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Available now on ModelsLab · Language Model

Qwen: Qwen3.5-35B-A3B35B Parameters. 3B Active.

Try Qwen: Qwen3.5-35B-A3BAPI Documentation

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
API Documentation 
import requests


response = requests.post(
    "https://modelslab.com/api/v7/llm/chat/completions",
    json={
  "key": "YOUR_API_KEY",
  "prompt": "",
  "model_id": ""
}
)
print(response.json())

FAQ

Common questions about Qwen: Qwen3.5-35B-A3B

Read the docs

It uses a sparse Mixture-of-Experts architecture that activates only 3B of 35B parameters per token. This design outperforms previous 235B models while requiring 8GB GPU memory, delivering superior efficiency without sacrificing reasoning or coding performance.

Yes. It's a native multimodal model with unified vision-language capabilities. It processes text, images, and documents within a 256K token context window, extensible to 1M tokens for complex multi-step workflows.

The model covers 201 languages and dialects with nuanced cultural understanding. This enables inclusive deployment across global markets without separate language-specific models.

It scores 61.6 on Terminal-Bench 2.0, surpassing Claude 4.5 Opus (59.3), and 78.8 on SWE-bench Verified. It also leads on MCPMark (48.2%) for tool-calling reliability in agentic workflows.

With 4-bit quantization, it runs on 8GB GPU VRAM or 22GB Mac M-series. It supports bf16 and 4-bit quantization formats for flexible deployment across edge and consumer hardware.

Yes. It's available under Apache 2.0 license with open weights, enabling full customization and deployment without licensing restrictions.

Ready to create?

Start generating with Qwen: Qwen3.5-35B-A3B on ModelsLab.

Try Qwen: Qwen3.5-35B-A3BAPI Documentation