Gemini 4 Carbon vs Gemma 3 12B
Gemini 4 Carbon: 89.4% average across five Vision Evals tasks (#1 of 68 models). We have not run Gemma 3 12B through Roboflow's Vision Evals, so there is no head-to-head score. Run both on your own image below.
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Gemini 4 Carbon vs Gemma 3 12B Comparison Table
Evals updated November 10, 2026Pricing updated November 10, 2026
| Property | Gemini 4 Carbon | Gemma 3 12B |
|---|---|---|
| Organization | ||
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Nov 2026 | Mar 2025 |
| Context Window | 1.0M | 128K |
| Parameters | Unknown | 12B |
| License | Proprietary | Custom |
| Commercial use | commercial use per vendor terms | See license terms |
| Pricing per 1M tokens | ||
| Input $/1M | No published price | $0.050 |
| Output $/1M | No published price | $0.150 |
| Vision Tasks | ||
| Classification | Demo | Supported |
| Object Detection | Demo | Not listed |
| Promptable Concept Segmentation | Demo | Not listed |
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 89.4% | Not evaluated |
| Avg cost / sample | $0.0061 | – |
| Avg speed / sample | 8.71s | – |
| By task | ||
| Object Detection (low) | 84.5% ±0.3, Mean of 3 runs, range 84.2 to 84.8 | – |
| Object Detection (high) | 85.9% ±0.3, Mean of 3 runs, range 85.6 to 86.2 | – |
| Counting (low) | 90.1% ±1.4, Mean of 3 runs, range 89.2 to 91.9 | – |
| Counting (high) | 86.9% ±2.7, Mean of 3 runs, range 83.8 to 89.2 | – |
| Identification (low) | 93.8% ±0.0, Mean of 3 runs, range 93.8 to 93.8 | – |
| Identification (high) | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 | – |
| OCR (low) | 87.2% | – |
| by category |
| |
| OCR (high) | 89.7% | – |
| by category |
| |
| Reasoning (low) | 91.4% ±1.7, Mean of 3 runs, range 90.1 to 93.4 | – |
| Reasoning (high) | 92.3% ±0.3, Mean of 3 runs, range 92.0 to 92.7 | – |
Gemini 4 Carbon vs Gemma 3 12B: Overview
Gemini 4 Carbon is a multimodal model from Google. It builds on Gemini 4 Argon and supports reasoning over text and images. Google describes improvements in coding and long-running tasks. Model weights are not publicly released.
Gemma 3 12B, announced by Google DeepMind on March 12, 2025, is part of the open-weight Gemma 3 family, designed to provide a balance between capability and accessibility. With around 12 billion parameters, it supports multimodal input (text + images) and outputs text, making it useful for reasoning, summarization, Q&A, and visual understanding tasks. The model supports an input context of 128,000 tokens and typically generates up to ~8,000 tokens in output.
The 12B variant is instruction-tuned (“Gemma-3-12B-IT”) and optimized for multilingual use across more than 140 languages. It can run on a single GPU or TPU, offering a lighter compute footprint than very large proprietary models, while still achieving strong performance in reasoning benchmarks. Quantized and lower-precision variants are available to improve efficiency. Limitations include smaller output lengths relative to input capacity, scaling hardware needs at larger sizes, and performance below massive proprietary models on the most complex multimodal or reasoning-heavy tasks.
Frequently Asked Questions
Gemini 4 Carbon is released under Proprietary, while Gemma 3 12B uses Custom. In practice: Gemini 4 Carbon: 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.