Gemini 3 Flash vs Gemma 4 26B A4B
Compare Gemini 3 Flash and Gemma 4 26B A4B 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 26B A4B on Vision Evals
Gemini 3 Flash scores higher on 4 of the six Vision Evals tasks.
The widest gap is Counting, where Gemini 3 Flash leads 67.6% to 43.2%.
Overall, Gemini 3 Flash averages 74.9% (#15 of 53) against 63.6% (#42 of 53) for Gemma 4 26B A4B.
Gemma 4 26B A4B is cheaper ($0.0019 vs $0.0021 per sample), while Gemini 3 Flash is faster (4.1s vs 27.8s per sample).
Gemini 3 Flash vs Gemma 4 26B A4B Comparison Table
Evals updated September 5, 2026Pricing updated September 21, 2026
| Property | Gemini 3 Flash | Gemma 4 26B A4B |
|---|---|---|
| Organization | ||
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Dec 2025 | Apr 2026 |
| Context Window | 1.0M | 256K |
| Parameters | 25.2B | |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $0.500 | $0.090 |
| Output $/1M | $3.00 | $0.300 |
| 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 | 74.9% | 63.6% |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.0021 | $0.0019 |
| Avg speed / sample | 4.10s | 27.84s |
| By task | ||
| Object Detection | 38.6% | 44.2% ±0.7, Mean of 3 runs, range 43.5 to 44.8 |
| Counting | 67.6% | 43.2% ±2.0, Mean of 3 runs, range 41.9 to 46.0 |
| Identification | 93.8% | 81.3% ±3.1, Mean of 3 runs, range 78.1 to 84.4 |
| OCR | 87.6% | 88.7% ±1.3, Mean of 3 runs, range 87.6 to 90.2 |
| Data Extraction | 96.9% | 76.6% ±0.5, Mean of 3 runs, range 76.3 to 77.3 |
| Reasoning (low) | 64.9% | 47.7% ±2.0, Mean of 3 runs, range 45.0 to 49.0 |
| Reasoning (high) | 74.2% | – |
Gemini 3 Flash vs Gemma 4 26B A4B: 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 26B A4B is the Mixture-of-Experts variant in Google's Gemma 4 family, with 25.2B total parameters but only 3.8B active per token. Built from the same Gemini 3 research as the 31B dense sibling and released as open weights under the Apache 2.0 license, it supports a 256K token context window with text and image input and configurable thinking mode. The "A4B" in the name refers to its approximately 4B active parameters. The MoE design makes it significantly faster at inference than the dense 31B, running nearly as fast as a 4B-parameter model while delivering roughly 97% of the dense model's quality.
For vision tasks, the 26B A4B shares the same multimodal capabilities as the 31B image understanding with variable aspect ratios and resolutions, and structured bounding box output for UI element detection. The tradeoff versus the 31B dense model is a small quality reduction in exchange for much faster inference and lower hardware requirements, fitting in 18GB of VRAM at 4-bit quantization. It ranked #6 among open models on the Arena AI text leaderboard at launch.
Frequently Asked Questions
On Roboflow's Vision Evals, Gemini 3 Flash performed better. It scores higher on 4 of the six vision tasks and averages 74.9% (#15 of 53) against 63.6% (#42 of 53) for Gemma 4 26B A4B. 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 43.2%. This is the widest gap between the two models across the benchmark's tasks.
Gemma 4 26B A4B is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0019 per sample against $0.0021. Actual costs depend on your image sizes, prompts, and output length.
Gemini 3 Flash is faster. Across Roboflow's Vision Evals it averaged 4.1s per inference against 27.8s. 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 object detection and image classification in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.