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Gemini 4 Hydrogen vs Gemma 3 27B

Gemini 4 Hydrogen: 87.3% average across five Vision Evals tasks (#3 of 69 models). We have not run Gemma 3 27B through Roboflow's Vision Evals, so there is no head-to-head score. Run both on your own image below.

Compare Gemini 4 Hydrogen vs Gemma 3 27B live

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GoogleGemini 4 Hydrogen
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GoogleGemma 3 27B
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Gemini 4 Hydrogen vs Gemma 3 27B Comparison Table

Evals updated November 12, 2026Pricing updated November 13, 2026

PropertyGemini 4 HydrogenGemma 3 27B
OrganizationGoogleGoogle
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateNov 2026Mar 2025
Context Window1.0M128K
ParametersUnknownUnknown
LicenseProprietaryCustom
Commercial usecommercial use per vendor termsSee license terms
Pricing per 1M tokens
Input $/1MNo published price$0.080
Output $/1MNo published price$0.350
Vision Tasks
ClassificationDemoSupported
Chart Question AnsweringNot listedSupported
Document Question AnsweringNot listedSupported
Image TaggingNot listedSupported
Multi-Label ClassificationNot listedSupported
object-detectionDemoNot listed
Promptable Concept SegmentationDemoNot listed
Both support 4 more tasks
Model Features
Both support 3 more features
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort
Overall
87.3%
Not evaluated
Avg cost / sample$0.0094–
Avg speed / sample13.23s–
By task
Object Detection (low)
81.0%
±0.8, Mean of 3 runs, range 80.3 to 81.9
$0.016
–
Object Detection (high)
83.3%
±0.7, Mean of 3 runs, range 82.4 to 83.8
$0.022
–
Counting (low)
83.8%
±2.0, Mean of 3 runs, range 82.4 to 86.5
$0.0041
–
Counting (high)
84.2%
±2.7, Mean of 3 runs, range 82.4 to 87.8
$0.012
–
Identification (low)
96.9%
±0.0, Mean of 3 runs, range 96.9 to 96.9
$0.0030
–
Identification (high)
96.9%
±0.0, Mean of 3 runs, range 96.9 to 96.9
$0.0039
–
OCR (low)
86.7%
$0.0089
–
by category
Single value
78.7%
Transcription
96.1%
Structured JSON
93.6%
Text localization
83.9%
OCR (high)
87.2%
$0.026
–
by category
Single value
80.9%
Transcription
95.3%
Structured JSON
93.1%
Text localization
83.7%
Reasoning (low)
88.1%
±0.7, Mean of 3 runs, range 87.4 to 88.7
$0.0046
–
Reasoning (high)
91.8%
±0.3, Mean of 3 runs, range 91.4 to 92.0
$0.013
–

Gemini 4 Hydrogen vs Gemma 3 27B: Overview

Gemini 4 Hydrogen

Gemini 4 Hydrogen is a multimodal model from Google, positioned as a successor to Gemini 3.8 Flash with stronger agent and coding capabilities at Flash-class speed.

Gemma 3 27B

Gemma 3 27B, announced on March 12, 2025, is the largest open-weight model in Google DeepMind’s Gemma 3 family. With around 27 billion parameters, it is multimodal—accepting both text and images as input and producing text outputs. It supports a 128,000-token context window and typically generates up to ~8,192 tokens, enabling it to process multi-page documents, extended conversations, or large batches of images in a single prompt.

The model is instruction-tuned in its “-it” variants for chat, reasoning, and summarization use cases, and it supports structured outputs and function calling. It is multilingual, covering over 140 languages. Deployment is flexible: the full BF16 model requires ~46 GB of VRAM, but quantization-aware training (QAT) versions in 8-bit or 4-bit reduce the footprint significantly, allowing more accessible use outside large-scale clusters. While it delivers stronger reasoning and multimodal performance than smaller Gemma models, it remains lighter and more open than proprietary systems, making it well-suited for research, development, and fine-tuned applications.

Frequently Asked Questions

Gemini 4 Hydrogen is released under Proprietary, while Gemma 3 27B uses Custom. In practice: Gemini 4 Hydrogen: commercial use per vendor terms. Licensing often matters more than raw accuracy for commercial deployments, so check the terms against how you plan to ship.

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.