Gemma 3 27B vs Qwen3.7 Plus
Compare Gemma 3 27B and Qwen3.7 Plus side-by-side. See how these vision models stack up in Image Captioning, OCR, and Open Prompt.
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Gemma 3 27B vs Qwen3.7 Plus Comparison Table
Evals updated August 20, 2026Pricing updated August 24, 2026
| Property | Gemma 3 27B | Qwen3.7 Plus |
|---|---|---|
| Organization | Qwen | |
| Category | open | closed |
| Modality | multimodal | — |
| Release Date | Mar 2025 | — |
| Context Window | 128K | — |
| Parameters | ||
| License | Custom | |
| Pricing per 1M tokens | ||
| Input $/1M | $0.080 | $0.320 |
| Output $/1M | $0.450 | $1.28 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Classification | Demo | |
| OCR | Demo | Demo |
| Visual Question Answering | Demo | Demo |
| Chart Question Answering | ||
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| object-detection | Demo | |
| Vision Language | ||
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | Not evaluated | 67.4% |
| Avg cost / sample | – | $0.0008 |
| Avg speed / sample | – | 7.01s |
| By task | ||
| Object Detection | – | 60.1% $0.0013 |
| Counting | – | 50.0% $0.0004 |
| Identification | – | 84.4% $0.0003 |
| OCR | – | 86.5% $0.0009 |
| Data Extraction | – | 83.5% $0.0004 |
| Reasoning (low) | – | 39.7% $0.0003 |
| Reasoning (high) | – | 68.2% $0.0043 |
Gemma 3 27B vs Qwen3.7 Plus: Overview
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
Gemma 3 27B has not yet been evaluated on Roboflow's current Vision Evals, so this comparison shows specs, licensing, and pricing rather than benchmark scores.
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.