Gemma 4 31B vs Qwen3.7 Flash
Compare Gemma 4 31B and Qwen3.7 Flash 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 Flash on Vision Evals
Gemma 4 31B scores higher on 5 of the six Vision Evals tasks.
The widest gap is Reasoning, where Gemma 4 31B leads 50.8% to 34.4%.
Overall, Gemma 4 31B averages 67.0% (#30 of 53) against 61.5% (#45 of 53) for Qwen3.7 Flash.
Qwen3.7 Flash is both cheaper ($0.0001 vs $0.0012 per sample) and faster (6.3s vs 28.8s per sample).
Gemma 4 31B vs Qwen3.7 Flash Comparison Table
Evals updated September 5, 2026Pricing updated September 11, 2026
| Property | Gemma 4 31B | Qwen3.7 Flash |
|---|---|---|
| Organization | Qwen | |
| Category | open | closed |
| Modality | multimodal | multimodal |
| Release Date | Apr 2026 | Jul 2026 |
| Context Window | 256K | 1.0M |
| Parameters | 31B | |
| License | Apache 2.0 | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $0.090 | $0.030 |
| Output $/1M | $0.340 | $0.130 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | ||
| Classification | Demo | Demo |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 67.0% | 61.5% |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.0012 | $0.0001 |
| Avg speed / sample | 28.79s | 6.28s |
| By task | ||
| Object Detection | 48.2% ±0.2, Mean of 3 runs, range 48.0 to 48.4 | 42.8% |
| Counting | 51.4% ±1.4, Mean of 3 runs, range 50.0 to 52.7 | 46.0% |
| Identification | 80.2% ±3.1, Mean of 3 runs, range 78.1 to 84.4 | 84.4% |
| OCR | 90.8% ±0.2, Mean of 3 runs, range 90.6 to 90.9 | 84.1% |
| Data Extraction | 80.4% ±2.6, Mean of 3 runs, range 77.3 to 82.5 | 77.3% |
| Reasoning (low) | 50.8% ±1.7, Mean of 3 runs, range 49.0 to 52.3 | 34.4% |
| Reasoning (high) | – | 61.6% |
Gemma 4 31B vs Qwen3.7 Flash: 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.
Qwen3.7 Flash is the low-latency, cost-oriented tier of Alibaba's Qwen3.7 series, a vision-language reasoning model that accepts interleaved text and image input and returns text. It is built as a hybrid thinking model: like the rest of the Qwen3.7, Qwen3.6, and Qwen3.5 families served through Alibaba Cloud Model Studio, it can either emit an explicit reasoning trace before answering or respond directly, with thinking behavior controlled by an enable_thinking switch that defaults to on for the Qwen3.7 generation. The model exposes a context window of roughly one million tokens and a maximum generation length of 65,536 tokens, which allows long multi-image sequences, long documents, and extended agent trajectories to be held in a single request.
Functionally, Qwen3.7 Flash targets multimodal agent workloads rather than pure chat. Reported strengths include object recognition, spatial understanding, and perception of real-world scenes, alongside visual coding, search, and computer-use style interaction where the model reads screen content and reasons over interface state. Weights are not published; the model is a proprietary endpoint positioned below Qwen3.7 Plus and Qwen3.7 Max in the same series, and it supports function calling and tool use for agentic pipelines.
Frequently Asked Questions
On Roboflow's Vision Evals, Gemma 4 31B performed better. It scores higher on 5 of the six vision tasks and averages 67.0% (#30 of 53) against 61.5% (#45 of 53) for Qwen3.7 Flash. 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 50.8% against 34.4%. This is the widest gap between the two models across the benchmark's tasks.
Qwen3.7 Flash is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0001 per sample against $0.0012. Actual costs depend on your image sizes, prompts, and output length.
Qwen3.7 Flash is faster. Across Roboflow's Vision Evals it averaged 6.3s per inference against 28.8s. 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.