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.
Compare Gemma 3 4B vs Google Vision OCR live
Run the same image across every model that supports a task and compare their outputs side-by-side.
Extract and compare text from images across multiple models.
Upload an image
Drag and drop an image here, or click to browse
Models in this comparison
Gemma 3 4B vs Google Vision OCR: Overview
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, 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
| Property | Gemma 3 4B | Google Vision OCR |
|---|---|---|
| Organization | ||
| Category | open | closed |
| Modality | multimodal | vision |
| Release Date | Mar 2025 | Feb 2016 |
| Context Window | 128K | — |
| Parameters | 4B | |
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $0.050 | |
| Output $/1M | $0.100 | |
| Vision Tasks | ||
| OCR | Demo | Demo |
| Captioning | Demo | |
| Vision Language | ||
| Visual Question Answering | Demo | |
| Model Features | ||
| Multimodal Vision | ||
Vision Evalspass/fail results · 67 prompts Score key:≥75%40–74%<40% | ||
| Visual Understanding | ||
| Overall Score | 37.31% | |
| Avg Response Time | 16.80s | |
| 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 Time | 0.92s | |
| Median input tokensincl. image tokens | 300 | |
| Median output tokens | 12 | |
| 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) | |