Florence-2 vs GPT-5.4

Compare Florence-2 and GPT-5.4 side-by-side. See how these vision models stack up in Image Captioning, OCR, and Object Detection.

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AzureFlorence-2
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OpenAIGPT-5.4
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Models in this comparison

OpenAI

Florence-2 vs GPT-5.4: Overview

Florence-2

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-5.4

GPT-5.4 is a proprietary multimodal large language model developed by OpenAI and released on March 5, 2026. It is designed for professional workloads such as advanced software development, research, and agentic automation. The model combines the general reasoning capabilities of the GPT-5 series with software engineering improvements derived from GPT-5.3-Codex. In the API and Codex environments it supports context windows of up to 1 million tokens, enabling long-context reasoning and large-scale code or document workflows.

Compared with GPT-5.2, GPT-5.4 reduces false individual claims by 33% and lowers overall response errors by 18%, improving factual reliability across complex tasks. It is also the first general-purpose OpenAI release with native computer-use capabilities, allowing agents to interact with desktops, browsers, and external applications to complete multi-step workflows. The model family includes three variants: GPT-5.4 (standard), GPT-5.4 Pro for higher-performance workloads, and GPT-5.4 Thinking, a reasoning-oriented version in ChatGPT that presents an upfront plan before generating its response. The API also introduces a Tool Search system that allows models to retrieve tool definitions dynamically, reducing token usage in tool-heavy integrations.

Florence-2 vs GPT-5.4 Comparison Table

PropertyFlorence-2GPT-5.4
OrganizationMicrosoftOpenAI
Categoryopenclosed
Modalitymultimodalmultimodal
Release DateJun 2025Mar 2026
Context Window1.1M
Parameters230M
LicenseMITProprietary
Pricing per 1M tokens
Input $/1M$2.50
Output $/1M$15.00
Vision Tasks
CaptioningDemoDemo
Object DetectionDemoDemo
OCRDemoDemo
ClassificationDemo
Instance Segmentation
Open Vocabulary Object Detection
Phrase Grounding
Region Proposal
Vision Language
Visual Question AnsweringDemo
Model Features
Foundation Vision
LLMs with Vision Capabilities
Multimodal Vision
Zero-shot Detection
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Overall Score
77.61%
Avg Response Time7.16s
Median input tokensincl. image tokens1.4K
Median output tokens108
Est. cost / taskon this benchmark$0.0052
Defect Detection
86.7%(13/15)
Document Understanding
88.9%(8/9)
Object Counting
40%(4/10)
Object Understanding
85.7%(12/14)
Spatial Understanding
78.9%(15/19)

Output tokens (incl. reasoning) and est. cost / task are measured on this benchmark from a single low-temperature run, and shown only for models whose run covered at least 90% of prompts. Methodology