Gemini 3 Flash vs Gemma 4 31B
Compare Gemini 3 Flash and Gemma 4 31B side-by-side. See how these vision models stack up in Object Detection, Classification, Open Prompt, OCR, and Image Captioning.
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Models in this comparison
Gemini 3 Flash vs Gemma 4 31B on Vision Evals
Gemini 3 Flash scores higher on 3 of the 4 Vision Evals tasks.
The widest gap is Counting, where Gemini 3 Flash leads 67.6% to 51.4%.
Overall, Gemini 3 Flash averages 65.7% (#27 of 63) against 57.7% (#39 of 63) for Gemma 4 31B.
Gemma 4 31B is cheaper ($0.0015 vs $0.0024 per sample), while Gemini 3 Flash is faster (5.8s vs 34.4s per sample).
Gemini 3 Flash vs Gemma 4 31B Comparison Table
Evals updated October 13, 2026Pricing updated October 16, 2026
| Property | Gemini 3 Flash | Gemma 4 31B |
|---|---|---|
| Organization | ||
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Dec 2025 | 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 | $0.500 | $0.200 |
| Output $/1M | $3.00 | $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 | 65.7% | 57.7% 4/5 tasks |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.0024 | $0.0015 |
| Avg speed / sample | 5.83s | 34.36s |
| By task | ||
| Object Detection | 38.6% | 47.5% ±0.6, Mean of 3 runs, range 46.8 to 48.0 |
| Counting | 67.6% | 51.4% ±2.7, Mean of 3 runs, range 48.6 to 54.0 |
| Identification | 93.8% | 79.2% ±1.6, Mean of 3 runs, range 78.1 to 81.3 |
| OCR (low) | 63.9% | – |
| by category |
| |
| OCR (high) | 73.1% | – |
| by category |
| |
| Reasoning (low) | 64.9% | 52.8% ±1.3, Mean of 3 runs, range 51.7 to 54.3 |
| Reasoning (high) | 74.2% | – |
Gemini 3 Flash vs Gemma 4 31B: Overview
Gemini 3 Flash is a proprietary multimodal large language model developed by Google through Google DeepMind, designed to deliver fast, cost-efficient reasoning across real-time products and developer workflows. Released in December 2025, it is the Flash-tier variant of the Gemini 3 family, balancing low latency with reasoning quality approaching Pro models.
The model supports text, images, audio, and video, with an exceptionally large context window of roughly one million input tokens and outputs up to ~65k tokens. It emphasizes rapid responses for coding, summarization, analysis, and agentic tasks, and exposes configurable “thinking levels” via API to trade speed for deeper reasoning. Today, Gemini 3 Flash positions itself as a high-throughput, production-ready model, serving as the default in the Gemini app and Google Search’s AI Mode, optimized for scalable, interactive AI applications.
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 Flash performed better. It scores higher on 3 of the 4 vision tasks and averages 65.7% (#27 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 Counting benchmark at low effort, Gemini 3 Flash leads with 67.6% against 51.4%. 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 $0.0024. Actual costs depend on your image sizes, prompts, and output length.
Gemini 3 Flash is faster. Across Roboflow's Vision Evals it averaged 5.8s 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 object detection and image classification in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.