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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 on Vision Evals

Gemini 3.1 Pro scores higher on all six Vision Evals tasks.

The widest gap is Reasoning, where Gemini 3.1 Pro leads 72.2% to 29.8%.

Overall, Gemini 3.1 Pro averages 83.1% (#3 of 25) against 65.8% (#20 of 25) for Qwen3 VL 235B A22B Instruct.

Qwen3 VL 235B A22B Instruct is cheaper ($0.0007 vs $0.0093 per sample), while Gemini 3.1 Pro is faster (7.8s vs 9.2s per sample).

Gemini 3.1 ProQwen3 VL 235B A22B Instruct

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

Evals updated August 6, 2026Pricing updated August 10, 2026

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$0.210
Output $/1M$12.00$1.90
Vision Tasks
CaptioningDemoDemo
Chart Question Answering
ClassificationDemo
Document Question Answering
Image Tagging
Multi-Label Classification
Object DetectionDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
Model Features
Foundation Vision
LLMs with Vision Capabilities
Multimodal Vision
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort
Overall
83.1%
65.8%
Avg cost / sample$0.0093$0.0007
Avg speed / sample7.81s9.17s
By task
Object Detection
67.4%
$0.010
52.2%
$0.0014
Counting
71.6%
$0.0071
47.3%
$0.0002
Identification
100.0%
$0.0070
90.6%
$0.0002
OCR
92.6%
$0.0066
88.1%
$0.0010
Data Extraction
94.8%
$0.0063
86.6%
$0.0002
Reasoning (low)
72.2%
$0.012
29.8%
$0.0002
Reasoning (high)
74.8%
$0.021
33.8%
$0.0002

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.