Qwen3.5 397B A17B
The hyper-efficient multimodal giant for reasoning, coding, and autonomous agents.
Total Parameters
397B
17B Activated
Context Window
262K
Tokens
Modalities
Text, Image
& Video
Speed
Up to 19x
Faster vs Qwen3-Max
Next-Generation Efficiency Meets Native Multimodal Reasoning
Qwen3.5 397B A17B is a state-of-the-art vision-language foundation model. By utilizing a highly efficient sparse Mixture-of-Experts (MoE) architecture, it activates only 17 billion of its 397 billion parameters per token. This allows it to deliver cross-generational parity with massive dense models at blazing-fast speeds. Featuring early fusion training across text, images, and video, Qwen3.5 397B A17B is purpose-built for real-world adaptability, complex coding tasks, and global deployment.
Mixture of Experts — 17B active / 397B total
Built for the hardest problems
Native Multimodal Workflows
Process text, high-resolution images, and videos simultaneously with early-fusion architecture. Perfect for UI element detection and visual document understanding.
Autonomous Coding Agents
Top-tier performance in agentic coding environments. Natively supports tool calling, executing complex CLI workflows, and recovering from execution failures.
Deep Reasoning (Thinking Mode)
Built-in "Thinking Mode" generates step-by-step internal logic before answering, dramatically increasing accuracy on complex math, science, and logic problems.
Global Multilingual Deployment
Trained for nuanced cultural understanding across 201 languages and dialects, making it the ideal foundation model for global enterprise applications.
Industry-Leading Intelligence
Proven performance across reasoning, coding, and agentic workflows.
Overall Intelligence
Better than 94% of models
Coding Capability
Better than 94% of models
Agentic Capability
Better than 95% of models
| Category | Benchmark | Score |
|---|---|---|
| Reasoning | GPQA Diamond | 89.3% |
| Reasoning | τ²-Bench Telecom | 95.6% |
| Reasoning | IFBench | 78.8% |
| Reasoning | AA-LCR | 65.7% |
| Reasoning | GDPval-AA | 35.7% |
| Reasoning | HLE | 27.3% |
| Reasoning | CritPt | 1.7% |
| Coding | SciCode | 42.0% |
| Coding | Terminal-Bench Hard | 40.9% |
| Knowledge | AA-Omniscience | 31.4% |
Metrics sourced from Artificial Analysis.
Flexible Pricing Tiers
Choose the optimal balance of speed and cost for your workflow. Prices are per 1M tokens.
| Tier | Input / 1M tokens | Output / 1M tokens |
|---|---|---|
| Standard | $0.15 | $1.20 |
| Async | $0.30 | $1.80 |
| Realtime | $0.60 | $3.60 |
Context window supported up to 256k tokens.
Start Building in Minutes
Qwen3.5 397B A17B is accessible via OpenAI-compatible endpoints. Here is how to integrate it using the standard Python SDK via Doubleword.ai.
from openai import OpenAI
client = OpenAI(
api_key="your-api-key-here",
base_url="https://api.doubleword.ai/v1"
)
# Step 1: Upload a batch input file
with open("batch_requests.jsonl", "rb") as file:
batch_file = client.files.create(
file=file,
purpose="batch"
)
print(f"File ID: {batch_file.id}")
# Step 2: Create a batch job
batch = client.batches.create(
input_file_id=batch_file.id,
endpoint="/v1/chat/completions",
completion_window="24h"
)
print(f"Batch ID: {batch.id}")
# Step 3: Check batch status
batch_status = client.batches.retrieve(batch.id)
print(f"Status: {batch_status.status}")