Key takeaways

• Qwen3.5 9B is a multimodal foundation model designed for strong reasoning, coding, and visual understanding, seamlessly integrating and reasoning across text and image modalities for versatile AI tasks.
• It utilizes a Transformer-based architecture with 9 billion parameters, featuring a unified vision-language design with early fusion of multimodal tokens for integrated text and image processing.
• The model demonstrates strong mathematical problem-solving skills, achieving 98.60% accuracy on the MATH benchmark (subset) with detailed step-by-step reasoning, excelling in algebraic manipulations, calculus, and number theory.
• However, Qwen3.5 9B struggles with advanced mathematical problems, showing 60% accuracy on the AIME 2025 benchmark, especially with nuanced combinatorial counting, complex modular arithmetic, and abstract algebra. A significant challenge was its inability to provide solutions for 6 out of 15 AIME problems.
• Minor discrepancies in scoring for the MATH benchmark were often due to strict output formatting rules rather than fundamental mathematical errors, and it struggled with complex geometric interpretations in some cases.

Technical specifications

Context length

262k tokens

Max output

—

Architecture

Transformer

Modalities

text+image->text

Parameters

~9B

Open weights

Yes

Licence

Apache 2.0

Regions

China

Vendor

Qwen

Provider model key

qwen/qwen3.5-9b