Florence-2 vs LLaVA-1.5
We don't benchmark Florence-2 or LLaVA-1.5 on Vision Evals. Florence-2 is open-weight under MIT: free for commercial use. LLaVA-1.5 is open-weight under Custom.
Compare Florence-2 vs LLaVA-1.5 live
Run the same image across every model that supports a task and compare their outputs side-by-side.
These models don't share enough common tasks for a side-by-side demo. See the comparison table below for their capabilities.
Florence-2 vs LLaVA-1.5 Comparison Table
| Property | Florence-2 | LLaVA-1.5 |
|---|---|---|
| Organization | Microsoft | Microsoft |
| Category | open | open |
| Modality | multimodal | multimodal |
| Release Date | Jun 2025 | Oct 2023 |
| Context Window | — | — |
| Parameters | 230M | 7B, 13B |
| License | MIT | Custom |
| Commercial use | free for commercial use | See license terms |
| Vision Tasks | ||
| Captioning | Demo | Not listed |
| Instance Segmentation | Supported | Not listed |
| Object Detection | Demo | Not listed |
| OCR | Demo | Not listed |
| Open Vocabulary Object Detection | Supported | Not listed |
| Phrase Grounding | Supported | Not listed |
| Region Proposal | Supported | Not listed |
| Vision Language | Not listed | Supported |
| Visual Question Answering | Not listed | Supported |
| Model Features | ||
| Foundation Vision | Supported | Not listed |
| LLMs with Vision Capabilities | Not listed | Supported |
| Zero-shot Detection | Supported | Not listed |
Both support 1 more feature | ||
Florence-2 vs LLaVA-1.5: 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.
LLaVA-1.5 is an open-source large multimodal model released in October 2023 by researchers at the University of Wisconsin-Madison and Microsoft Research. It builds on the original LLaVA architecture by introducing targeted refinements: switching the vision encoder to CLIP-ViT-L at 336-pixel resolution, replacing the projection layer with a two-layer MLP, and adding academic-task-oriented visual question answering data with response formatting prompts during training. These modifications achieve state-of-the-art performance across 11 benchmarks at release, with training completing in approximately one day on a single 8-A100 node.
The model accepts an image paired with a text prompt and generates natural language responses, supporting visual question answering, image captioning, and open-ended visual conversation. LLaVA-1.5 is available in 7B and 13B parameter variants built on the Vicuna language model, and is distributed under the Llama 2 Community License due to its Llama-2-based foundation. The original LLaVA paper was presented as an oral at NeurIPS 2023. Subsequent releases in the series (LLaVA-NeXT (LLaVA-1.6), LLaVA-NeXT-Video, and LLaVA-OneVision) are separate models with their own release pages and build on this foundation with expanded OCR, video, and multi-image capabilities.
