Gemini 3.1 Pro vs Qwen3.5 122B A10B

Compare Gemini 3.1 Pro and Qwen3.5 122B A10B 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 122B A10B
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Models in this comparison

Gemini 3.1 Pro vs Qwen3.5 122B A10B: 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 122B A10B

Qwen3.5-122B-A10B is a high-capacity multimodal Mixture-of-Experts (MoE) model developed by Alibaba’s Qwen team as part of the Qwen3.5 model family. The architecture contains 122 billion total parameters while activating roughly 10 billion per token through sparse expert routing, allowing the model to balance large-scale reasoning ability with relatively efficient inference compared to dense models of similar size.

The model is designed to process both text and visual inputs within a unified multimodal framework, enabling tasks that require reasoning across images, documents, charts, and natural language. This makes it suitable for applications such as document understanding, diagram interpretation, and complex visual question answering.

Qwen3.5-122B-A10B supports a native context window of approximately 256,000 tokens, which can be extended further through techniques such as YaRN scaling to support very long-context workloads. Released under the Apache 2.0 license, it builds on earlier Qwen multimodal systems and provides developers with an open-weight model capable of handling demanding multimodal reasoning and analysis tasks.

Gemini 3.1 Pro vs Qwen3.5 122B A10B Comparison Table

PropertyGemini 3.1 ProQwen3.5 122B A10B
OrganizationGoogleQwen
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateFeb 2026Feb 2026
Context Window1.0M256K
Parameters122B
LicenseProprietaryApache 2.0
Pricing per 1M tokens
Input $/1M$2.00$0.260
Output $/1M$12.00$2.08
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%
76.12%
Avg Response Time6.13s1.77s
Median input tokensincl. image tokens1.1K1.2K
Median output tokens117
Est. cost / taskon this benchmark$0.0024$0.0003
Defect Detection
73.3%(11/15)
86.7%(13/15)
Document Understanding
88.9%(8/9)
77.8%(7/9)
Object Counting
44.4%(4/9)
40%(4/10)
Object Understanding
92.9%(13/14)
92.9%(13/14)
Spatial Understanding
73.7%(14/19)
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