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Gemma 3 4B vs Qwen3.7 Plus

Compare Gemma 3 4B and Qwen3.7 Plus side-by-side. See how these vision models stack up in Image Captioning, OCR, and Open Prompt.

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GoogleGemma 3 4B
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QwenQwen3.7 Plus
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Models in this comparison

Gemma 3 4B vs Qwen3.7 Plus Comparison Table

Evals updated October 8, 2026Pricing updated October 8, 2026

PropertyGemma 3 4BQwen3.7 Plus
OrganizationGoogleQwen
Categoryopenclosed
Modalitymultimodal—
Release DateMar 2025Jun 2026
Context Window128K—
Parameters4BUnknown
LicenseCustomUnknown
Pricing per 1M tokens
Input $/1M$0.050$0.320
Output $/1M$0.100$1.28
Vision Tasks
CaptioningDemoDemo
ClassificationSupportedDemo
OCRDemoDemo
Visual Question AnsweringDemoDemo
Chart Question AnsweringSupportedNot listed
Document Question AnsweringSupportedNot listed
Image TaggingSupportedNot listed
Multi-Label ClassificationSupportedNot listed
object-detectionNot listedDemo
Vision LanguageSupportedNot listed
Model Features
Foundation VisionSupportedNot listed
LLMs with Vision CapabilitiesSupportedNot listed
Multimodal VisionSupportedNot listed
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort
OverallNot evaluated
58.9%
Avg cost / sample–$0.0008
Avg speed / sample–7.77s
By task
Object Detection–
60.1%
$0.0013
Counting–
50.0%
$0.0004
Identification–
84.4%
$0.0003
OCR (low)–
60.3%
$0.0009
by category
Single value
53.5%
Transcription
86.7%
Structured JSON
75.8%
Text localization
23.1%
OCR (high)–
65.5%
$0.0042
by category
Single value
58.3%
Transcription
89.7%
Structured JSON
81.3%
Text localization
30.4%
Reasoning (low)–
39.7%
$0.0003
Reasoning (high)–
68.2%
$0.0043

Gemma 3 4B vs Qwen3.7 Plus: 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.

Qwen3.7 Plus
No description available

Frequently Asked Questions

Gemma 3 4B has not yet been evaluated on Roboflow's current Vision Evals, so this comparison shows specs, licensing, and pricing rather than benchmark scores.

Yes. The comparison demo on this page runs both models on the same image side by side for image captioning and OCR in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.