Gemini 3 Pro vs Gemma 4 12B
Compare Gemini 3 Pro and Gemma 4 12B side-by-side.
Compare Gemini 3 Pro vs Gemma 4 12B live
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These models don't share enough common tasks for a side-by-side demo. See the comparison table below for their capabilities.
Models in this comparison
Gemini 3 Pro vs Gemma 4 12B: Overview
Gemini 3 Pro is Google DeepMind’s flagship multimodal frontier model, built for high-accuracy reasoning and large-scale context understanding across text, images, audio, video, code, and documents. It delivers major gains over Gemini 2.5 Pro, supported by a 1M-token window and strong performance on Google-reported benchmarks such as GPQA Diamond, MMMU-Pro, and Video-MMMU.
The model excels at structured outputs, tool use, and agentic coding, enabling complex multi-step workflows and analysis of entire books, codebases, or long videos in a single prompt. Positioned as Google’s top production model, it balances advanced reasoning with broad multimodal capabilities, making it well suited for research assistants, automation agents, coding systems, and enterprise-scale document and media analysis.
Gemma 4 12B is an open-weight multimodal model from Google in the Gemma 4 family. It is intended for text and image understanding tasks such as visual question answering, OCR, captioning, and document understanding, with a smaller parameter footprint than the larger Gemma 4 variants.
This entry is connected to Roboflow Playground vision evals for comparison. No runnable Playground workflow is configured yet, so the model page is used for discovery and benchmark context rather than direct hosted inference.
Gemini 3 Pro vs Gemma 4 12B Comparison Table
| Property | Gemini 3 Pro | Gemma 4 12B |
|---|---|---|
| Organization | ||
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Nov 2025 | Jun 2026 |
| Context Window | 1.0M | — |
| Parameters | 12B | |
| License | Proprietary | Apache 2.0 |
| Vision Tasks | ||
| Captioning | ||
| OCR | ||
| Vision Language | ||
| Visual Question Answering | ||
| Classification | ||
| Object Detection | ||
| Model Features | ||
| Multimodal Vision | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
Vision Evalspass/fail results · 67 prompts Score key:≥75%40–74%<40% | ||
| Visual Understanding | ||
| Overall Score | 62.69% | |
| Avg Response Time | 6.88s | |
| Defect Detection | 73.3%(11/15) | |
| Document Understanding | 88.9%(8/9) | |
| Object Counting | 10%(1/10) | |
| Object Understanding | 78.6%(11/14) | |
| Spatial Understanding | 57.9%(11/19) | |