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Gemma 4 31B vs Qwen3.6 Plus

Compare Gemma 4 31B and Qwen3.6 Plus side-by-side. See how these vision models stack up in Image Captioning, OCR, and Open Prompt.

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GoogleGemma 4 31B
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QwenQwen3.6 Plus
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Models in this comparison

Gemma 4 31B vs Qwen3.6 Plus: 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.

Qwen3.6 Plus

Qwen3.6 Plus is a flagship model in Alibaba’s Qwen Plus series, designed for agentic workflows, coding, and multi-step reasoning. It supports a 1 million token context window and up to 65,536 output tokens, with built-in reasoning capabilities. The model is available as a hosted, proprietary API through Alibaba Cloud.

Compared to Qwen3.5, it improves reliability in multi-step execution and frontend code generation, with stronger performance on agentic coding tasks. It also supports document and image understanding, though its vision capabilities are more limited than dedicated Qwen-VL models. Qwen3.6 Plus is part of a broader Qwen ecosystem that includes both closed-source APIs and open-weight models.

Gemma 4 31B vs Qwen3.6 Plus Comparison Table

PropertyGemma 4 31BQwen3.6 Plus
OrganizationGoogleQwen
Categoryopenclosed
Modalitymultimodalmultimodal
Release DateApr 2026Apr 2026
Context Window256K1.0M
Parameters31B
LicenseApache 2.0Proprietary
Pricing per 1M tokens
Input $/1M$0.120$0.325
Output $/1M$0.370$1.95
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%
Visual Understanding
Overall Score
67.16%
68.66%
Avg Response Time34.59s34.17s
Median input tokensincl. image tokens2941.2K
Median output tokens16947
Est. cost / taskon this benchmark$0.0001$0.0005
Defect Detection
80%(12/15)
86.7%(13/15)
Document Understanding
88.9%(8/9)
77.8%(7/9)
Object Counting
10%(1/10)
20%(2/10)
Object Understanding
71.4%(10/14)
78.6%(11/14)
Spatial Understanding
73.7%(14/19)
68.4%(13/19)
OCR
Overall Score
84.72%
58.52%
Avg Response Time11.82s5.49s
Median input tokensincl. image tokens290124
Median output tokens13118
Est. cost / taskon this benchmark$0.0001$0.0001
Focused Scene OCR
86.9%(86/99)
76.8%(76/99)
Handwritten Math
50%(5/10)
80%(8/10)
License Plate Recognition
93.3%(28/30)
13.3%(4/30)
Text Recognition
80%(24/30)
50%(15/30)
VQA & Extraction
85%(51/60)
51.7%(31/60)

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