Florence-2 vs Qwen3 VL 235B A22B Instruct
Compare Florence-2 and Qwen3 VL 235B A22B Instruct side-by-side. See how these vision models stack up in Image Captioning and OCR.
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Florence-2 vs Qwen3 VL 235B A22B Instruct Comparison Table
Evals updated July 10, 2026Pricing updated July 16, 2026
| Property | Florence-2 | Qwen3 VL 235B A22B Instruct |
|---|---|---|
| Organization | Microsoft | Qwen |
| Category | open | open |
| Modality | multimodal | multimodal |
| Release Date | Jun 2025 | Sep 2025 |
| Context Window | — | 256K |
| Parameters | 230M | 235B |
| License | MIT | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $0.210 | |
| Output $/1M | $1.90 | |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Object Detection | Demo | |
| OCR | Demo | 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 | 66.4% |
| Object Detection | – | 42.3% |
| Counting | – | 47.3% |
| Identification | – | 90.6% |
| OCR | – | 88.1% |
| Data Extraction | – | 86.6% |
| Reasoning | – | 43.5% |
| Avg cost / sample | – | $0.0007 |
| Avg speed / sample | – | 8.2s |
Florence-2 vs Qwen3 VL 235B A22B Instruct: 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.
Qwen3 VL 235B A22B Instruct is a flagship multimodal vision-language model developed by Qwen (Alibaba Cloud), designed for instruction-following tasks that combine advanced text generation with visual understanding. It serves as a high-end open-weight model for developers and researchers building multimodal AI systems that require strong reasoning, perception, and long-context capabilities.
The model supports interleaved text and image inputs, very long context windows (up to roughly 256K tokens), and efficient inference through a mixture-of-experts architecture with about 22B active parameters out of 235B total. In today’s landscape, it competes with top-tier proprietary vision-language models while offering the advantages of open weights and flexible deployment. Typical applications include multimodal assistants, document and image analysis, visual reasoning, and large-context instruction-based workflows.