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Available now on ModelsLab · Language Model

Qwen: Qwen3.5-9BReasoning. Coding. Multimodal.

Try Qwen: Qwen3.5-9BAPI Documentation

Build Smarter Agents. Faster.

Native Reasoning

Chain-of-Thought Before Response

Generates explicit reasoning traces for improved accuracy on complex reasoning and coding tasks.

Production Tool Calling

Function Calling Built In

Native function calling with 66.1% BFCL-V4 score enables reliable multi-agent workflows and autonomous task orchestration.

Massive Context Window

262K Native, 1M Extensible

Process long documents and complex workflows natively, scalable to 1M tokens with RoPE scaling.

Examples

See what Qwen: Qwen3.5-9B can create

Copy any prompt below and try it yourself in the playground.

API Integration Agent

“You are a backend engineer. Write a Python function that integrates with a REST API, handles authentication, retries on failure, and logs all requests. Include error handling for rate limits and network timeouts.”

Document Analysis

“Analyze this 50-page technical specification document. Extract all API endpoints, their parameters, response formats, and authentication requirements. Organize findings in a structured JSON format.”

Multi-Step Workflow

“Create a workflow that: 1) queries a database for user records, 2) validates email addresses, 3) sends notifications via webhook, 4) logs results. Include error recovery at each step.”

Code Review Agent

“Review this TypeScript code for security vulnerabilities, performance issues, and best practices. Provide specific line-by-line feedback with refactoring suggestions.”

For Developers

A few lines of code.
Reasoning model. Three lines.

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: Qwen3.5-9B

Read the docs

Qwen3.5-9B combines hybrid Gated DeltaNet and Gated Attention architecture with native multimodal reasoning, function calling, and always-on chain-of-thought. It outperforms larger models like GPT-3.5 on coding and reasoning benchmarks while maintaining 9B efficiency.

Yes. Native function calling with 66.1% BFCL-V4 score enables production-ready tool use, external API calls, and multi-agent workflows. Use the preserve_thinking parameter to retain reasoning across multi-turn agent loops.

Qwen3.5-9B has 262K native context, extensible to 1M tokens with RoPE scaling. This enables long-document analysis, complex workflows, and extended multi-turn conversations without performance degradation.

Yes. It's a full vision-language model supporting text, images, and video inputs within a unified interface. Vision capabilities include 89.2% OCRBench, 84.5% VideoMME, and 78.9% MathVision scores.

Qwen3.5-9B supports 201 languages with 81.2% MMMLU coverage, making it suitable for multilingual chatbots, customer support, and global applications.

On Mac mini M4 with Q4_K_M quantization, token generation averages 35 tokens/sec with ~800ms initial processing and 1.2 seconds per subsequent turn. API response times are comparable to GPT-3.5 Turbo.

Ready to create?

Start generating with Qwen: Qwen3.5-9B on ModelsLab.

Try Qwen: Qwen3.5-9BAPI Documentation