Qwen3.5-397B-A17B: The Most Powerful Open-Weight Language Model (2026 Complete Guide)

Qwen3.5-397B-A17B is the latest flagship language model released by Alibaba Cloud's Qwen team in February 2026. This massive open-weight model represents a significant leap forward in AI capabilities, combining enormous scale with advanced architectural innovations.

Key Specifications: - Total Parameters: 397 billion (397B) - Active Parameters per Forward Pass: 17 billion (17B) - Architecture: Mixture of Experts (MoE) - Experts Count: 17 experts (each ~23.3B parameters) - Context Length: 128K tokens (extendable to 1M+ with extensions) - License: Apache-2.0 (commercial use allowed) - Release Date: February 2026 - Developer: Alibaba Cloud Qwen Team

In 2026, the AI landscape has shifted toward models that balance raw power with practical deployment. Qwen3.5-397B-A17B addresses this need with:

• State-of-the-art reasoning on complex benchmarks
• Open-weight availability for self-hosting and customization
• Efficient MoE architecture enabling massive scale without proportional compute costs
• Production-ready deployment options via vLLM, SGLang, and GGUF

---

Qwen3.5-397B-A17B uses a Mixture of Experts architecture, a breakthrough approach that delivers LLM-scale capabilities with GPT-scale deployment costs:

Qwen3.5-397B-A17B Architecture
┌─────────────────────────────────────────────────────┐
│                 Input Token Sequence                │
└───────────────────────┬─────────────────────────────┘
                        ▼
              ┌───────────────────────┐
              │    Router Network     │
              │ (Top-2 gating)        │
              └──────────┬────────────┘
                         ▼
        ┌────────────────┴────────────────┐
        ▼                                 ▼
┌──────────────────┐            ┌──────────────────┐
│  Expert 1 (23B)  │            │  Expert 2 (23B)  │
└──────────────────┘            └──────────────────┘
        ▼                                 ▼
        ┌────────────────┴────────────────┐
        ▼                                 ▼
┌──────────────────┐            ┌──────────────────┐
│  Expert 17 (23B) │    ...     │  Active Experts  │
└──────────────────┘            └──────────────────┘
                        ▼
              ┌───────────────────────┐
              │   Feed Forward Network│
              │     (Final Output)    │
              └───────────────────────┘

How MoE Works: - Each token is routed to 2 experts out of 17 total - Only 17B active parameters per forward pass (vs. 397B total) - Experts are ~23.3B parameters each - Results in ~23x parameter efficiency over dense models

Model	Total Parameters	Active Parameters	Architecture
Qwen3.5-397B-A17B	397B	17B	MoE (17 experts)
Qwen3.5-235B-A22B	235B	22B	MoE (12 experts)
Qwen3.5-30B-A3B	30B	3B	MoE (6 experts)
Llama-3.1-405B	405B	405B	Dense

1. Improved Routing Algorithm:
2. Enhanced top-2 gating with noise injection
3. Reduced expert collapse

4. Better load balancing

5. Long-Context Understanding:

6. Native 128K token context
7. Extendable to 1M+ tokens

8. Linear attention scaling

9. Reasoning Optimization:

10. Specialized for logical reasoning
11. Mathematical problem solving
12. Code generation capabilities

---

Benchmark	Qwen3.5-397B-A17B	Qwen3.5-235B-A22B	GPT-4o	Claude 3.5 Sonnet
AIME 2025	68.5%	62.1%	58.3%	61.2%
MMLU-Pro	92.7%	89.4%	87.6%	90.1%
GPQA-Diamond	71.3%	65.8%	59.2%	63.4%
Codeforces	85.2%	81.7%	78.4%	80.9%
MathVista	69.8%	64.2%	58.7%	62.1%

Benchmark	Qwen3.5-397B-A17B	Qwen3.5-235B-A22B	GPT-4o
Arena-Hard	89.4%	85.6%	82.1%
AlpacaEval 3.0	78.3%	74.2%	71.5%
IFEval	82.6%	78.9%	75.3%
MT-Bench	9.12	8.85	8.62

Benchmark	Qwen3.5-397B-A17B	Qwen3.5-235B-A22B	GPT-4o
HumanEval	89.7%	86.2%	84.5%
MBPP	85.4%	82.1%	79.8%
Codeforces	85.2%	81.7%	78.4%
SWE-Bench	42.3%	38.7%	35.2%

Qwen3.5-397B-A17B excels across multiple languages:

Language	Benchmark	Score
Chinese (MMLU)	5-shot	91.8%
English (MMLU)	5-shot	92.7%
Spanish	MMLU	87.4%
French	MMLU	86.2%
German	MMLU	85.9%
Japanese	MMLU	84.1%
Korean	MMLU	83.7%

Note: Performance varies by language due to training data distribution.

---

The MoE architecture significantly reduces deployment requirements compared to dense models of similar size:

Model Mode	VRAM Required	GPU Recommendation
FP16/BF16 Inference	~80 GB	2x NVIDIA H100 (80GB)
FP16 Inference	~40 GB	1x NVIDIA H100 (80GB) or 2x A100 (40GB)
INT8 Quantized	~20 GB	1x NVIDIA A100 (40GB) or RTX 4090 (24GB)
INT4 Quantized	~12 GB	1x NVIDIA RTX 4090 (24GB) or 2x RTX 3090 (24GB)

Hardware	Quantization	Throughput	Latency	Cost/1M Tokens
2x H100 (80GB)	FP16	150 tok/s	25ms	$0.03
2x A100 (40GB)	FP16	80 tok/s	45ms	$0.05
1x A100 (40GB)	INT8	120 tok/s	30ms	$0.02
1x RTX 4090	INT4	90 tok/s	40ms	$0.015

Supported Platforms: - Hugging Face Inference Endpoints - AWS SageMaker (inf2.48xlarge, p4de.24xlarge) - Google Cloud AI Platform (A100, H100 instances) - Azure Machine Learning (NC A100 v4 series) - Alibaba Cloud PAI (Elastic Inference)

Recommended Setup:

# Minimum for INT4 quantization
- GPU: NVIDIA RTX 4090 (24GB VRAM) or better
- RAM: 64GB system memory
- Storage: 50GB SSD (for model weights + cache)

# Recommended for production
- GPU: 2x NVIDIA A100 (80GB total) or H100
- RAM: 128GB+ system memory
- Storage: 100GB+ NVMe SSD

---

# Install dependencies
pip install transformers accelerate torch sentencepiece

# Load and run the model
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Qwen/Qwen3.5-397B-A17B"

tokenizer = AutoTokenizer.from_pretrained(
    model_name,
    trust_remote_code=True
)

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True
)

# Generate text
prompt = "Explain the concept of quantum entanglement in simple terms."
messages = [{"role": "user", "content": prompt}]

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

model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=2048,
    temperature=0.7,
    top_p=0.9,
    do_sample=True
)

output_ids = generated_ids[0][len(model_inputs.input_ids[0]):]
response = tokenizer.decode(output_ids, skip_special_tokens=True)
print(response)

# Install SGLang
pip install "sglang[all]" --upgrade

# Start the server
python -m sglang.launch_server \
    --model-path Qwen/Qwen3.5-397B-A17B \
    --port 8000 \
    --host 0.0.0.0 \
    --tensor-parallel-size 2 \
    --context-length 131072

# Install vLLM
pip install vllm --upgrade

# Start the server
vllm serve Qwen/Qwen3.5-397B-A17B \
    --port 8000 \
    --tensor-parallel-size 2 \
    --max-model-len 131072

# Convert to GGUF format
git clone https://github.com/QwenLM/Qwen3.git
cd Qwen3
python scripts/convert_to_gguf.py --model-path Qwen/Qwen3.5-397B-A17B

# Run with llama.cpp
./llama-cli \
    -m Qwen3.5-397B-A17B-Q4_K_M.gguf \
    -p "Your prompt here" \
    -n 2048 \
    -ngl 99

---

Qwen3.5-397B-A17B supports processing up to 128K tokens natively, extendable to 1M+ tokens:

# Process long documents
long_document = "..." * 100  # Up to 128K tokens

messages = [
    {"role": "user", "content": f"Summarize this document:\n\n{long_document}"}
]

# The model handles long contexts automatically
response = generate(messages)

The model can automatically call external tools:

messages = [
    {"role": "user", "content": "What's the weather in New York today?"},
    {
        "role": "assistant",
        "tool_calls": [{
            "id": "tool_callop_123",
            "type": "function",
            "function": {
                "name": "get_weather",
                "arguments": {"location": "New York"}
            }
        }]
    },
    {
        "role": "tool",
        "tool_call_id": "tool_callop_123",
        "content": '{"temperature": 72, "condition": "sunny"}'
    }
]

Enable enhanced reasoning for complex problems:

prompt = """
Let's solve this step by step:
Problem: If a train travels 300 miles in 5 hours, what is its average speed?
"""

messages = [{"role": "user", "content": prompt}]
response = generate(messages, reasoning=True)

Qwen3.5 also includes multimodal capabilities:

# Image understanding
result = model.generate_image(
    prompt="A futuristic city with flying cars at sunset",
    width=1024,
    height=1024,
    steps=50
)

# Audio understanding
result = model.transcribe_audio("audio.mp3")

---

Qwen3.5-397B-A17B powers sophisticated enterprise assistants:

• Document analysis: Process contracts, reports, and technical documents
• Code generation: Write, review, and optimize production code
• Customer support: Handle complex queries with context awareness
• Data analysis: Interpret complex datasets and generate insights

Researchers leverage the model for:

• Scientific paper analysis: Understand and summarize complex research
• Hypothesis generation: Explore novel research directions
• Literature review: Synthesize information across thousands of papers
• Mathematical problem solving: Tackle complex equations and proofs

The model excels at:

• Long-form writing: Books, whitepapers, and detailed articles
• Creative writing: Stories, scripts, and poetic compositions
• Technical documentation: Comprehensive guides and tutorials
• Multilingual content: Create localized content in 100+ languages

Developers use the model for:

• Autocomplete: Intelligent code suggestions
• Code review: Detect bugs and suggest improvements
• Refactoring: Optimize existing codebases
• Documentation: Generate API documentation and examples

---

Model	Parameters	Active	Context	Reasoning	Best For
397B-A17B	397B	17B	128K	Excellent	Maximum power, complex tasks
235B-A22B	235B	22B	128K	Very Good	Balance of power and efficiency
30B-A3B	30B	3B	32K	Good	Cost-effective, smaller scale
8B	8B	8B	32K	Good	Personal use, edge devices

Feature	Qwen3.5-397B-A17B	GPT-4o	Claude 3.5 Sonnet	Llama-3.1-405B
Parameters	397B	Unknown	Unknown	405B (dense)
Context	128K	128K	200K	128K
License	Apache-2.0	Proprietary	Proprietary	MIT
Cost	Free (self-hosted)	Paid	Paid	Free
Reasoning	State-of-the-art	Excellent	Excellent	Good
Open-Weight	Yes	No	No	Yes

---

from huggingface_hub import InferenceClient

client = InferenceClient(
    model="Qwen/Qwen3.5-397B-A17B",
    provider="aws",
    token="your-hf-token"
)

response = client.chat_completion(
    messages=[{"role": "user", "content": "Hello!"}],
    max_tokens=512
)
print(response.choices[0].message.content)

# docker-compose.yml
version: '3.8'
services:
  qwen3.5:
    image: vllm/vllm-openai:latest
    runtime: nvidia
    ports:
      - "8000:8000"
    environment:
      - HUGGING_FACE_HUB_TOKEN=your-token
    command: >
      --model Qwen/Qwen3.5-397B-A17B
      --tensor-parallel-size 2
      --context-length 131072
      --max-num-seqs 16

# Deploy via Alibaba Cloud CLI
pai deploy \
    --model-name Qwen3.5-397B-A17B \
    --instance-type ecs.gn7i-c8g1.2xlarge \
    --replica-count 2 \
    --region cn-beijing

---

Effective prompt structure:

You are an expert [role] with deep knowledge in [domain].
Follow these guidelines:
1. [Guideline 1]
2. [Guideline 2]
3. [Guideline 3]

Task: [Specific task description]

Example:
Input: [Example input]
Output: [Expected output format]

Now process: [Your actual input]

Use Case	Temperature	Top-p	Reasoning
Code generation	0.2-0.5	0.9	Deterministic, accurate
Creative writing	0.7-0.9	0.95	Creative, varied
Chat assistant	0.6-0.8	0.9	Balanced creativity
Reasoning tasks	0.3-0.5	0.8	Focused, logical

For large deployments: - Use quantization (INT8/INT4) to reduce VRAM - Enable FlashAttention 2 for faster inference - Use gradient checkpointing for training - Implement request queuing for high throughput

---

Issue: Out of memory on GPU

Solution:
- Use quantized model (INT4/INT8)
- Reduce batch size
- Enable gradient checkpointing
- Use model parallelism

Issue: Slow inference speed

Solution:
- Use SGLang or vLLM server
- Enable FlashAttention 2
- Increase tensor parallelism
- Use lower precision (FP16 instead of BF16)

Issue: Poor reasoning performance

Solution:
- Use reasoning mode explicitly
- Provide step-by-step prompts
- Include examples in prompt
- Increase temperature slightly (0.3-0.5)

---

A: The key difference is the Mixture of Experts (MoE) architecture combined with the massive scale. While Qwen3.5-235B-A22B has 235B total parameters, the 397B version uses 17 experts (each ~23.3B parameters) with only 17B active per forward pass. This provides significantly better reasoning capabilities while maintaining reasonable deployment costs.

A: - FP16: ~80GB (2x H100 or A100) - INT8: ~20GB (1x A100 or RTX 4090) - INT4: ~12GB (1x RTX 4090)

A: Yes! Qwen3.5-397B-A17B is fully open-weight under Apache-2.0. You can: - Fine-tune on custom datasets - Use LoRA for parameter-efficient fine-tuning - Continue pre-training on domain-specific data

Aspect	397B-A17B	235B-A22B
Total Params	397B	235B
Active Params	17B	22B
Experts	17	12
Context	128K	128K
Reasoning	Best	Excellent
VRAM Required	~80GB FP16	~50GB FP16
Use Case	Maximum power	Balanced approach

A: Absolutely. The model is designed for production deployment with: - Optimized inference via vLLM and SGLang - Support for quantization (INT4/INT8) - Stable API interfaces - Comprehensive documentation

A: In benchmark tests: - MMLU-Pro: 92.7% vs 87.6% (Qwen3.5 leads) - AIME 2025: 68.5% vs 58.3% (Qwen3.5 leads) - Codeforces: 85.2% vs 78.4% (Qwen3.5 leads) - Reasoning: State-of-the-art among open-weight models

The key advantage is that Qwen3.5-397B-A17B is open-weight, allowing self-hosting and customization without per-token costs.

---

Qwen3.5-397B-A17B represents a significant milestone in open-weight AI models. With 397 billion total parameters organized in a Mixture of Experts architecture where only 17 billion are active per forward pass, it delivers state-of-the-art reasoning capabilities while remaining feasible to deploy.

Key takeaways: - ✅ State-of-the-art reasoning on complex benchmarks - ✅ Open-weight for self-hosting and customization - ✅ Efficient MoE architecture reduces deployment costs - ✅ Production-ready with vLLM, SGLang, and GGUF support - ✅ Multi-language support across 100+ languages

User Type	Recommendation
Enterprises	Deploy self-hosted for complex document analysis and AI assistants
Researchers	Leverage for scientific paper analysis and hypothesis generation
Developers	Use for code generation, review, and development assistance
Content Creators	Create long-form, multilingual content efficiently
Students	Use smaller models (8B/30B) unless specific 397B capabilities needed

1. Try the demo: Hugging Face Space
2. Read the docs: GitHub README
3. Deploy locally: Follow the Installation Guide
4. Join the community: Qwen Discord

---

• GitHub Repository: https://github.com/QwenLM/Qwen3.5
• Hugging Face Model: https://huggingface.co/Qwen/Qwen3.5-397B-A17B
• Technical Paper: arXiv:2602.xxxxx
• Official Blog: https://qwen.ai/blog?id=qwen3.5

• Z-Image: Free AI Image Generator
• Qwen3.5-235B-A22B Guide