Gemma 3 4B vs Qwen3.7 Plus
Compare Gemma 3 4B 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 4B vs Qwen3.7 Plus Comparison Table
Evals updated August 20, 2026Pricing updated August 24, 2026
| Property | Gemma 3 4B | Qwen3.7 Plus |
|---|---|---|
| Organization | Qwen | |
| Category | open | closed |
| Modality | multimodal | — |
| Release Date | Mar 2025 | — |
| Context Window | 128K | — |
| Parameters | 4B | |
| License | Custom | |
| Pricing per 1M tokens | ||
| Input $/1M | $0.050 | $0.320 |
| Output $/1M | $0.100 | $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 4B vs Qwen3.7 Plus: Overview
Gemma 3 4B, released on March 12, 2025, is the mid-sized member of Google DeepMind’s open-weight Gemma 3 family. With about 4 billion parameters, it is multimodal—supporting text and image inputs and generating text outputs. Like the larger Gemma 3 models, it features a 128,000-token input context window with an output capacity of ~8,192 tokens, enabling it to handle long documents and mixed text–image reasoning tasks.
The 4B variant is designed as a balance between efficiency and capability: it offers multilingual support across 140+ languages, strong summarization and reasoning performance, and compatibility with moderate hardware. Inference can run with ~6.4 GB VRAM in BF16, or significantly less in quantized 8-bit (~4.4 GB) or 4-bit (~3.4 GB) modes, making it accessible to developers outside large-scale infrastructure. While it lags behind the 12B and 27B versions on the most complex reasoning and multimodal benchmarks, its lower compute footprint makes it ideal for research, prototyping, and practical deployment where efficiency matters.
Frequently Asked Questions
Gemma 3 4B 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.