Qwen: Qwen3.5 Plus 2026-02-15
Multimodal Power, Million Tokens
Run Qwen3.5 Plus Efficiently
1M Context
Process Massive Inputs
Handle 1,000,000 tokens with text, image, video inputs via Qwen: Qwen3.5 Plus 2026-02-15 API.
Hybrid Architecture
Linear Attention MoE
Qwen qwen3 5 plus 2026 02 15 uses sparse experts for 19x faster long-context decoding.
Auto Reasoning
Adaptive Tool Use
Qwen: Qwen3.5 Plus 2026-02-15 LLM auto-activates search, code interpreter in Auto mode.
Examples
See what Qwen: Qwen3.5 Plus 2026-02-15 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)”
Math Proof
“Prove that the sum of the first n natural numbers is n(n+1)/2 using mathematical induction. Show all steps clearly.”
JSON Schema
“Generate a JSON schema for a user profile including name, email, age, and preferences array with validation rules.”
Logic Puzzle
“Three houses in a row, owned by Alice, Bob, Carl. Alice has a dog, Bob drinks tea, Carl lives in the middle. Dog owner drinks coffee. Who drinks milk?”
For Developers
A few lines of code.
Million tokens. 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 Plus 2026-02-15 on ModelsLab.