Roboflow

Gemma 4 31B vs Llama 3.2 Vision 11b

Compare Gemma 4 31B and Llama 3.2 Vision 11b side-by-side. See how these vision models stack up in Image Captioning, OCR, Open Prompt, and Classification.

Compare Gemma 4 31B vs Llama 3.2 Vision 11b live

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

Compare image classification labels and confidence scores side-by-side.

Open Classification in the full playground
GoogleGemma 4 31B
Run to compare this model.
MetaLlama 3.2 Vision 11b
Run to compare this model.

Models in this comparison

Gemma 4 31B vs Llama 3.2 Vision 11b: 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.

Llama 3.2 Vision 11b

Llama 3.2 Vision 11B, released by Meta on September 25, 2024, is the first mid-sized model in the Llama family with vision capabilities, supporting both text and image inputs with text-only outputs. It contains around 11 billion parameters (~10.6B) and features a 128,000-token context window, making it suitable for multimodal reasoning over long documents and image-text tasks. The model was trained on ~6 billion image–text pairs and has a knowledge cutoff of December 2023.

The model is available in a base and an instruction-tuned (“Vision-Instruct”) version, optimized for tasks like captioning, visual question answering, and image reasoning. It leverages Group-Query Attention (GQA) for improved inference efficiency and scalability. While text tasks officially support multiple languages (English, German, French, Italian, Portuguese, Hindi, Spanish, Thai), multimodal (image+text) tasks are supported primarily in English. Llama 3.2 Vision 11B is accessible through Hugging Face, Amazon Bedrock, Azure AI Foundry, NVIDIA NIM, and OCI, making it a widely deployable open-weight multimodal foundation model.

Gemma 4 31B vs Llama 3.2 Vision 11b Comparison Table

PropertyGemma 4 31BLlama 3.2 Vision 11b
OrganizationGoogleMeta
Categoryopenopen
Modalitymultimodalmultimodal
Release DateApr 2026Sep 2024
Context Window256K128K
Parameters31B11B
LicenseApache 2.0Proprietary
Pricing per 1M tokens
Input $/1M$0.120$0.345
Output $/1M$0.350$0.345
Vision Tasks
CaptioningDemoDemo
ClassificationDemoDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
Object DetectionDemo
Model Features
Multimodal Vision
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Visual Understanding
Overall Score
67.16%
Avg Response Time34.59s
Median input tokensincl. image tokens294
Median output tokens169
Est. cost / taskon this benchmark$0.0001
Defect Detection
80%(12/15)
Document Understanding
88.9%(8/9)
Object Counting
10%(1/10)
Object Understanding
71.4%(10/14)
Spatial Understanding
73.7%(14/19)
OCR
Overall Score
84.72%
Avg Response Time11.82s
Median input tokensincl. image tokens290
Median output tokens131
Est. cost / taskon this benchmark$0.0001
Focused Scene OCR
86.9%(86/99)
Handwritten Math
50%(5/10)
License Plate Recognition
93.3%(28/30)
Text Recognition
80%(24/30)
VQA & Extraction
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