Gemini 3.1 Pro vs Qwen3 VL 235B A22B Instruct

Compare Gemini 3.1 Pro and Qwen3 VL 235B A22B Instruct 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 VL 235B A22B Instruct
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Models in this comparison

Gemini 3.1 Pro vs Qwen3 VL 235B A22B Instruct: 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 VL 235B A22B Instruct

Qwen3 VL 235B A22B Instruct is a flagship multimodal vision-language model developed by Qwen (Alibaba Cloud), designed for instruction-following tasks that combine advanced text generation with visual understanding. It serves as a high-end open-weight model for developers and researchers building multimodal AI systems that require strong reasoning, perception, and long-context capabilities.

The model supports interleaved text and image inputs, very long context windows (up to roughly 256K tokens), and efficient inference through a mixture-of-experts architecture with about 22B active parameters out of 235B total. In today’s landscape, it competes with top-tier proprietary vision-language models while offering the advantages of open weights and flexible deployment. Typical applications include multimodal assistants, document and image analysis, visual reasoning, and large-context instruction-based workflows.

Gemini 3.1 Pro vs Qwen3 VL 235B A22B Instruct Comparison Table

PropertyGemini 3.1 ProQwen3 VL 235B A22B Instruct
OrganizationGoogleQwen
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateFeb 2026Sep 2025
Context Window1.0M256K
Parameters235B
LicenseProprietaryApache 2.0
Pricing per 1M tokens
Input $/1M$2.00$0.200
Output $/1M$12.00$0.880
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%
Avg Response Time6.13s
Median input tokensincl. image tokens1.1K
Median output tokens11
Est. cost / taskon this benchmark$0.0024
Defect Detection
73.3%(11/15)
Document Understanding
88.9%(8/9)
Object Counting
44.4%(4/9)
Object Understanding
92.9%(13/14)
Spatial Understanding
73.7%(14/19)

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