Gemma 4 12B vs Gemma 4 31B
Compare Gemma 4 12B and Gemma 4 31B side-by-side.
Compare Gemma 4 12B vs Gemma 4 31B live
Run the same image across every model that supports a task and compare their outputs side-by-side.
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
Gemma 4 12B vs Gemma 4 31B: Overview
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.
Gemma 4 31B is the largest dense model in Google's Gemma 4 family, built from the same research as Gemini 3 and released as open weights under the Apache 2.0 license. It supports a 256K token context window with text and image input, configurable thinking mode for step-by-step reasoning, and multilingual support across 140+ languages. The unquantized model fits on a single 80GB GPU.
For vision tasks, Gemma 4 31B supports image understanding with variable aspect ratios and resolutions, and can output structured bounding boxes for UI element detection, making it useful for document parsing and UI understanding. Compared to Gemma 3, it delivers stronger reasoning and multimodal performance. It is part of a four-size family alongside the 26B A4B MoE variant and two on-device models (E2B, E4B), with the 31B dense variant optimized for output quality and fine-tuning over inference speed.
Gemma 4 12B vs Gemma 4 31B Comparison Table
| Property | Gemma 4 12B | Gemma 4 31B |
|---|---|---|
| Organization | ||
| Category | open | open |
| Modality | multimodal | multimodal |
| Release Date | Jun 2026 | Apr 2026 |
| Context Window | — | 256K |
| Parameters | 12B | 31B |
| License | Apache 2.0 | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $0.120 | |
| Output $/1M | $0.350 | |
| Vision Tasks | ||
| Captioning | Demo | |
| OCR | Demo | |
| Vision Language | ||
| Visual Question Answering | Demo | |
| classification | Demo | |
| Object Detection | Demo | |
| Model Features | ||
| Multimodal Vision | ||
Vision Evalspass/fail results · 67 prompts Score key:≥75%40–74%<40% | ||
| Overall Score | 62.69% | 67.16% |
| Avg Response Time | 6.88s | 34.59s |
| Median input tokensincl. image tokens | 294 | |
| Median output tokens | 169 | |
| Est. cost / taskon this benchmark | $0.0001 | |
| Defect Detection | 73.3%(11/15) | 80%(12/15) |
| Document Understanding | 88.9%(8/9) | 88.9%(8/9) |
| Object Counting | 10%(1/10) | 10%(1/10) |
| Object Understanding | 78.6%(11/14) | 71.4%(10/14) |
| Spatial Understanding | 57.9%(11/19) | 73.7%(14/19) |
Output tokens (incl. reasoning) and est. cost / task are measured on this benchmark from a single low-temperature run, and shown only for models whose run covered at least 90% of prompts. Methodology