Claude Opus 4.6 vs Florence-2
Compare Claude Opus 4.6 and Florence-2 side-by-side. See how these vision models stack up in OCR, Object Detection, and Image Captioning.
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Claude Opus 4.6 vs Florence-2 Comparison Table
Evals updated July 10, 2026Pricing updated July 21, 2026
| Property | Claude Opus 4.6 | Florence-2 |
|---|---|---|
| Organization | Anthropic | Microsoft |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Feb 2026 | Jun 2025 |
| Context Window | 1.0M | — |
| Parameters | 230M | |
| License | Proprietary | MIT |
| Pricing per 1M tokens | ||
| Input $/1M | $5.00 | |
| Output $/1M | $25.00 | |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Classification | 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 | ||
Claude Opus 4.6 vs Florence-2: Overview
Claude Opus 4.6 is the flagship large language model from Anthropic, released on 2026-02-05 for advanced reasoning, complex coding, and enterprise agent workflows. It supports text and image inputs via API, offers a 200K-token standard context window with a 1M-token beta option, and enables outputs up to 128K tokens, with adaptive reasoning and context compaction for sustained tasks.
As of 2026-02-17, Anthropic also released Claude Sonnet 4.6, extending the 1M-token context window to a broader tier. Opus remains positioned for maximum depth and benchmark performance, while Sonnet 4.6 brings long-context capability to more cost- and latency-sensitive production use cases.
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.
