Gemini 3.1 Pro vs Qwen3.5 9b

Compare Gemini 3.1 Pro and Qwen3.5 9b side-by-side. See how these vision models stack up in Image Captioning, Open Prompt, and OCR.

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GoogleGemini 3.1 Pro
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QwenQwen3.5 9b
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Gemini 3.1 Pro vs Qwen3.5 9b: Overview

Gemini 3.1 Pro

Gemini 3.1 Pro is a proprietary multimodal model from Google’s Gemini 3 series, released in early 2026 and designed for advanced reasoning across large multimodal datasets. It accepts text, images, audio, video, and documents, supporting up to a 1-million-token input context with up to 64k output tokens. Compared with Gemini 3 Pro, it improves long-context synthesis and multi-step reasoning, enabling more reliable analysis of large documents, datasets, and software codebases.

The model also advances visual understanding and grounding, allowing it to interpret UI screenshots, diagrams, and real-world scenes while referencing specific regions within images or video. These capabilities make Gemini 3.1 Pro well suited for multimodal workflows involving document processing, interface analysis, robotics research, and complex visual reasoning.

Qwen3.5 9b

Qwen3.5-9B is a 9-billion-parameter multimodal foundation model developed by Alibaba Cloud's Qwen team, released on March 2, 2026 as part of the Qwen3.5 model family. Designed for efficient multimodal reasoning and long-context language tasks, it notably outperforms the older Qwen3-30B, a model more than three times its size, on key benchmarks including GPQA Diamond, IFEval, and LongBench.

The model supports vision-language inputs through an early-fusion multimodal architecture built on a dense hybrid foundation of Gated Delta Networks and Gated Attention. It can also operate in a text-only mode by skipping the vision encoder during inference. It provides a 262,144-token context window (extensible to ~1M tokens via YaRN) and is released under the Apache License 2.0. Within the current AI landscape, Qwen3.5-9B offers a strong balance of capability and efficiency, making it well-suited for multimodal assistants, document analysis, long-context reasoning, and developer-deployed agentic systems.

Gemini 3.1 Pro vs Qwen3.5 9b Comparison Table

PropertyGemini 3.1 ProQwen3.5 9b
OrganizationGoogleQwen
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateFeb 2026Mar 2026
Context Window1.0M262K
Parameters9B
LicenseProprietaryApache 2.0
Pricing per 1M tokens
Input $/1M$2.00$0.100
Output $/1M$12.00$0.150
Vision Tasks
CaptioningDemoDemo
Object DetectionDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
ClassificationDemo
Model Features
LLMs with Vision Capabilities
Multimodal Vision
Foundation Vision
Vision Evalspass/fail results · 66 prompts
Score key:≥75%40–74%<40%
Overall Score
75.76%
71.64%
Avg Response Time6.13s8.99s
Median input tokensincl. image tokens1.1K
Median output tokens11
Est. cost / taskon this benchmark$0.0024
Defect Detection
73.3%(11/15)
86.7%(13/15)
Document Understanding
88.9%(8/9)
66.7%(6/9)
Object Counting
44.4%(4/9)
30%(3/10)
Object Understanding
92.9%(13/14)
71.4%(10/14)
Spatial Understanding
73.7%(14/19)
84.2%(16/19)

Output tokens (incl. reasoning) and est. cost / task are measured on this benchmark from a single low-temperature run, and shown only for models whose run covered at least 90% of prompts. Methodology