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Gemma 3 4B vs Google Vision OCR

Compare Gemma 3 4B and Google Vision OCR side-by-side. See how these vision models stack up in OCR.

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GoogleGemma 3 4B
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Models in this comparison

Gemma 3 4B vs Google Vision OCR: Overview

Gemma 3 4B

Gemma 3 4B, released on March 12, 2025, is the mid-sized member of Google DeepMind’s open-weight Gemma 3 family. With about 4 billion parameters, it is multimodal—supporting text and image inputs and generating text outputs. Like the larger Gemma 3 models, it features a 128,000-token input context window with an output capacity of ~8,192 tokens, enabling it to handle long documents and mixed text–image reasoning tasks.

The 4B variant is designed as a balance between efficiency and capability: it offers multilingual support across 140+ languages, strong summarization and reasoning performance, and compatibility with moderate hardware. Inference can run with ~6.4 GB VRAM in BF16, or significantly less in quantized 8-bit (~4.4 GB) or 4-bit (~3.4 GB) modes, making it accessible to developers outside large-scale infrastructure. While it lags behind the 12B and 27B versions on the most complex reasoning and multimodal benchmarks, its lower compute footprint makes it ideal for research, prototyping, and practical deployment where efficiency matters.

Google Vision OCR

Google Vision OCR, released as part of the Cloud Vision API’s general availability in February 2016, is a proprietary Google Cloud service for extracting text from images and documents. It supports common formats like JPEG, PNG, GIF, TIFF, and PDF, and provides two main modes: TEXT_DETECTION for short snippets and scene text, and DOCUMENT_TEXT_DETECTION for dense documents, which returns structured layout information with bounding boxes.

While not an LLM (so it has no token context window or parameter count), the service performs OCR across printed text and some handwriting. It outputs detected text along with positional metadata, making it useful for digitizing scanned files, receipts, forms, and signs. However, complex layouts like tables often require downstream processing. Accessible via REST and RPC APIs, with client libraries in major languages, Google Vision OCR is widely used for document processing pipelines, archival, and accessibility applications.

Gemma 3 4B vs Google Vision OCR Comparison Table

PropertyGemma 3 4BGoogle Vision OCR
OrganizationGoogleGoogle
Categoryopenclosed
Modalitymultimodalvision
Release DateMar 2025Feb 2016
Context Window128K
Parameters4B
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$0.050
Output $/1M$0.100
Vision Tasks
OCRDemoDemo
CaptioningDemo
Vision Language
Visual Question AnsweringDemo
Model Features
Multimodal Vision
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Visual Understanding
Overall Score
37.31%
Avg Response Time16.80s
Defect Detection
60%(9/15)
Document Understanding
55.6%(5/9)
Object Counting
0%(0/10)
Object Understanding
42.9%(6/14)
Spatial Understanding
26.3%(5/19)
OCR
Overall Score
64.19%
Avg Response Time0.92s
Median input tokensincl. image tokens300
Median output tokens12
Est. cost / taskon this benchmark<$0.0001
Focused Scene OCR
63.6%(63/99)
Handwritten Math
10%(1/10)
License Plate Recognition
86.7%(26/30)
Text Recognition
73.3%(22/30)
VQA & Extraction
58.3%(35/60)