Gemini 3.1 Pro vs Gemma 4 31B
Compare Gemini 3.1 Pro and Gemma 4 31B side-by-side. See how these vision models stack up in Image Captioning, Open Prompt, Classification, OCR, and Object Detection.
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Gemini 3.1 Pro vs Gemma 4 31B on Vision Evals
Gemini 3.1 Pro scores higher on all 4 Vision Evals tasks.
The widest gap is Identification, where Gemini 3.1 Pro leads 100.0% to 79.2%.
Overall, Gemini 3.1 Pro averages 76.8% (#13 of 63) against 57.7% (#39 of 63) for Gemma 4 31B.
Gemma 4 31B is cheaper ($0.0015 vs $0.010 per sample), while Gemini 3.1 Pro is faster (8.4s vs 34.4s per sample).
Gemini 3.1 Pro vs Gemma 4 31B Comparison Table
Evals updated October 13, 2026Pricing updated October 23, 2026
| Property | Gemini 3.1 Pro | Gemma 4 31B |
|---|---|---|
| Organization | ||
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Feb 2026 | Apr 2026 |
| Context Window | 1.0M | 256K |
| Parameters | Unknown | 31B |
| License | Proprietary | Apache 2.0 |
| Commercial use | commercial use per vendor terms | free for commercial use |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | $0.200 |
| Output $/1M | $12.00 | $0.400 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | Supported | Supported |
| Classification | Demo | Demo |
| Document Question Answering | Supported | Supported |
| Image Tagging | Supported | Supported |
| Multi-Label Classification | Supported | Supported |
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | Supported | Supported |
| Visual Question Answering | Demo | Demo |
| Model Features | ||
| Foundation Vision | Supported | Supported |
| LLMs with Vision Capabilities | Supported | Supported |
| Multimodal Vision | Supported | Supported |
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 76.8% | 57.7% 4/5 tasks |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.010 | $0.0015 |
| Avg speed / sample | 8.44s | 34.36s |
| By task | ||
| Object Detection | 67.4% | 47.5% ±0.6, Mean of 3 runs, range 46.8 to 48.0 |
| Counting | 71.6% | 51.4% ±2.7, Mean of 3 runs, range 48.6 to 54.0 |
| Identification | 100.0% | 79.2% ±1.6, Mean of 3 runs, range 78.1 to 81.3 |
| OCR (low) | 72.6% | – |
| by category |
| |
| OCR (high) | 73.1% | – |
| by category |
| |
| Reasoning (low) | 72.2% | 52.8% ±1.3, Mean of 3 runs, range 51.7 to 54.3 |
| Reasoning (high) | 74.8% | – |
Gemini 3.1 Pro vs Gemma 4 31B: Overview
Gemini 3.1 Pro is a proprietary multimodal model from Google’s Gemini 3 series, released in early 2026 and designed for advanced reasoning across large multimodal datasets. It accepts text, images, audio, video, and documents, supporting up to a 1-million-token input context with up to 64k output tokens. Compared with Gemini 3 Pro, it improves long-context synthesis and multi-step reasoning, enabling more reliable analysis of large documents, datasets, and software codebases.
The model also advances visual understanding and grounding, allowing it to interpret UI screenshots, diagrams, and real-world scenes while referencing specific regions within images or video. These capabilities make Gemini 3.1 Pro well suited for multimodal workflows involving document processing, interface analysis, robotics research, and complex visual reasoning.
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, Gemini 3.1 Pro performed better. It scores higher on all 4 vision tasks and averages 76.8% (#13 of 63) against 57.7% (#39 of 63) 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 Identification benchmark at low effort, Gemini 3.1 Pro leads with 100.0% against 79.2%. This is the widest gap between the two models across the benchmark's tasks.
Gemma 4 31B is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0015 per sample against $0.010. Actual costs depend on your image sizes, prompts, and output length.
Gemini 3.1 Pro is faster. Across Roboflow's Vision Evals it averaged 8.4s 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 open prompts in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.