Gemma 4 31B vs Qwen3.7 Plus
Compare Gemma 4 31B and Qwen3.7 Plus side-by-side. See how these vision models stack up in Image Captioning, OCR, Open Prompt, Object Detection, and Classification.
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Models in this comparison
Gemma 4 31B vs Qwen3.7 Plus on Vision Evals
Gemma 4 31B scores higher on 2 of the 4 Vision Evals tasks.
The widest gap is Reasoning, where Gemma 4 31B leads 52.8% to 39.7%.
Overall, Gemma 4 31B averages 57.7% (#37 of 61) against 58.9% (#35 of 61) for Qwen3.7 Plus.
Qwen3.7 Plus is both cheaper ($0.0008 vs $0.0015 per sample) and faster (7.8s vs 34.4s per sample).
Gemma 4 31B vs Qwen3.7 Plus Comparison Table
Evals updated October 8, 2026Pricing updated October 8, 2026
| Property | Gemma 4 31B | Qwen3.7 Plus |
|---|---|---|
| Organization | Qwen | |
| Category | open | closed |
| Modality | multimodal | — |
| Release Date | Apr 2026 | Jun 2026 |
| Context Window | 256K | — |
| Parameters | 31B | Unknown |
| License | Apache 2.0 | Unknown |
| Pricing per 1M tokens | ||
| Input $/1M | $0.090 | $0.320 |
| Output $/1M | $0.340 | $1.28 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Classification | Demo | Demo |
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Visual Question Answering | Demo | Demo |
| Chart Question Answering | Supported | Not listed |
| Document Question Answering | Supported | Not listed |
| Image Tagging | Supported | Not listed |
| Multi-Label Classification | Supported | Not listed |
| Vision Language | Supported | Not listed |
| Model Features | ||
| Foundation Vision | Supported | Not listed |
| LLMs with Vision Capabilities | Supported | Not listed |
| Multimodal Vision | Supported | Not listed |
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 57.7% 4/5 tasks | 58.9% |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.0015 | $0.0008 |
| Avg speed / sample | 34.36s | 7.77s |
| By task | ||
| Object Detection | 47.5% ±0.6, Mean of 3 runs, range 46.8 to 48.0 | 60.1% |
| Counting | 51.4% ±2.7, Mean of 3 runs, range 48.6 to 54.0 | 50.0% |
| Identification | 79.2% ±1.6, Mean of 3 runs, range 78.1 to 81.3 | 84.4% |
| OCR (low) | – | 60.3% |
| by category |
| |
| OCR (high) | – | 65.5% |
| by category |
| |
| Reasoning (low) | 52.8% ±1.3, Mean of 3 runs, range 51.7 to 54.3 | 39.7% |
| Reasoning (high) | – | 68.2% |
Gemma 4 31B vs Qwen3.7 Plus: Overview
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.
Frequently Asked Questions
On Roboflow's Vision Evals, Qwen3.7 Plus performed slightly better overall. The two split the 4 vision tasks 2 to 2, but Qwen3.7 Plus averages 58.9% (#35 of 61) against 57.7% (#37 of 61) for Gemma 4 31B. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
Yes. On the Vision Evals Reasoning benchmark at low effort, Gemma 4 31B leads with 52.8% against 39.7%. This is the widest gap between the two models across the benchmark's tasks.
Qwen3.7 Plus is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0008 per sample against $0.0015. Actual costs depend on your image sizes, prompts, and output length.
Qwen3.7 Plus is faster. Across Roboflow's Vision Evals it averaged 7.8s per inference against 34.4s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.
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.