Florence-2 vs Gemini 2.5 Pro
Compare Florence-2 and Gemini 2.5 Pro 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 2.5 Pro Comparison Table
Evals updated July 10, 2026Pricing updated July 16, 2026
| Property | Florence-2 | Gemini 2.5 Pro |
|---|---|---|
| Organization | Microsoft | |
| Category | open | closed |
| Modality | multimodal | multimodal |
| Release Date | Jun 2025 | Jun 2025 |
| Context Window | — | 1.0M |
| Parameters | 230M | |
| License | MIT | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $1.25 | |
| Output $/1M | $10.00 | |
| 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 | 67.9% |
| Object Detection | – | 26.4% |
| Counting | – | 52.7% |
| Identification | – | 93.8% |
| OCR | – | 88.8% |
| Data Extraction | – | 84.5% |
| Reasoning | – | 60.9% |
| Avg cost / sample | – | $0.0036 |
| Avg speed / sample | – | 4.7s |
Florence-2 vs Gemini 2.5 Pro: 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 2.5 Pro, released on June 17, 2025, is Google DeepMind’s most capable model in the Gemini 2.5 family, optimized for deep reasoning, coding, and complex multimodal tasks. It accepts text, images, audio, video, and PDFs as input and outputs text. The model supports 1 million input tokens with an output capacity of up to 65K tokens, enabling large-scale comprehension of datasets, codebases, and technical documents. Its training knowledge extends to January 2025.
Pro outperforms earlier Gemini 2.0 models across benchmarks, including agentic coding tasks where it achieved ~63.8% on SWE-Bench Verified. It supports structured outputs, function calling, code execution, search grounding, and URL context, making it well-suited for enterprise, STEM, and developer workflows. However, it does not currently support image or audio generation in its stable release, and its higher computational cost and latency make it less efficient than Flash or Flash-Lite. It is available via the Gemini API, Google AI Studio, and Vertex AI.