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Gemma 4 31B vs Qwen2.5 VL 7B Instruct

Compare Gemma 4 31B and Qwen2.5 VL 7B Instruct 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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QwenQwen2.5 VL 7B Instruct
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Gemma 4 31B vs Qwen2.5 VL 7B Instruct Comparison Table

Evals updated August 20, 2026Pricing updated August 24, 2026

PropertyGemma 4 31BQwen2.5 VL 7B Instruct
OrganizationGoogleQwen
Categoryopenopen
Modalitymultimodalmultimodal
Release DateApr 2026Jan 2025
Context Window256K33K
Parameters31B7B
LicenseApache 2.0Apache 2.0
Pricing per 1M tokens
Input $/1M$0.100
Output $/1M$0.340
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

Gemma 4 31B vs Qwen2.5 VL 7B Instruct: 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.

Qwen2.5 VL 7B Instruct

Qwen2.5-VL-7B-Instruct is a 7-billion parameter vision-language model from Alibaba’s QwenLM team, released on January 26, 2025 under the Apache 2.0 license. It is the instruction-tuned variant of the 7B scale in the Qwen2.5-VL family, designed to process multimodal inputs such as text, images, charts, documents, and video. The model enables structured outputs—including JSON for structured content and bounding boxes for visual localization. Weights are publicly available on Hugging Face and GitHub, making it suitable for both research and applied multimodal use.