Very High Reasoning

Qwen3.5-9B

Qwen3.5-9B is Alibaba's powerful 9B-parameter open-source multimodal AI model, excelling in text, image, and video reasoning with a massive 262K native context window extensible to 1M+ tokens across 201 languages. Featuring native tool calling, always-on thinking mode, and hybrid architecture for efficient inference, it outperforms larger models on benchmarks like MathVision and MMMLU, perfect for agents, coding, and global applications.

262k Context

Very High Intelligence

Not specified Knowledge

Available for Chat, Vision, and File Uploads.

Performance Benchmarks

MMLU-Pro

82.5%

GPQA

81.7%

IFEval

91.5%

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Why use Qwen3.5-9B?

Multimodal Reasoning

Unified text, image, and video understanding with top benchmarks like 89.2% OCRBench and 84.5% VideoMME

Native Tool Calling

Production-ready function calling excelling at 66.1% BFCL-V4 and 79.1% TAU2-Bench for agent workflows

Long Context Window

262,144 native tokens extensible to 1M+ for processing extensive documents

Capability Examples

Reasoning Challenge

Solve this logic puzzle: There are 3 switches, one controls a light bulb in another room. One does nothing, one turns it on. Labels are all wrong. What switch do you flip first to determine each?

First, flip the switch labeled "ON". If the light is on, it's the "OFF" switch (since labels wrong). If off, flip the "OFF" labeled one next: if light on now, it was "ON"; else the last is "ON".

Multimodal Tool Use

Analyze this image of a flowchart [imagine flowchart here] and generate Python code to implement it as a function that takes user input for variables A and B, calling a math API if needed.

python def flowchart_logic(A, B): if A > B: return math.sqrt(A - B) # API call simulated else: return A B The flowchart shows if-else with sqrt on right branch, mult on left; handles OCR-detected nodes accurately.

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