts, documents, and screenshots, Qwen2.5-VL is the go-to choice.

Capability	Qwen2.5-VL-3B	Qwen2.5-VL-7B	Qwen2.5-VL-72B
Image understanding	Good	Very Good	Excellent
OCR accuracy	85%+	92%+	97%+
Chart/graph analysis	Basic	Good	Excellent
Document parsing	Good	Very Good	Excellent
Video understanding	Limited	Good	Very Good

from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor

model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    "Qwen/Qwen2.5-VL-7B-Instruct",
    device_map="auto"
)
processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct")

messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": "https://example.com/chart.png"},
            {"type": "text", "text": "Analyze this chart and summarize the key trends."}
        ]
    }
]

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

Which Qwen Model Should You Use?

Here is a decision tree based on your use case:

Your Use Case	Recommended Model	Why
General chatbot	Qwen3-8B or Qwen3-32B	Latest architecture, hybrid thinking
Code generation	Qwen2.5-Coder-32B	Best open-source coding model
Code autocomplete	Qwen2.5-Coder-7B	Fast enough for real-time completion
Math/reasoning	QwQ-32B	Purpose-built for reasoning
Image understanding	Qwen2.5-VL-72B	Best open-source VL model
Edge/mobile deployment	Qwen3-0.6B or Qwen3-30B-A3B	Tiny footprint, decent quality
Production API server	Qwen2.5-72B-Instruct	Most stable, well-optimized
Fine-tuning base	Qwen2.5-7B or 14B	Great balance of capability and trainability
RAG applications	Qwen2.5-32B-Instruct	Strong instruction following, long context
Budget deployment	Qwen3-30B-A3B (MoE)	235B quality at 3B active cost

VRAM Requirements

Model	FP16	INT8	INT4 (GPTQ/AWQ)
Qwen3-8B	16 GB	8 GB	5 GB
Qwen3-14B	28 GB	14 GB	8 GB
Qwen3-32B	64 GB	32 GB	18 GB
Qwen3-30B-A3B (MoE)	~60 GB	~30 GB	~18 GB
Qwen2.5-72B	144 GB	72 GB	40 GB
Qwen2.5-Coder-32B	64 GB	32 GB	18 GB

Running Qwen Models via API

If you do not have the hardware to run Qwen locally, several platforms offer Qwen models via API:

# Via Together AI
curl https://api.together.xyz/v1/chat/completions \
  -H "Authorization: Bearer YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Qwen/Qwen2.5-72B-Instruct",
    "messages": [{"role": "user", "content": "Hello"}]
  }'

# Via Ollama (local)
curl http://localhost:11434/api/chat \
  -d '{
    "model": "qwen3:8b",
    "messages": [{"role": "user", "content": "Hello"}]
  }'

Wrapping Up

The Qwen model family is one of the most comprehensive open-source AI ecosystems available in 2026. Whether you need a tiny model for edge deployment, a coding specialist, a reasoning engine, or a frontier-class general model, there is a Qwen variant that fits.

For production applications that combine LLM capabilities with media generation (images, video, audio, and more), Hypereal AI offers unified API access to both language models and creative AI models, letting you build complete AI workflows without managing multiple providers.