Claude Opus 4.1 vs YOLO World
Compare Claude Opus 4.1 and YOLO World side-by-side. See how these vision models stack up in Object Detection.
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Claude Opus 4.1 vs YOLO World: Overview
Claude 4.1 Opus, released by Anthropic in August 2025, is the upgraded flagship of the Claude 4 family, building on Opus 4 with stronger reasoning and agentic capabilities. Like its predecessor, it is multimodal and optimized for text, code, and tool use, with support for large context windows suited to multi-file codebases, technical workflows, and long-horizon problem solving.
On benchmarks, Opus 4.1 improves coding performance, reaching ~74.5% on SWE-Bench Verified compared to Opus 4’s ~72.5%. It demonstrates more precise debugging, refactoring, and orchestration of agentic tasks while maintaining similar safety and alignment safeguards. It is best suited for enterprise-scale software development, research automation, and advanced reasoning workflows where reliability and depth of analysis are critical.
YOLO-World v2 Small (YOLO-World-S-v2) is the smallest variant of Tencent AI Lab’s YOLO-World v2 family, released around February 2024 under GPL-v3. With ~13 million parameters, it adopts a prompt-then-detect paradigm using offline vocabularies and is pretrained on large-scale datasets such as Objects365 and GoldG. The model processes image inputs at 640×640 or 1280×1280 resolutions and supports zero-shot open-vocabulary object detection, enabling recognition of novel categories from text prompts without retraining.
Evaluations show competitive results across benchmarks like LVIS and COCO, while maintaining real-time efficiency. On an NVIDIA V100, the small variant reaches ~74 FPS at standard resolutions. Together with larger YOLO-World v2 models, it provides a scalable framework for efficient, open-vocabulary detection across diverse deployment settings.
Claude Opus 4.1 vs YOLO World Comparison Table
| Property | Claude Opus 4.1 | YOLO World |
|---|---|---|
| Organization | Anthropic | Tencent AI Lab |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Aug 2025 | Feb 2024 |
| Context Window | 200K | 13.0M |
| Parameters | ||
| License | Proprietary | GPL v3 |
| Pricing per 1M tokens | ||
| Input $/1M | $15.00 | |
| Output $/1M | $75.00 | |
| Vision Tasks | ||
| Object Detection | Demo | Demo |
| Captioning | Demo | |
| Classification | Demo | |
| OCR | Demo | |
| Open Vocabulary Object Detection | ||
| Phrase Grounding | ||
| Vision Language | ||
| Visual Question Answering | Demo | |
| Model Features | ||
| Multimodal Vision | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Real-Time Vision | ||
| Zero-shot Detection | ||
Vision Evalspass/fail results · 67 prompts Score key:≥75%40–74%<40% | ||
| Overall Score | 59.7% | |
| Avg Response Time | 7.09s | |
| Median input tokensincl. image tokens | 2.0K | |
| Median output tokens | 140 | |
| Est. cost / taskon this benchmark | $0.040 | |
| Defect Detection | 73.3%(11/15) | |
| Document Understanding | 88.9%(8/9) | |
| Object Counting | 0%(0/10) | |
| Object Understanding | 64.3%(9/14) | |
| Spatial Understanding | 63.2%(12/19) | |
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