Florence-2 vs Gemini 3 Flash
Compare Florence-2 and Gemini 3 Flash side-by-side. See how these vision models stack up in Image Captioning, OCR, and Object Detection.
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Florence-2 vs Gemini 3 Flash Comparison Table
Evals updated July 10, 2026Pricing updated July 21, 2026
| Property | Florence-2 | Gemini 3 Flash |
|---|---|---|
| Organization | Microsoft | |
| Category | open | closed |
| Modality | multimodal | multimodal |
| Release Date | Jun 2025 | Dec 2025 |
| Context Window | — | 1.0M |
| Parameters | 230M | |
| License | MIT | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $0.500 | |
| Output $/1M | $3.00 | |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Classification | Demo | |
| Instance Segmentation | ||
| Open Vocabulary Object Detection | ||
| Phrase Grounding | ||
| Region Proposal | ||
| Vision Language | ||
| Visual Question Answering | Demo | |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
| Zero-shot Detection | ||
Vision Evalsground-truth scores across 6 vision tasks | ||
| Overall | Not evaluated | 77.2% |
| Object Detection | – | 39.3% |
| Counting | – | 67.6% |
| Identification | – | 93.8% |
| OCR | – | 87.6% |
| Data Extraction | – | 96.9% |
| Reasoning | – | 78.3% |
| Avg cost / sample | – | $0.0017 |
| Avg speed / sample | – | 3.9s |
Florence-2 vs Gemini 3 Flash: Overview
Florence-2, introduced by Microsoft Research at CVPR 2024, is an open-source vision-language foundation model designed to unify diverse computer vision tasks within a single sequence-to-sequence framework. Unlike traditional models that specialize in specific tasks, Florence-2 accepts both images and text prompts and outputs text for tasks such as captioning, object detection, segmentation, OCR, and region-based grounding. It comes in two sizes—Florence-2-base (~230M parameters) and Florence-2-large (~770M parameters)—and is trained on FLD-5B, a large dataset of ~126M images with ~5.4B annotations.
The model demonstrates strong zero-shot and fine-tuned performance, often rivaling larger vision-language systems while remaining lightweight and efficient. Released under the MIT license, all weights are publicly available, making it accessible for fine-tuning and deployment in applications like VQA, content tagging, accessibility, and research. Florence-2’s compact design, versatility, and openness position it as a practical alternative to larger proprietary multimodal models.
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.
