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Gemini 2.5 Pro vs Gemma 4 31B

Compare Gemini 2.5 Pro and Gemma 4 31B side-by-side. See how these vision models stack up in Object Detection, Open Prompt, Classification, OCR, and Image Captioning.

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GoogleGemini 2.5 Pro
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GoogleGemma 4 31B
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Gemini 2.5 Pro vs Gemma 4 31B: Overview

Gemini 2.5 Pro

Gemini 2.5 Pro, released on June 17, 2025, is Google DeepMind’s most capable model in the Gemini 2.5 family, optimized for deep reasoning, coding, and complex multimodal tasks. It accepts text, images, audio, video, and PDFs as input and outputs text. The model supports 1 million input tokens with an output capacity of up to 65K tokens, enabling large-scale comprehension of datasets, codebases, and technical documents. Its training knowledge extends to January 2025.

Pro outperforms earlier Gemini 2.0 models across benchmarks, including agentic coding tasks where it achieved ~63.8% on SWE-Bench Verified. It supports structured outputs, function calling, code execution, search grounding, and URL context, making it well-suited for enterprise, STEM, and developer workflows. However, it does not currently support image or audio generation in its stable release, and its higher computational cost and latency make it less efficient than Flash or Flash-Lite. It is available via the Gemini API, Google AI Studio, and Vertex AI.

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.

Gemini 2.5 Pro vs Gemma 4 31B Comparison Table

PropertyGemini 2.5 ProGemma 4 31B
OrganizationGoogleGoogle
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateJun 2025Apr 2026
Context Window1.0M256K
Parameters31B
LicenseProprietaryApache 2.0
Pricing per 1M tokens
Input $/1M$1.25$0.120
Output $/1M$10.00$0.370
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%
Visual Understanding
Overall Score
70.15%
67.16%
Avg Response Time11.87s34.59s
Median input tokensincl. image tokens294294
Median output tokens565169
Est. cost / taskon this benchmark$0.0060$0.0001
Defect Detection
73.3%(11/15)
80%(12/15)
Document Understanding
88.9%(8/9)
88.9%(8/9)
Object Counting
20%(2/10)
10%(1/10)
Object Understanding
78.6%(11/14)
71.4%(10/14)
Spatial Understanding
78.9%(15/19)
73.7%(14/19)
OCR
Overall Score
78.6%
84.72%
Avg Response Time4.91s11.82s
Median input tokensincl. image tokens290290
Median output tokens323131
Est. cost / taskon this benchmark$0.0036$0.0001
Focused Scene OCR
78.8%(78/99)
86.9%(86/99)
Handwritten Math
80%(8/10)
50%(5/10)
License Plate Recognition
90%(27/30)
93.3%(28/30)
Text Recognition
73.3%(22/30)
80%(24/30)
VQA & Extraction
75%(45/60)
85%(51/60)

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