Claude Opus 4.6 vs YOLO World

Compare Claude Opus 4.6 and YOLO World side-by-side. See how these vision models stack up in Object Detection.

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AnthropicClaude Opus 4.6
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TencentYOLO World
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Models in this comparison

Tencent

Claude Opus 4.6 vs YOLO World: Overview

Claude Opus 4.6

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.

YOLO World

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 4.6 vs YOLO World Comparison Table

PropertyClaude Opus 4.6 YOLO World
OrganizationAnthropicTencent AI Lab
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateFeb 2026Feb 2024
Context Window1.0M13.0M
Parameters
LicenseProprietaryGPL v3
Pricing per 1M tokens
Input $/1M$5.00
Output $/1M$25.00
Vision Tasks
Object DetectionDemoDemo
CaptioningDemo
ClassificationDemo
OCRDemo
Open Vocabulary Object Detection
Phrase Grounding
Vision Language
Visual Question AnsweringDemo
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
64.18%
Avg Response Time23.35s
Median input tokensincl. image tokens2.2K
Median output tokens130
Est. cost / taskon this benchmark$0.014
Defect Detection
73.3%(11/15)
Document Understanding
77.8%(7/9)
Object Counting
20%(2/10)
Object Understanding
71.4%(10/14)
Spatial Understanding
68.4%(13/19)

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