Gemma 4 31B vs GPT-5 Mini

Compare Gemma 4 31B and GPT-5 Mini side-by-side. See how these vision models stack up in Image Captioning, OCR, Open Prompt, Object Detection, and Classification.

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GoogleGemma 4 31B
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OpenAIGPT-5 Mini
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Gemma 4 31B vs GPT-5 Mini: 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.

GPT-5 Mini

GPT-5 Mini, released by OpenAI on August 7, 2025, is a mid-tier variant of the GPT-5 family that balances cost, speed, and capability. It is multimodal, supporting both text and image inputs, and offers a substantial input context window of ~400,000 tokens with output lengths up to ~128,000 tokens. While less powerful than the full GPT-5, it inherits its safety tuning, instruction-following improvements, and multimodal reasoning, making it a practical choice for developers who need large context handling without the expense of premium models.

GPT-5 Mini is optimized for affordability while retaining strong reasoning performance. Benchmarks show it outperforming earlier models such as GPT-4o on many multimodal and medical VQA tasks, though it lags behind GPT-5 on the most complex problems. Ideal use cases include prototyping, scalable content generation, document analysis, and mid-range reasoning tasks where efficiency and context capacity matter more than top-tier accuracy.

Gemma 4 31B vs GPT-5 Mini Comparison Table

PropertyGemma 4 31BGPT-5 Mini
OrganizationGoogleOpenAI
Categoryopenclosed
Modalitymultimodalmultimodal
Release DateApr 2026Aug 2025
Context Window256K400K
Parameters31B
LicenseApache 2.0Proprietary
Pricing per 1M tokens
Input $/1M$0.120$0.250
Output $/1M$0.350$2.00
Vision Tasks
CaptioningDemoDemo
ClassificationDemoDemo
Object DetectionDemoDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
Model Features
Multimodal Vision
Foundation Vision
LLMs with Vision Capabilities
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Overall Score
67.16%
73.13%
Avg Response Time34.59s11.72s
Median input tokensincl. image tokens2941.4K
Median output tokens169143
Est. cost / taskon this benchmark$0.0001$0.0006
Defect Detection
80%(12/15)
80%(12/15)
Document Understanding
88.9%(8/9)
77.8%(7/9)
Object Counting
10%(1/10)
10%(1/10)
Object Understanding
71.4%(10/14)
85.7%(12/14)
Spatial Understanding
73.7%(14/19)
89.5%(17/19)

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