Qwen: Qwen3.5-Flash
Flash Reasoning, Million Tokens
Run Qwen3.5-Flash Efficiently
1M Context
Hybrid Attention Scales
Gated DeltaNet plus MoE handles 1M tokens with linear compute via 3:1 linear-to-full ratio.
MoE Architecture
Sparse Experts Accelerate
3B active params in Qwen: Qwen3.5-Flash beat larger predecessors on reasoning benchmarks.
Vision Native
Multimodal Flash Tasks
Processes text, images, video with early fusion for document parsing and UI navigation.
Examples
See what Qwen: Qwen3.5-Flash can create
Copy any prompt below and try it yourself in the playground.
Code Review
“Review this Python function for efficiency and suggest optimizations: def fibonacci(n): if n <= 1: return n else: return fibonacci(n-1) + fibonacci(n-2)”
JSON Schema
“Generate a JSON schema for a user profile with fields: name (string), age (integer 0-120), email (string format), preferences (array of strings). Include validation rules.”
SQL Query
“Write an optimized SQL query to find top 10 customers by total spend from orders table joined with customers, grouped by customer_id, last 12 months.”
API Design
“Design REST API endpoints for task management app: create task, list tasks, update task status, delete task. Specify HTTP methods, paths, request/response JSON.”
For Developers
A few lines of code.
Flash inference. One call.
ModelsLab handles the infrastructure: fast inference, auto-scaling, and a developer-friendly API. No GPU management needed.
- Serverless: scales to zero, scales to millions
- Pay per token, no minimums
- Python and JavaScript SDKs, plus REST API
import requestsresponse = requests.post("https://modelslab.com/api/v7/llm/chat/completions",json={"key": "YOUR_API_KEY","prompt": "","model_id": ""})print(response.json())
Ready to create?
Start generating with Qwen: Qwen3.5-Flash on ModelsLab.