Gemma 4 12B vs Qwen2.5 VL 7B Instruct
Compare Gemma 4 12B and Qwen2.5 VL 7B Instruct side-by-side.
Compare Gemma 4 12B vs Qwen2.5 VL 7B Instruct live
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Models in this comparison
Gemma 4 12B vs Qwen2.5 VL 7B Instruct: 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.
Qwen2.5-VL-7B-Instruct is a 7-billion parameter vision-language model from Alibaba’s QwenLM team, released on January 26, 2025 under the Apache 2.0 license. It is the instruction-tuned variant of the 7B scale in the Qwen2.5-VL family, designed to process multimodal inputs such as text, images, charts, documents, and video. The model enables structured outputs—including JSON for structured content and bounding boxes for visual localization. Weights are publicly available on Hugging Face and GitHub, making it suitable for both research and applied multimodal use.
Gemma 4 12B vs Qwen2.5 VL 7B Instruct Comparison Table
| Property | Gemma 4 12B | Qwen2.5 VL 7B Instruct |
|---|---|---|
| Organization | Qwen | |
| Category | open | open |
| Modality | multimodal | multimodal |
| Release Date | Jun 2026 | Jan 2025 |
| Context Window | — | 33K |
| Parameters | 12B | 7B |
| License | Apache 2.0 | Apache 2.0 |
| Vision Tasks | ||
| Captioning | Demo | |
| OCR | Demo | |
| Vision Language | ||
| Visual Question Answering | Demo | |
| Object Detection | ||
| Model Features | ||
| Multimodal Vision | ||
| LLMs with Vision Capabilities | ||
Vision Evalspass/fail results · 67 prompts Score key:≥75%40–74%<40% | ||
| Overall Score | 62.69% | 52.24% |
| Avg Response Time | 6.88s | 47.64s |
| Defect Detection | 73.3%(11/15) | 60%(9/15) |
| Document Understanding | 88.9%(8/9) | 77.8%(7/9) |
| Object Counting | 10%(1/10) | 0%(0/10) |
| Object Understanding | 78.6%(11/14) | 57.1%(8/14) |
| Spatial Understanding | 57.9%(11/19) | 57.9%(11/19) |