Florence-2 vs Qwen3.5 122B A10B
Compare Florence-2 and Qwen3.5 122B A10B side-by-side. See how these vision models stack up in Image Captioning and OCR.
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Florence-2 vs Qwen3.5 122B A10B Comparison Table
Evals updated July 10, 2026Pricing updated July 21, 2026
| Property | Florence-2 | Qwen3.5 122B A10B |
|---|---|---|
| Organization | Microsoft | Qwen |
| Category | open | open |
| Modality | multimodal | multimodal |
| Release Date | Jun 2025 | Feb 2026 |
| Context Window | — | 256K |
| Parameters | 230M | 122B |
| License | MIT | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $0.260 | |
| Output $/1M | $2.08 | |
| 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 | ||
Florence-2 vs Qwen3.5 122B A10B: 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.
Qwen3.5-122B-A10B is a high-capacity multimodal Mixture-of-Experts (MoE) model developed by Alibaba’s Qwen team as part of the Qwen3.5 model family. The architecture contains 122 billion total parameters while activating roughly 10 billion per token through sparse expert routing, allowing the model to balance large-scale reasoning ability with relatively efficient inference compared to dense models of similar size.
The model is designed to process both text and visual inputs within a unified multimodal framework, enabling tasks that require reasoning across images, documents, charts, and natural language. This makes it suitable for applications such as document understanding, diagram interpretation, and complex visual question answering.
Qwen3.5-122B-A10B supports a native context window of approximately 256,000 tokens, which can be extended further through techniques such as YaRN scaling to support very long-context workloads. Released under the Apache 2.0 license, it builds on earlier Qwen multimodal systems and provides developers with an open-weight model capable of handling demanding multimodal reasoning and analysis tasks.
