Florence-2 vs GPT-5
Compare Florence-2 and GPT-5 side-by-side. See how these vision models stack up in Image Captioning, OCR, and Object Detection.
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Florence-2 vs GPT-5 Comparison Table
Evals updated July 28, 2026Pricing updated July 28, 2026
| Property | Florence-2 | GPT-5 |
|---|---|---|
| Organization | Microsoft | OpenAI |
| Category | open | closed |
| Modality | multimodal | multimodal |
| Release Date | Jun 2025 | Aug 2025 |
| Context Window | — | — |
| 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 |
| Chart Question Answering | ||
| Classification | Demo | |
| Document Question Answering | ||
| Image Tagging | ||
| Instance Segmentation | ||
| Multi-Label Classification | ||
| Open Vocabulary Object Detection | ||
| Phrase Grounding | ||
| Region Proposal | ||
| Vision Language | ||
| Visual Question Answering | Demo | |
| Model Features | ||
| Foundation Vision | ||
| Multimodal Vision | ||
| LLMs with Vision Capabilities | ||
| Zero-shot Detection | ||
Florence-2 vs GPT-5: 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.
GPT-5, released by OpenAI in August 2025, is a multimodal large language model that advances beyond the GPT-4 family with a new “unified system” architecture. This design allows the model to dynamically choose between fast responses and extended reasoning depending on task complexity. It supports text, code, and images, alongside stronger tool use and agentic workflows, making it more adaptable for real-world problem solving. While its exact context window size is not disclosed, GPT-5 is optimized for long-horizon reasoning and multi-step tool chaining, indicating substantially expanded capacity over its predecessors.
The release introduced specialized variants: GPT-5 Pro, offering extended reasoning for complex workflows, and GPT-5 Codex, optimized for advanced coding tasks such as large-scale refactoring and code review. GPT-5 shows benchmark gains in coding, biomedical reasoning, multimodal analysis, and scientific tasks. Developers also gain new controls, such as verbosity and personalization parameters, for greater steerability. With these improvements, GPT-5 positions itself as OpenAI’s most capable and versatile model, suited for enterprise automation, research, healthcare, and sophisticated coding environments.
