Gemma 4 31B vs Qwen3.5 122B A10B

Compare Gemma 4 31B and Qwen3.5 122B A10B side-by-side. See how these vision models stack up in Image Captioning, OCR, and Open Prompt.

Compare Gemma 4 31B vs Qwen3.5 122B A10B live

Run the same image across every model that supports a task and compare their outputs side-by-side.

Extract and compare text from images across multiple models.

Open OCR in the full playground
GoogleGemma 4 31B
Run to compare this model.
QwenQwen3.5 122B A10B
Run to compare this model.

Models in this comparison

Gemma 4 31B vs Qwen3.5 122B A10B: Overview

Gemma 4 31B

Gemma 4 31B is the largest dense model in Google's Gemma 4 family, built from the same research as Gemini 3 and released as open weights under the Apache 2.0 license. It supports a 256K token context window with text and image input, configurable thinking mode for step-by-step reasoning, and multilingual support across 140+ languages. The unquantized model fits on a single 80GB GPU.

For vision tasks, Gemma 4 31B supports image understanding with variable aspect ratios and resolutions, and can output structured bounding boxes for UI element detection, making it useful for document parsing and UI understanding. Compared to Gemma 3, it delivers stronger reasoning and multimodal performance. It is part of a four-size family alongside the 26B A4B MoE variant and two on-device models (E2B, E4B), with the 31B dense variant optimized for output quality and fine-tuning over inference speed.

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.

Gemma 4 31B vs Qwen3.5 122B A10B Comparison Table

PropertyGemma 4 31BQwen3.5 122B A10B
OrganizationGoogleQwen
Categoryopenopen
Modalitymultimodalmultimodal
Release DateApr 2026Feb 2026
Context Window256K256K
Parameters31B122B
LicenseApache 2.0Apache 2.0
Pricing per 1M tokens
Input $/1M$0.120$0.260
Output $/1M$0.350$2.08
Vision Tasks
CaptioningDemoDemo
Object DetectionDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
classificationDemo
Model Features
Multimodal Vision
LLMs with Vision Capabilities
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Overall Score
67.16%
76.12%
Avg Response Time34.59s1.77s
Median input tokensincl. image tokens2941.2K
Median output tokens1697
Est. cost / taskon this benchmark$0.0001$0.0003
Defect Detection
80%(12/15)
86.7%(13/15)
Document Understanding
88.9%(8/9)
77.8%(7/9)
Object Counting
10%(1/10)
40%(4/10)
Object Understanding
71.4%(10/14)
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