Qwen: Qwen3.5-35B-A3B
35B Parameters. 3B Active.
Efficiency Meets Multimodal Power
Sparse Architecture
3B Active Parameters
Only 3B of 35B activate per token, outperforming 235B models with minimal compute overhead.
Native Multimodal
Text, Vision, Documents
Unified vision-language foundation handles images, documents, and text in single inference pass.
Massive Context
256K Native Context
Process entire documents and conversations natively, extensible to 1M tokens for complex workflows.
Examples
See what Qwen: Qwen3.5-35B-A3B can create
Copy any prompt below and try it yourself in the playground.
Code Analysis
“Analyze this Python function for performance bottlenecks and suggest optimizations using vectorization and caching strategies.”
Document Summarization
“Extract key findings, methodology, and conclusions from this research paper into a structured summary.”
Visual Reasoning
“Describe the architectural elements and design principles visible in this building photograph.”
Multilingual Translation
“Translate this technical documentation from English to Mandarin, preserving formatting and technical terminology accuracy.”
For Developers
A few lines of code.
Efficient inference. Massive context.
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: Qwen3.5-35B-A3B on ModelsLab.